Vignette | Citation | Need helps?

Introduction | Installation | Load MSA2dist | Sequence Format conversion | Frame aware Biostrings::DNAStringSet translation (cds2aa()) | Pairwise sequence comparison | Calculate pairwise AA distances (aastring2dist()) | Grantham's distance | Calculate pairwise DNA distances (dnastring2dist()) | ape::dist.dna models | IUPAC distance | Coding sequences | Calculating synonymous and nonsynonymous substitutions (dnastring2kaks()) | Using any model from KaKs_Calculator 2.0 | Using indices to calculate Ka/Ks | Codon comparison | Create codon matrix (dnastring2codonmat()) | Calculate average behavior of each codon (codonmat2xy()) | Plot average behavior of each codon | Dxy and FST | Dxy and FST from pre-calculated distance matrix (dist2dxy()) | Dxy and FST directly from Biostrings::DNAStringSet (dnastring2dxy()) | References | Session Info

Overiew of GESECA method | Analysis of time course data | Analysis of single-cell RNA-seq | Analysis of spatial transcriptomic data | Analysis of 10X visium spatial data | Analysis of 10X xenium spatial transcriptomics data | Session info

Introduction | Installation | Setup | Batch correction method prediction | Batch effects correction | Performance evaluation | sessionInfo()

Zarr version | Reading and Writing | Stores | Data Types | Codecs | Compression codecs | Other codecs | Optional fields or features

Introduction | Installation instructions | Multithreading | Yang data: lycophyte roots | Univariate tests | Tests for univariate aggregation or regularity: nearest neighbour distances | Bivariate tests | Tests for colocalization or antilocalization: interfeature nearest neighbour distances | Eng2019: Mouse brain cells | Vicinity to cell edge or centroid, and other within-cell localization patterns | Gradients | Predicting tumor types with probabilistic indices as predictor | Schematic workflow | Troubleshooting | Session info | Bibliography

Introduction | Statistical Methodology | 1. Hurdle Model | 2. Information Borrowing via Empirical Bayes | 3. Differential Expression Testing | Data Structure | Example Workflow | Interpreting Results | Multiple Testing Correction | Supported Input Format | Session Information

Introduction | Loading motifs from several sources | Sidebar: importing raw files directly | Inspecting cross-source redundancy | Deduplicating via graph clustering | Visualising the cluster structure | Trimming uninformative flanks | Adding the metadata back | Export as a single MEME file | Session info | References

Introduction | Motif comparisons | An overview of available comparison metrics | Comparison parameters | Comparison P-values | A faster alternative: compare_motifs_lite() | Speed comparison vs compare_motifs() and yamtk cmp | Merging motifs | A faster alternative: merge_motifs_lite() / merge_similar_lite() | Motif trees with ggtree | Using motif_tree() | Using compare_motifs() and ggtree() | Plotting motifs alongside trees | Motif P-values | The dynamic programming algorithm for calculating P-values and scores | The branch-and-bound algorithm for calculating P-values from scores | The random subsetting algorithm for calculating scores from P-values | Session info | References

Introduction | The universalmotif class and conversion utilities | The universalmotif class | Converting to and from another package's class | Importing and exporting motifs | Importing | Exporting | Modifying motifs and related functions | Converting motif type | Merging motifs | Motif reverse complement | Switching between DNA and RNA alphabets | Motif trimming | Rounding motifs | Motif creation | From a PCM/PPM/PWM/ICM matrix | From sequences or character strings | Generating random motifs | Motif visualisation | Motif logos | Stacked motif logos | Plot arbitrary text logos | Higher-order motifs | | Sequence | Tidy motif manipulation with the universalmotif_df data structure | Miscellaneous motif utilities | DNA/RNA/AA consensus functions | Filter through lists of motifs | Generate random motif matches | Motif shuffling | Scoring and match functions | Type conversion functions | Session info | References

Introduction | Basic sequence handling | Creating random sequences | Calculating sequence background | Clustering sequences by k-let composition | Shuffling | Shuffling sequences | Local shuffling | Composition-matched backgrounds with match_bkg() | Generating ground-truth positive sequences with implant_motifs() | Motif pair co-occurrence with motif_coocc() | Sequence scanning and enrichment | Choosing a logodds threshold | Logodds thresholds | P-values | Multiple testing-corrected P-values | Regular and higher order scanning | A faster alternative: scan_sequences_lite() | Speed comparison vs motifmatchr and yamtk | Visualizing motif hits across sequences | Enrichment analyses | A faster alternative: enrich_motifs_lite() | De novo motif discovery | Positional enrichment with motif_peaks() | Proximity enrichment with motif_proximity() | Fixed and variable-length gapped motifs | Detecting low complexity regions and sequence masking | Motif discovery with MEME | Miscellaneous string utilities | Session info | References

Introduction | Installation | Prerequisites | Loading data | Creating gene sets | Running the methods | Running the benchmark | Exploring the results | Benchmarking a single method at different choices of gene loss and noise | Session information

Using credentials to access S3 buckets | Creating an S3 client

Introduction | Limitations with Rarr | Example data | Quick start guide | Additional details | Working with Zarr metadata | Writing subsets of data | Creating an "empty" array | Updating a subset of an existing array | Appendix | Session info

feature and pheno data# | Subsetting the data | Exploratory analysis using boxplots | A note about ggplot2 | Other exploratory analysis | Normalisation | Filtering | Differential expression | Automating the DE analysis | Output as GRanges | Visualisation options | Creating a GEO submission file | Analysing data from GEO | Reading bead summary data into beadarray | Reading IDAT files | Citing beadarray | Asking for help on beadarray

Acknowledgments | DspikeIn | DspikeIn requirements | To build TreeSummarizedExperiment file please refer to | spike-in validation | Did spike-ins behave as expected across all samples? | Pre_processing | Spiked species and related parameters should be defined | Calculate the percentage of spiked species retrieval | spiked species retrieval is system-dependent | Spiked species and related parameters for ITS | Calculating Scaling Factors | Convert relative counts to absolute counts | Summary Stat | Conclusion | Abundance-based Core microbiome | Data transform & Normalization | Ridge plot | Differential abundance (Single Pairwise) | Differential abundance (Multilayer Pairwise) | Turnover (Presence/absence analysis) | Visualization | Microbial dynamics & NetWork comparision | Visualizes pairwise relationships between node-level metrics using a quadrant-based plot | Comparision of node-level metrics | To find neighbors of the target node | Compute degree metrics and visualize the network | Small-World Index Analysis | DspikeIn volume protocol

Introduction | Setup | Load Required Libraries and Data | Running STADyUM | Likelihood Ratio Test Calculations | Visualization | Pause-escape Rate Quantile Heatmap | Pause-escape Rate Scatter Plot | Pause-escape Rate and Pausing Dispersion Contour Plot | Pausing Dispersion Contour Plot | Summary | Session Information

Introduction | Setup | Load Required Libraries | Data Loading and TSS Selection | Load TSS Annotations | Select Single TSS per Gene | Building Read-Count Regions | Load Transcript Annotations | Define Pause and Gene Body Regions | Running STADyUM | Estimate Transcription Rates | Visualization | CD4 Visualizations | Pause Release Rate vs. Transcription Activity | Pause Release Rate vs. Elongation Parameters | Summary | Session Information

Introduction | Installation | Basic Usage | Simulate Polymerase | Estimate Transcription Rates from Simulated Data | Likelihood Ratio Tests for Simulated Data | Session Info

Introduction | Installation | Data | Example application of queeems | Tree length analyses | Natural selection analyses | Conclusion | Citation | Version Information | Session info | References

Introduction | Installation | Getting started | Exploring the data structure | Data preparation | Taxonomic data processing | Supported formats | Original taxa column | Special handling of uncultured and unclassified | Choosing a Taxonomic Level | Handling Data Without Taxonomic Strings | Data normalization | > Move to: Full-scale modeling workflow using real cohort data for reference | Runnable toy workflow setup | Setup the toy dataset | Model training and evaluation | External validation | Full screening workflow in one step | Get the the raw prediction scores for each sample | Full-scale modeling workflow using real cohort data for reference | Data filtering and splitting | Quality control | Model training and Evaluation | Prediction & Evaluation | Step 1: Predict on New Data | Step 2: Evaluate Model Performance | Streamlined Screening Workflow | Best practices and recommendations | Normalization method selection | Model selection | Validation strategies | Class imbalance | Troubleshooting | Common issues and solutions | Feature mismatch between datasets | Memory issues with large datasets | Session information | References

Within omics sample relationship verification | Create toy data | Running the allelesharing algorithm | Report mismatches and provide graphical summary | Across omics data type sample relationship verification | An example using real world methylation data from a SummarizedExperiment | SessionInfo | Reference

Introduction | Background on the ivyGlimpse app | Summary information on the underlying data | Additional details | Basic experimental design layout | Tumor-level details | Sub-block-level details | Details on RNA-seq samples | Key RNA-seq subsets | Subsets of design origin | Subsets based on structure | A simple differential expression study | Differential expression by molecular subtype | Classification of structural character | Next steps

Introduction | Quick start | Using low-level functions | Finding overlaps | Searching on text | Fetching all data | Flushing the cache | Customizing the database | Creating the custom database | Reading custom files | Contributing custom sets | Session information

Introduction | Motivation for Bioconductor Integration | Installation | Create SummarizedExperiment objects for ECODA | From SingleCellExperiment object | From Seurat object | From counts or frequency data frame | Visualization and quantification of sample separation | PCA | Quantifying group separation | Box and bar plots | Heatmap | Cell type correlation | Highly variable cell types | scECODA and pseudobulk | FAQ | Can ECODA be affected by batch effect? | Should I remove red blood cells, platelets and neutrophils? | How can I remove specific cell types? | References | Session Info

Introduction | Loading peaks from a BED file | Extracting peak sequences and building a matched background | De novo motif discovery | Deduplicating the discovered set | Comparing against JASPAR via MotifDb | Validating enrichment against a composition-matched background | Scanning all peaks and testing positional bias | Motif clustering with motif_coocc() | Summary table | Session info | References

Position count matrices | | Sequence | Position probability matrices | Position weight matrices | Information content matrices | References

Introduction | Installation | Quick start guide | Sample data examples | Sample input data (provided within the package) | Sample input data (provided online through a Zenodo repository) | Data preparation | Input data format | Input data generation | bedGraph binding / accessibility profiles | Peak GFF files | Using damidBind | Loading data | Loading data from GRanges objects | Gene locus assignment to peaks | A brief word on normalisation | Analysing differential binding | Analysing differential expression | Visualising data | Venn diagrams of differentially bound loci | Volcano plots | The default volcano plot | Cleaning up the gene names | Highlighting gene groups | Browsing differentially-bound regions | Gene Ontology (GO) enrichment plots | Accessor methods and further analysis | References | Session info

Introduction | Installation | Loading precomputed RNA-seq counts | Inferring sample groups | Working with GCT files | Session info

BiocCheck Summary | Using BiocCheck | When should BiocCheck be run | Installing BiocCheck | Interpreting BiocCheck output | Installation | Deprecated Package Checks | Remotes Usage Check | LazyData Usage Check | Version Checks | Package and File Size Check | biocViews Checks | Build System Compatibility Checks | DESCRIPTION checks | NAMESPACE checks | .Rbuildignore checks | BiocCheck output folder check | Check for inst/doc folder | Vignette Checks | Checking Install or Update Package Calls in R code | Coding Practices Checks | Function length checking | man page checking | NEWS checks | Unit Test Checks | Formatting checks | Duplication checks | bioc-devel Subscription Check | Support Site Registration Check | BiocCheckGitClone | Using BiocCheckGitClone | Installing BiocCheckGitClone | Interpreting BiocCheckGitClone output | Bad File Check | CITATION checks | Expanding BiocCheck | SessionInfo

Introduction | Package Structure | Installation | Usage

MeLSI Tutorial | Abstract | Introduction | Comparison with Existing Bioconductor Packages | Installation and Setup | Basic Usage | Step 1: Prepare Your Data | Step 2: Data Preprocessing | Step 3: Run MeLSI Analysis | Comparison with Standard Methods | Visualizing Results | Variable Importance Plot (VIP) | Principal Coordinates Analysis (PCoA) | Advanced Usage: Custom Parameters | Parallel Permutation Testing | Working with Bioconductor Packages | Integration with phyloseq | Summary | Key Takeaways | Next Steps | Session Info

Introduction | Installation | Anatomy of a GraphExperiment object | Building a GraphExperiment object | Accessing rowGraphs/colGraphs and rowData/colData (a.k.a. 'getters') | Modifying GraphExperiment objects (a.k.a. 'setters') | Subsetting GraphExperiment objects | Session information | References

Introduction | Install and load the package | Install and check IgBLAST | Install IgBLAST | Check IgBLAST | Install and select an IgBLAST-compatible germline database | Built-in germline databases | Install additional IgBLAST-compatible germline databases | Install an IgBLAST-compatible germline database from IMGT/V-QUEST | Install an IgBLAST-compatible germline database from user-supplied gene allele sequences | Select the germline database to use with igblastn() | Internal data and auxiliary data | What are the internal data and auxiliary data? | IgBLAST-provided internal/auxiliary data | igblastr-generated internal/auxiliary data | How is this data generated? | How to use the igblastr-generated internal/auxiliary data with igblastr? | Select a constant region database (optional) | Use igblastn() | Prepare the query sequences | Call igblastn() on the query sequences | Downstream analysis | Distribution of percent mutation across BCR sequences | Distribution of germline genes | Lengths and motifs of CDR3 sequences | Advanced usage | Passing additional arguments to igblastn() | Restrict the search to a subset of user-supplied gene alleles | A TCR analysis example | Prepare the TCR query sequences | Install a TCR germline database | Select a TCR constant region database (optional) | Call igblastn() on the TCR query sequences | Future developments and session information | Future developments | Session information

KEGGREST | Installation | Overview | Exploring KEGG Resources with keggList() | Get specific entries with keggGet() | Search by keywords with keggFind() | Convert identifiers with keggConv() | Link across databases with keggLink()

Installation | Purpose of MSstatsBioNet | Dataset | MSstats Convert from Upstream Dataset | MSstats Process and GroupComparison | MSstatsBioNet Analysis | ID Conversion | Subnetwork Query | Visualize Networks | Session info

Installation | Dataset | ID Conversion | Subnetwork Query | Network Visualization | Session info

Introduction | Installation | Demonstrating the usage of iSEEid | Setting up example data | Launching the app | The SampleIdentificationCenter panel | How to perform annotation within iSEEid | Customizing the initial state for efficient annotation | Session info | Bibliography

Accessing Genome-Scale Data | Non-model organism gene annotations | Roadmap Epigenomics Project | Ensembl GTF and FASTA files for TxDb gene models and sequence queries | liftOver to map between genome builds | sessionInfo

Introduction | Installation | Data preparation | Quick start | Retrieve bulk TF ChIP-seq binding sites | Link ATAC-seq peaks to target genes | Optimize the number of metacells (Optional) | Calculate Peak to Gene Links | Add TF motif binding to peaks | Generate regulons | Network pruning (highly recommended) | Add Weights | Annotate with TF motifs (Optional) | Annotate with log fold changes (Optional) | Calculate TF activity | Session Info

Introduction | Installation | Importing MS Data from MetaboLights | General information for a MetaboLights data set | Session information

Setup | Case study: authors & datasets | Challenge and solution | Areas of interest | Collaboration | Duplicate collection-author combinations | What is an 'author'? | Case study: using ontology to identify datasets | Session information

Installation and use | cxg() Provides a 'shiny' interface | Collections, datasets and files | Using dplyr to navigate data | facets() provides information on 'levels' present in specific columns | Filtering faceted columns | Publication and other external data | Visualizing data in cellxgene | File download and use | Next steps | API changes | Session info

Install | Quick start | In-depth articles | Getting help

The big picture | Step 1: Setup and validation | What happens | Key options and their impact | Quirks | Step 2: Reading and quality filtering | Step 3: Collapsing identical reads | Key options | Step 4: Assigning reads to experiments | Step 5: Aligning reads to amplicons | Step 6: HDR detection | Step 7: The AlignmentsExperimentSet container | Step 8: Extracting events | Step 9: Filtering artifacts | Step 10: Consensus from both strands | Step 11: Cut-site overlap detection | Step 12: Shifting to relative coordinates | Step 13: Normalization against controls | Step 14: Summarization | Step 15: Reporting | Importing external alignments | Plotting and visualization | Quick reference: parameter summary | Quick reference: pipeline default differences

Why transform through code, not by hand | The 12 required columns | The Amplicon column: case is everything | Strategy A: uppercase the guide plus a buffer (typical) | Strategy B: uppercase the whole interior (for controls or broad analysis) | Summary of buffer interaction | Direction: 0 or 1 | Primers: anchoring sequences, not necessarily PCR primers | What primers do in amplican | Rules for primers | Practical tip | The LLM workflow | Step 1: Export your column headers | Step 2: Prepare the prompt | Step 3: Review and run the generated code | Prompt template | Example: uppercase by guide with buffer | Example: uppercase whole interior (for controls) | Example: detect direction | Example: validate config | Donor extension (advanced) | Validation checklist | Putting it all together

Prerequisites | Quick test with subsampling | Running the full pipeline | Simple one-liner | With custom parameters | Parallel processing | Output folder structure | Reading and interpreting results | config_summary.csv | barcode_reads_filters.csv | Tweak: primer matching | Tweak: which reads to use | Tweak: normalization | When normalization helps | When to disable normalization | How to check if normalization is working correctly | Adjusting the frequency threshold | Tweak: cut buffer | Tweak: PRIMER_DIMER threshold | Tweak: consensus strictness | HDR tweaking | Two detection modes | Decision tree | donor_mismatch behaves differently in each mode | Interaction with cut_buffer | How to check HDR results | Adding a custom HDR percentage column | Iteration workflow | Name folders by parameters | Subsample first, then run full | Compare across runs | Always check RunParameters.txt | Quick reference: tweak decision tree

Introduction | Preparing tximeta input | Running tximeta | How does it work? | Packages used for caching metadata | Pre-computed digests | SummarizedExperiment output | Retrieve the transcript database | Add exons per transcript | Easy summarization to gene-level | Assign ranges by abundance | Add different identifiers | Differential gene expression analysis | Differential transcript expression and usage analysis | Additional metadata slots | Mixed reference transcripts | salmon mixed reference | Importing oarfish data | Errors connecting to a database | What if digest isn't known? | Linked transcriptomes | Clear linkedTxomes | Loading linkedTxome JSON files | Clear linkedTxomes again | alevin details | Other quantifiers | Acknowledgments | Session info | References

Introduction | Installation | Input data preparation | Merge paired-end sequencing reads to fragment-level | Merge methylation states of CpGs on two strands to fragment-level | Generate fragment-level information about methylation states | Generate the methylation pattern of markers | Fragments intersecting with marker regions | Cancer detection with CancerDetector() | Tissue deconvolution with cfDeconvolve() | Tissue deconvolution with cfSort() | Visualization with PlotFractionPie() | Reference | Session info

tl;dr | Mathematical background | Normalization by library size | Testing for (lack of) intermediacy | Calling doublet clusters | Discussion | Session information

Installation | Usage | Libraries | Ambient removal | Quality control | Data integration | Semi-automatic annotation with Celltypist | Cell composition | Reclustering of cell populations | Gene ontology analysis | Candidate gene visualisation | Session information

Introduction | Installation | Quick Start | Locate example files | Read variant information | Convert PGEN to GDS | Explore the GDS file | Access genotype data | Advanced Usage | Selecting a subset of variants | Importing a range with start/count | Parallel conversion | Integration with Bioconductor | Cleanup | Session Info

Introduction | Installation | Experimental Desgin | Feature extraction | Feature alignment | Save feature list | Using the Results | Downstream Analysis | Interoperability and Visualization | Normalization and Transformation | Advanced Usage | Conclusion

Installation | Load libraries needed | Introduction | Definitions | Important points for consideration | Data normalization | Background | Multiple hypothesis correction | Pathway databases | Identifiers | Example data | Examples | Comparison of one condition/group versus another condition/group. | Caveat | Description | Interpretation | Fisher's exact test | Caveats | Visualizing Fisher's exact results | The Kolmogorov–Smirnov test (KS) | Visualizing KS test results | KS alternative: the one-sample Z test | Enrichment in Correlation | Visualizing correlation enrichment results | Phosphoproteomics data analysis | Visualizing kinase substrate enrichment

Load libraries needed | Load our test proteomics dataset | Compare enrichment methods

Introduction | Installation | Biased Feature Identification | Perform Feature Selection using featureSelect() | Visualize SVG Selection Using svg_nSD() | Identify Biased Genes Using biasDetect() | Refine SVGs by Removing Batch-Affected Outliers | R session information

Purpose of MSstatsConvert | MSstats data format | Logging | Importing and cleaning data | Preprocessing | Fractions and balanced design | Metabolomics with MZMine | Annotation levels and the SIRIUS option

Introduction | Installation | Example dataset | KEGGemUP at a glance | Building KEGG pathway graphs | Rendering KEGG pathway graphs | Mapping values onto KEGG pathway graphs | Focusing on subsets of a pathway graph | Working further with KEGG graphs | Caching information with KEGGemUP | Related methods & packages | FAQs | Session info | References

Introduction | List of functions | Getting started | Split Networks | Data Types | Plotting a Split Network: | Plotting Explicit Networks | Summary | Session info | References

Introducción | Lista de funciones | Para empezar | Redes dividida o implicitas | Tipos de datos | Para graficar una Red Dividida | Para graficar una Red Explícita | Resumen | Session info | References

Introduction | What do you get with EMMA? | How does EMMA work? | Installation | Usage example of EMMA: the macrophage dataset | About the data | Getting a list of Differentially Expressed Genes | Performing Functional Enrichment Analysis (FEA) | Entering the EMMA framework | EMMA_run(): Capturing the recorded information | EMMA_show(): Summarizing the recorded information | EMMA_get_record(): Retrieving the recorded information | EMMA_explain(): Summarizing recorded information into text | EMMA with custom/wrapper functions | EMMA_add_custom_metadata(): Adding extra information | EMMA_freeze(): Recording the Analysis Environment | EMMA with iterative workflows | Supported methods and future extensions | Session info | References

tl;dr | Algorithm overview | Size factor handling | Normalization size factors | RNA content size factors | Interactions between them | Doublet score calculations | Session information

Introduction | Required knowledge | Probe-exclusion reference files | Functions | preprocessingMinfiEwasWater() | svaEnmix() | preprocessingPheno() | methylationGLM() | methylationLME() | dnamReport() | Summary | Basics | Asking for help

Introduction | Citation | Required knowledge | Probe-exclusion reference files | Installation | Step 1: preprocessingMinfiEwasWater | Create example input files | Step 2: svaEnmix | Step 3: preprocessingPheno | Summary | Basics | Asking for help | References

Introduction | Citation | Required knowledge | Probe-exclusion reference files | Installation | Extract the example Makefile | Write preprocessingMinfiEwasWater outputs to disk | Create example input files | Write SVA outputs to disk | Reuse the temporary files written in Step 1 | Write preprocessingPheno outputs to disk | Write GLM model outputs to disk | Write LME model outputs to disk | Generate the report from the example outputs | GLM model structure and PRS terms | Longitudinal mixed-effects structure | Makefile use | Report generation | Full exported Makefile | Makefile section guide | User configuration | Models selection and parallel run | Per-model overrides | Directories | Global parameters | Step 1 to Step 5 shared parameters | Step 4 and Step 5 shared modeling parameters | Step 1 parameters | Step 2 parameters | Step 3 parameters | Step 4 parameters | Step 5 parameters | Argument normalisation | Include pipeline from dnaEPICO | Summary | Basics | Asking for help | References

Introduction | Installation | General usage | Creating Spectra objects | Accessing spectrum data | Filtering, aggregating and merging spectra data | Filter spectra data | Filter or aggregate mass peak data | Merging, aggregating and splitting | Examples and use cases for filter operations | Data manipulations | Visualizing Spectra | Aggregating spectra data | Comparing spectra | Exporting spectra | Changing backends | Backends | Handling very large data sets | Serializing (saving), moving and loading serialized Spectra objects | Session information | References

Introduction | The problem | What scBatchQC does | How scBatchQC compares to other QC packages | Installation | Quick start: simulate a two-batch dataset | Step 1: Batch-aware QC metrics | Understanding the key parameters | Step 2: Visualise QC distributions | Step 3: Estimate doublet rates | Step 4: Explore thresholds interactively | Step 5: Filter and continue | Storing results: BQCResult container | Edge cases | Recommended full workflow | Session information

Introduction | About CGRphylo2 | Why CGRphylo? | Computational Efficiency | Citation | Installation | Bioconductor Installation (recommended) | Development Version | Quick Start Example | Load Required Packages | Prepare Example Sequences | Sequence Quality Control | Calculate CGR Frequency Matrices | CGR Visualization | Calculate Distance Matrix | Distance Matrix Heatmap | Build Phylogenetic Tree | Export Results | Detailed Workflow | 1. Bioconductor Integration | 2. Data Preparation | 3. Distance Calculation Methods | 4. Choosing K-mer Size | 5. Parallel Processing | 6. Memory Optimisation for Very Large Datasets | Understanding CGR | The CGR Algorithm | K-mer Frequencies | Distance Metrics | Performance Benchmarks | Troubleshooting | Out of Memory | Slow Performance | Tree Topology Differs from MSA | Session Information | References

Introduction | Installation | Core pipeline | (Optional) Decomposing gene sets into coexpressed subsets | Scoring | Training ML models | Recommendations | Using M-scores to extract disease-relevant information | Build the reference data object | Calculate the reference M-scores | Identify key gene sets | Calculate the M-scores without healthy controls | Data licenses | Session info | References

Introduction | Workflow | Setup | Before you run PostChicago: | Run CHiCAGO | Read in the datasets | Basic statistics | Visualisation | Lineplots | Annotation of Line plots with intervals: | Creating Genome Browser tracks: | Data integration | Create interactions table | Visualize significant interactions using Lineplots | Quantification | Quantification within intervals of interest | Making oneGeneOnePeak tables | Matrices surrounding intervals of interest | Plotting aggregate profiles of interactions | Quantify reads in epigenetic peak interactions directly | Data integration to interaction peaks | Direct quantification within interaction peaks | Session Info | References

Load packages | Introduction | What is DS analysis? | Starting point | Getting started | Data description | Loading the data | Preprocessing | Data preparation | Data overview | Cluster-sample sizes | Dimension reduction | Differential State (DS) analysis | Aggregation of single-cell to pseudobulk data | Pseudobulk-level MDS plot | Sample-level analysis: Pseudobulk methods | Cell-level analysis: Mixed models | Handling results | Results filtering & overview | Calculating expression frequencies | Formatting results | Visualizing results | Between-cluster concordance | DR colored by expression | Cell-level viz.: Violin plots | Sample-level viz.: Pseudobulk heatmaps | Session info | References

Load packages | Data description | Simulation framework | prepSim: Preparing data for simulation | simData: Simulating complex designs | p_dd: Simulating differential distributions | rel_lfc: Simulating cluster-specific state changes | normalize & run dimension reduction | plotting | arrangement | Simulation a hierarchical cluster structure | p_type: Introducing type features | extract gene metadata & number of clusters | filter for type genes with high expression mean | sample 100 cells per cluster for plotting | filter for type & shared genes with high expression mean | Method benchmarking | Session info | References

ClustSIGNAL | Motivation | Overview | Single sample analysis with ClustSIGNAL | Running ClustSIGNAL on one sample | Visualising ClustSIGNAL clusters | Assessing clustering accuracy | Entropy spread and distribution | Multisample analysis with ClustSIGNAL | ClustSIGNAL run | Clustering metrics | Visualizing ClustSIGNAL clusters | Visualising entropy spread and distribution | ClustSIGNAL step-by-step run | Step 1: Initial clustering and subclustering | Step 2: Neighbourhood detection | Step 3: Entropy measure | Step 4: Adaptive smoothing | Step 5: Final clustering

Introduction | computeDoubletDensity | recoverDoublets | scDblFinder | directDblClassification | findDoubletClusters | Installation | Which method to choose? | Session information

Purpose | Introduction | General Workflow | (A) gene-miRNA interactions | (B) ceRNA interactions | (C) Null-model-based p-value computation | (D) ceRNA interaction network | Network Analysis | Parallelization and Shared Memory | Computing covariance matrices for the null model | Wider Application | SPONGEdb | SPONGEdb is available at: | spongEffects | Usage: | Citation | SPONGE

Introduction | Installation | Bioconductor install | GitHub install | Input Data | Transductomics data | Pileup files | TrIdentClassifier() | Function components | Contig filtering | Changing pileup windowSize | Pattern-matching | Patterns | Sloping: | Prophage-like: | noPattern: | Highly active/abundant and heterogenously integrated/present Prophage-like elements | NoPattern classifications with high VLP-fraction:whole-community read coverage ratios | Usage | Arguments/Parameters | Output | plotTrIdentResults() | Re-building pattern-matches | Plotting read coverage and associated pattern-matches | specializedTransductionID() | Zoom-in on Prophage-like elements | Identify borders of Prophage-like elements | Search for specialized transduction outside Prophage-like borders | Supplemental information | Usage Note | Acknowledgments | Funding | Session Information

Introduction | Installation | Why pyramidal images? | Usage | EBImage | magick images | OME.TIFF (Bio-Formats) images | Use cases | Visualizing H&E images | Interactive Shiny Applications | Session info

Introduction | Motivation | Quick start | Installation | Load library | Initial the database | Start shiny app | Create a new data | by administrator | via R session | Add downloadable file | Distribute to a shiny server | SessionInfo | References

General R code | General Shiny code | General package code | Git best practices | SessionInfo

Overview | Use Cases | Style guide | CI/CD | Package installation | SessionInfo

Overview | Use cases | Data manipulation | Data extraction | Managing gDR identifiers | Validating identifiers | Prettifying identifiers | Data validation | Prettifying | Colnames of data.table(s) | Assay names | SessionInfo

Introduction | General overview of the data model | Supported Experiments: | SummarizedExperiment objects: | MultiAssayExperiment object | SummarizedExperiment object | Assays | rowData | colData | metadata | Session info

Overview | Introduction | Data model | Drug processing | Required columns | gDR pipeline | Use Cases | Data preprocessing | Running gDR pipeline | Data extraction | SessionInfo

Introduction | Loading the gDR-generated MAE object | Converting the MAE object into a PharmacoSet object | TreatmentResponseExperiment Object | Row and Column Names | data.table Subsetting | assays | References

Overview | Loading a PharmacoSet (PSet) | Converting PharmacoSet to data.table for gDR pipeline | Subsetting to extract relevant information | Running drug response pipeline with data | SessionInfo

Overview | Use Cases | Test Data | Load data | PRISM | Processing LEVEL5 PRISM Data | Processing LEVEL6 PRISM Data | Package installation | SessionInfo

Overview | Packaged example data | Quick Start | Quick-Start Plots | Detailed Look | Why bigcounts? | Plotting Subset | Interpreting The Plots | Notes | Session information

AnVILPublish | Package installation | Package load | Requirements | Best practices | The gcloud SDK and GCPtools | Quarto software | Creating or updating workspaces | From package source | From collections of Rmd files | Updating notebooks or workspace permissions | Updating workspace notebooks from vignettes | Adding user access credentials to share the notebook | Vignette and .Rmd best practices | Orientation | Additional notes on .Rmd conversion | Session info

Introduction | Installation | Quick start | Input data | simPIC simulation | simPICcount class | Getting and setting parameters | Estimating Parameters | Simulating counts | Comparing Simulations and Real Data | Simulating Multiple Cell Types - equal proportions | Simulating Multiple Cell Types - variable proportions | Simulating Batch effects | Citing simPIC | Session information

Introduction | Installation | Importing Hi-C data | Tabular files | Cooler files | Juicer files | HiC-Pro files | Running the HiCDOC pipeline | Filtering data | Normalizing biases | Technical biases | Biological biases | Distance effect | Predicting compartments and differences | Visualizing data and results | Sanity checks | PCA checks | Compartment assignemnt | Warnings | Session info | References

Introduction | The PipelineDefinition | Example run | Exploring the metrics | Doublet detection and cell filtering | Evaluation based on the reduced space | Subpopulation silhouette | Variance in the PCs explained by the subpopulations | Correlation with covariates | Clustering | Metrics | Plotting | Computing time | Extension and reuse | Datasets | scRNAseq benchmark datasets used in the paper | Using new datasets

Public S3 Buckets | Private S3 Buckets | Session Info

Introduction | Query interface | Installation | Load the package | Load additional packages | Load and explore the metadata | Load the metadata | Explore the tissue | Quality control | Download single-cell RNA sequencing counts | Query raw counts | Query counts scaled per million | Query SCT normalised counts | Query pseudobulk | Download cell communication metadata | Extract only a subset of genes | Extract the counts as a Seurat object | Save your SingleCellExperiment | Saving as RDS (fast saving, slow reading) | Saving as HDF5 (slow saving, fast reading) | Saving as H5AD (slow saving, fast reading) | Visualise gene transcription | Integrate cloud and local metadata | Cell metadata | RNA abundance | Other representations | Session Info

Overview | Choose cells through metadata filters | Retrieve expression by representation | Single-cell counts | Counts per million | Pseudobulk | Targeted gene queries | Seurat | Portable output examples | Interpretation notes

Why this page exists | Data-processing context | Metadata dictionary | Practical exploration

Introduction | Load gene sets | Usage and benchmark with RNA-seq data | Usage and benchmark with proteomics data | Session information | References

Introduction | Import data | Quality control and pre-processing | Annotate cell types using GSVA | Read gene sets in GMT format | Add gene identifier type metadata | Build parameter object | Calculate GSVA scores | Using GSVA scores to assign cell types | Benchmarking | Session information | References

Overview | Key functions | Key conceptions | Background | Loading data | Selecting cells | Calculating mutation p-value | Filter mutations | Visualization | Binary heatmap | P value heatmap | AF heatmap | Exporting mutation data | Show the p value, af plot versus cell types | Session Info

Installation | File Management | List Azure Blob Storage Container Files | Uploading a file | Deleting a file | Downloading from the ABS | Folder-wise upload to ABS | Folder-wise download from ABS | The DATA tab | mtcars example | Uploading data | Downloading data | Delete a row in the table | Delete entire table | Bug Reports | Session information

Installation | Additional Setup | Use in the AnVIL cloud | Local use | Graphical interfaces | Working with Google cloud-based resources | Using gcloud_*() for account management | Using gsutil_*() for file and bucket management | Using av*() to work with AnVIL tables and data | Tables, reference data, and persistent files | Using avtable*() for accessing tables | Using avdata() for accessing Workspace Data | Using avstorage() and workspace files | Using avnotebooks*() for notebook management | Using avworkflows_*() for workflows | Using avworkspace_*() for workspaces | Session Info

Overview | Setting up a package to use a Hub | New Hub package | Notify Bioconductor team member | Building the package | inst/extdata/ | inst/scripts/ | vignettes/ | R/ | man/ | DESCRIPTION / NAMESPACE | Data objects | Confirm Valid Metadata | Package review | Additional resources to existing Hub package | Converting a non AnnotationHub annotation package or non ExperimentHub | Bug fixes | Update the resource | Update the metadata | Remove resources | Versioning | Visibility | Storage of Data Files | Hosting Data on a Publicly Accessible Site | Uploading Data to Microsoft Azure Genomic Data Lake | R Interface with R package | Command Line via AWS S3 CLI | Utilizing the Bioconductor Docker container | Validating | Example metadata.csv file and more information

Introduction | Motivation | High-level workflow | Installation | Quick Start | What is Entropy and Tsallis Entropy? | Historical Foundation and Intellectual Progression | Why This Matters for RNA-seq: The Isoform Complexity Problem | Mathematical Foundation and Interpretation | Tsallis Entropy: Definition and Intuition | The q Parameter: A Sensitivity Dial for Distribution Scales | Biological Interpretation: Richness and Evenness | Why Multi-Scale Entropy Matters: Biological Contexts | Beyond Classical Abundance Measures | Mechanistic Evidence: Disease and Evolution | TSENAT Main Workflow | Load data and metadata | Data preprocessing and filtering | Compute Tsallis Entropy | Quality Control: Sample Influence Assessment | Scale-Adaptive Interaction Tests | Transcript Switching Across Diversity Scales | Two-Stage Analysis Approach | Key Interpretation Questions | Delta Influence Across Diversity Scales | Effect Size Analysis | Interpretation of Pattern Types: | Appendices | References | Session Information

Introduction | Document Structure | Background: Transcript Diversity Concepts | Dataset and Methods | Benchmarking | Importing example data | Data filtering and preprocessing | Transcript diversity calculation | Differential analysis | Benchmark Results Summary | Shannon Entropy (SplicingFactory Laplace vs TSENAT Tsallis q=1) | Top 10 Significant Genes - SplicingFactory (Laplace) | Top 10 Significant Genes - TSENAT (Tsallis q=1) | Simpson Index (SplicingFactory vs TSENAT Tsallis q=2) | Top 10 Significant Genes - SplicingFactory (Simpson) | Top 10 Significant Genes - TSENAT (Tsallis q=2) | Analysis: Simpson Index and Tsallis Entropy at q=2 | Conclusion: Equivalence Validation Summary | Session Information

Introduction | Two Complementary Validation Approaches | Why Two Methods for One Question? | Setup | Computing Tsallis Entropy with Pseudocount Regularization | Rank-Based Approach (SRH) | Assumption Validation | SRH Test: Testing q x Condition Interactions | Generalized Additive Mixed Model Approach (GAMM) | Method Concordance Analysis | Statistical Power vs. Robustness: Understanding Method Discordance | Visualization: Method Comparison | Session Information

Introduction | Motivation | Panels | Compositional Analysis | Ordination Analysis | Structural Analysis | Other Panels | Tutorial | Installation | Example | Resources | Open datasets | Other tutorials | Citation | Acknowledgements | Help | Reproducibility | References

Introduction | Compositional Analysis | Abundance plot | Abundance density plot | Prevalence plot | Feature assay plot | Complex heatmap plot | Ordination Analysis | RDA plot | Scree plot | Loading plot | Reduced dimension plot | Structural Analysis | Row/Column tree plots | Row/Column graph plots | Other panels | Row/Column tile plots | Mediation plot | Row/Column data tables | Row/Column data plots | Reproducibility | References

Introduction | Installation | Loading Package | Integration with the scRepertoire Ecosystem | Quick Start | Loading the Example Data | Input Data Format | Working with Single-Cell Data | The runGLIPH() Function | Key Arguments | Method Presets | Running GLIPH2 (Default) | Running GLIPH1 | Understanding the Output | Cluster Properties | Cluster Membership | Motif Enrichment | Network Edges | Customizing the Analysis | Using method = "custom" | Adjusting Significance Thresholds | Choosing a Reference Database | Using a Custom Reference Database | Motif Discovery with findMotifs() | Example | Cluster Scoring with clusterScoring() | Scoring Components | De Novo TCR Generation with deNovoTCRs() | Network Visualization with plotNetwork() | Loading Saved Results with loadGLIPH() | Saving Results to Disk | Performance | Accelerated Computation with immApex | Tips and Best Practices | Session Info

Introduction | Installation | Example | 1. Customized contrast matrix | 2. Process data | 3. Results | Visualization | Session Info

Introduction | Key Features | Comparison to Existing Methods | Installation | Basic Usage | Load Required Packages | 1. Load Example Data | (Demo only) Create balanced subsets for a fast vignette build | 2. Create CSFNMF Object | 3. Run CellMentor with Hyperparameter Optimization | 4. Project Data | 5. Integration with SingleCellExperiment | Running CellMentor on Your Own Data | Required Inputs | Workflow | Session Information | References

What is it? | How to get help | Further reading

Introduction | Processing overview | Outline of process | How To Use motifbreakR: A Practical Example | Step 1 | Read in Single Nucleotide Variants | SNPs from rsID: | SNPs from BED formatted file: | Step 2 | Find Broken Motifs | Parallel Evaluation (an aside) | Step 3 | Visualize | Methods | Sum of Log Probabilities | Weighted Sum | Calculating Scoring Vector $p$ | Calculating $\omega$ For Weighted Sum | Calculating $\omega$ For Relative Entropy | Calculate P-values for PWM match | Session Info

Introduction | Installation | Data Structure | Required Columns | Important: The offset Column | Example Data | Exploratory Data Analysis | Raw Cell Velocities | Mean Velocity per Well | Model Fitting | Control Parameters | Interpreting Results | Overall Treatment Effects ($\delta_t$) | Visualizing Effects | From Log-Fold-Change to Fold-Change | Pairwise Comparisons | Comparison Matrix | Visualize $\rho$ | Visualize $\pi$ | Visualize $\rho$ vs. $\pi$ with a volcano plot | Violin Plots for Specific Comparisons | Model Diagnostics and Calibration | Posterior Predictive Checks (PPC) | Cell-Level Check | Well-Level Check | Leave-One-Out (LOO) diagnostics | MCMC diagnostics | Check $\hat{R}$ and ESS | Check for divergent transitions and other issues | Variance Components | Advanced: Dose-Response Profiles | Session Info

SNP Effect Matrices | Scoring Binding | Prepare Inputs | Scoring | Enrichment | Test for Enrichment | Visualization | Enrichment in Promoters | Scoring Variants | Extras | Scoring Method | Sequence preparation | Loading a custom set of SEMs

Introduction | Preparation | Sketch cells | Principal component analysis (PCA) | t-SNE | UMAP | Clustering | Session info

Introduction | Installation | Preparation | Subsampling | Diagnostic plots | Session info | References

Introduction | Related methods | Quick start | Input data | Output format | Jones et al mouse long read dataset | Loading example data | Finding splicing events | Preprocessing input data | Finding individual events | Finding all events | Upstream methods | Obtaining exon ranges | Using prepare_exons() | Using prepare_exons_by_partition() | Manual construction | Session info

Workflow | How to cite epigraHMM | How to get help for epigraHMM | Quick start | Data input | Count matrix input | Alignment file input | About gap and blacklisted regions | Data normalization | Other types of normalization (e.g. GC-content and control assays) | Peak calling | Multi-sample single-condition analysis | Multi-sample, multiple-condition analysis | Calling peaks | Data visualization | Plotting peak tracks | Heatmap with posterior probabilities of differential enrichment | Miscellaneous | Posterior probabilities and associated differential enrichment | Selecting the optimal number of differential combinatorial patterns of enrichment | Theory | Model overview | Session info | References

PRELIMINARIES. | Theoretical Framework of Reference. | goSorensen Installation. | Data Used in this Vignette. | Before Using goSorensen. | MATRIX OF GO TERMS ENRICHMENT. | For One List (With or Without GO Level Restriction). | For Two or More Lists (With or Without GO Level Restriction). | NOTE: | ENRICHMENT CONTINGENCY TABLES | For a Specific Ontology (With or Without GO Level Restriction). | Contingency Tables for Two Lists. | Contingency Tables for Two or More Lists. | For More than One Ontology and GO Configurations (Levels or No Level Restriction). | EQUIVALENCE TESTS. | Equivalence Test for Two Lists. | Equivalence Test for Two or More Lists. | For Multiple Ontologies and GO Configurations. | References

PRELIMINARIES. | Theoretical Framework of Reference | Data Used in this Vignette. | Obtaining the Irrelevance-threshold Matrix of Dissimilarities. | For a Specific Ontology, with or without GO level restriction. | NOTE: | For More than One Ontology, with or without GO level restriction. | Representing a Dissimilarity Matrix in Statistical Graphs. | Dendrogram. | MDS-Biplot. | Characterizing MDS-Biplot Dimensions. | Step 1. Split the biplot axis: | Step 2. Obtain the matrix of GO terms enrichment: | Step 3. Compute means and variances: | Step 4. Establish a statistic: | Step 5. Select the GO terms with the highest statistic: | References

Introduction | Support | Contents | Requirements | Installation | Install using GitHub and devtools | Running MaAsLin 3 | Input data | Output files | Diagnostics | Run a demo | In R | Replotting | Contrast testing | Command line | Options | Required parameters | Model formula | Feature specific covariates | Analysis options | Compositionality corrections | Absolute abundance | Compositionality corrections continued | Median comparisons | Plotting parameters | Technical/miscellaneous parameters | Tool comparison | Troubleshooting

Introduction | Dummy Imputation with mice | Data Format | Creating an imExposomeSet | Accessing to Exposome Data | Exposures Behaviour | Extracting an ExposomeSet from an imExposomeSet | Exposome-Wide Association Studies (ExWAS) | Extract the exposures over the threshold of effective tests | Session info

Introduction | Installation | Pipeline | Data Format | Three table format | Single table format | Analysis | Loading Exposome Data | From TXT files | From data.frame | Accessing to Exposome Data | Exposome Pre-process | Missing Data in Exposures and Phenotypes | Exposures Normality | Exposures Imputation | Exposures Characterization (i.e. data visualization) | Exposures PCA | Exposures Correlation | Individuals Clustering | Exposure associations | Understanding Principal Component Analysis | Exposure/Enviroment/Exposome Wide Association Studies (ExWAS) | Sensitivity analysis: stratified models | Inverse ExWAS | Variable selection ExWAS | Session info

Introduction | Input data | Running an analysis | Standard application | Saving plot to file | Plot expression stratified by other variables | Intuition about the backend | Interpretation | Should a variable be modeled as fixed or random effect? | Which variables should be included? | Assess correlation between all pairs of variables | Advanced analysis | Extracting additional information from model fits | Removing batch effects before fitting model | Variation within multiple subsets of the data | Detecting problems caused by collinearity of variables | Including weights computed separately | Including interaction terms | Application to expression data | Gene-level counts | limma::voom() | DESeq2 | Isoform quantification | tximport | Compare with other methods | Tissue is major source of variation | Individual is major source of variation | Statistical details | Implementation in R | Interpretation of percent variance explained | Variation with multiple subsets of the data | Variance partitioning and differential expression | Modelling error in gene expression measurements | Session Info | References

Introduction | Overview | Example | Session information

Introduction | Partially Generative Simulation | Experimental Design | Model Parameters | Plate Effects ($\alpha_p$) | Control Treatment (Offset) | Cell Velocity Distribution ($\kappa_w$) | Generating the Data | Inspecting the Simulated Data | Visualizing Simulated Data | Analyzing Simulated Data | Validation: Truth vs. Inference | Treatment Effects ($\delta_t$) | Variability Parameters | Fully Generative Simulation | Model Specification | Generate Data | Comparing Simulation Modes | Power Analysis | Simulation Loop | Interpreting Results | Session Info

Introduction | Importing spectral data | TopSpin-processed spectra | Raw FID data | Visual inspection | Preprocessing | Pipeline-style usage | Stepwise execution | Provenance | Modelling | Principal Components Analysis (PCA) | Partial-Least Squares (PLS) | Orthogonal-Partial-Least Squares (OPLS) | Citation | Session Info

Introduction | Installation | Load the package into R session | Load data | Top table | Model | Visualizations | Volcano plot | Highlight top genes or genes of interest | Additional fdr threshold | Interactive plot | Log-ratio plot | Color feature labels | Facet by variable(s) | Mixed input | Sort features | Based on significance | Display text | Significance star | Log fold change | Heatmap | Nested coefficient labels | Upset plot | Down-regulated genes | Return overlapping sets | Scatter plot | Highlight genes of interest and top genes | Interactive plot with feature annotation | Add correlation | Waterfall plot | Multiple coefficients | MA plot | Label top genes and genes of interest | Significant genes barplot | Session information

BiocBaseUtils | Installation | Load Package | Assertions | Logical | Character | Numeric | Slot replacement | show method | Contributing | Session Info

Introduction | Installation | Prerequisites | Loading and preparing data | Extracting the expression matrix | Extracting and altering metadata/coldata columns | Extracting and altering the metadata/coldata or its column names | Creating frequency tables | Visualization | Session information

Introduction | Motivation | Design | Image Format: SVG | Data Repositories | Tutorial Overview | Getting Started | Installation | Packages and Documentation | Spatial Heatmaps | Quick Start | aSVG Image | Numeric Data | Plot SHM | Human Brain | Gene Expression Data | aSVG Image | Experimental Design | Preprocess Assay Data | SHM: Multiple Genes | SHM: Other Selected Features | SHM: Customization | Mouse Organs | SHM: Transparency | Arabidopsis Shoot | SHM: Microarray | Superimposing raster and vector graphics | SHMs of Multiple Variables | SHM: Multiple Variables | Extended functionalities | Spatial Enrichment | Hierarchical Clustering | Network Graphs | Module Identification | Module Visualization | Shiny App | Local System | Server Deployment | Custom Shiny App | Database Backend | Supplementary Section | Numeric Data | Object Types | vector | data.frame | SummarizedExperiment | Advanced Functionalities | SHM: Re-matching | SHM: Time Series | Version Informaion | Funding | References

Introduction | Overview | Data Structures | Getting Started | Installation | Packages and Documentation | Quick Start | Co-clustering Labels | Pre-processing | Co-clustering | Co-visualization | Spatial Single-Cell Data | Shiny App | Supplementary Section | Single-cell Clustering Labels | Co-clustering Labels: Other Coloring | Cell-by-Group | Feature-by-Group | Fixed-by-Group | Tailoring Co-clustering Results | Command-line Method | Graphical Method | Version Informaion | Funding | References

Summary | Funding | References

Introduction | How to … (recipe gallery) | How to assemble a single contig | How to assemble many contigs at once (SangerAlignment) | How to use a CSV mapping instead of regex | How to handle forward-only (or reverse-only) datasets | How to deal with low-quality / short reads | How to detect spurious low-overlap merges | How to choose a consensus base-calling method | How to interpret the chromatogram | How to deal with secondary peaks | How to launch the interactive Shiny app | How to export results | Constructor parameter reference | Required parameters (REGEX path) | Trimming parameters | Consensus parameters | Performance / parallelism parameters | Troubleshooting | Citation | Session info

Installation | Overview

Introduction | Installation | Interoperability with IsoformSwitchAnalyzeR | Paired differential expression and splicing | Preparing the experiment parameters | Check your metadata | Running the analysis | Additional parameters | Over-Representation Analysis | Running the inbuilt ORA function | Analysing ORA results | Scatter plot of enrichment scores | Session Info | References

Abstract | Introduction | Vignette contents | Input Data Format | PSM annotation and visualization | Simulation and visualization of isotopic envelopes for metabolites | Simulation and visualization of isotopic envelopes for peptides | Score functions for PSM evaluation | Visualization of SIP proteomic result | S4 classes in Aerith

Overview | What Makes Aerith Unique | Data Format Overview | File Format Conversion | Converting Raw Files to Supported Formats | Working with FT1 and FT2 Files | Reading MS1 and MS2 Scan Data | Creating Subset Files for Testing and Development | Working with mzML Files | Working with MGF Files | Processing Peptide Identification Results | Reading pepXML Files | Working with Sipros Output Files | Reading PSM Files from Sipros | Quality Control and Data Visualization | Total Ion Current (TIC) Analysis | Instrument Performance Analysis | Advanced Precursor Analysis | Summary | Key Strengths of Aerith: | Best Practices Summary:

Installation | Quick start | Up to speed with AnVIL | Use in the AnVIL cloud | Local use | Graphical interfaces | For end users | Fast binary package installation | Working with Google cloud-based resources | Using gcloud_*() for account management | Using gsutil_*() for file and bucket management | Using av*() to work with AnVIL tables and data | Tables, reference data, and persistent files | Using avtable*() for accessing tables | Using avdata() for accessing Workspace Data | Using avbucket() and workspace files | Using avnotebooks*() for notebook management | Using avworkflows_*() for workflows | Using avworkspace_*() for workspaces | Using drs_*() for resolving DRS (Data Repository Service) URIs | For developers | Set-up | Service APIs | Construction | Invoke endpoints | Process responses | Test endpoints | Service implementations | Extending the Service class to implement your own RESTful interface | Support, bug reports, and source code availability | Appendix | Acknowledgments | Session info

Introduction | Installation | Basic usage | Metadata-oriented BAM access | Filtering with ScanBamParam | Alignment-object output | Sequence and quality extraction | Fast count summaries | Relationship to existing Bioconductor tools | Session information

Introduction | Example data | Simple imputation | Passing paramters to the imputation function | The MARGIN argument | Mixed imputation | Different margins | Passing paramters to the imputation functions | Using the whole matrix to compute imputated values | Session information | License | References

Methods | Datasets | Existing search outputs | New search outputs | Introduction | MaxQuant | Label-free | TMT | DIA-NN | plexDIA | sage | FragPipe | Session information | License

BiocAzul | Installation | Package Loading | Introduction | Basic Usage | Connecting to the AnVIL Data Explorer | Listing Catalogs | Exploring Projects | Exploring Facets | Filtering and Queries | Integration with Terra | Conclusion | Session Information

Reading data as QFeatures | Encoding the experimental design | Filtering out contaminants and reverse hits | Removing up unneeded feature variables | Managing missing values | Counting unique features | Imputation | Data transformation | Normalisation | Feature aggregation | See also | Session information | License | References

Why use fraq? | Quick start | Pre-built functions | Walkthroughs with synthetic data | Summarization | quick-look QC plots | Splitting | Modification | Supported formats | Extension system: flow graph overview | Extension system: writing a custom kernel in R | Extension system: writing a custom kernel with Rcpp | Some important tips when building custom kernels | Streaming with named pipes | Tuning and threading | FRAQ file format | Session information

Loading the data | Data preprocessing | Running BANKSY | Run Harmony on BANKSY's embedding | Session information

1 Introduction | 2 Example 1: ENCODE data set with problematic samples | 3 Example 2: ER binding in tamoxifen resistant data set | 4 Example 3: Single sample assessment | 5 Customising QC plots | 6 Using ChIPQC and DiffBind together | 7 Acknowledgements | 8 Session Info

Overview | Demostration: a local data.frame with 5 columns | Demonstration: a remote GWAS file with 34 columns | Session Info

Overview | Load the libraries we need | Display a list of the currently supported genomes | Display MYC | Create and display minimal 1-row data.frame centered below MYC on chr8 | Create and display a simulated quantitative (bedGraph) track | Zoom out by direct manipulation of the currently displayed region | Zoom out and by function calls | Session Info

Overview | Display a genomic region of interest in igvR | Query the AnnotationHub | Select Two Resources: boadPeaks and fc bigwig | broadPeaks: subset and display | bigWig: subset and display | Session Info

Overview | Display a genomic region of interest in igvR | Obtain the coordinates | Navigate the Table Browser | Examine the Data | Download the Data | Read the data into R | Create and Display a Quantitative Track | Session Info

Overview | Example | Code | Display | Session Info

Overview | Explicit loading of "custom" hg38 | Use the SARS-CoV-2 genome | Configure and run an nginx webserver, with CORS and Byte-Range support | CORS: cross-origin resource sharing | Byte-range support | A sample nginx configuration. | Run nginx out of a docker container. | Session Info

Overview | Demonstration | Display | Session Info

Overview | Load the libraries | Initialize igvR | Scenario 1: Loading a Remote CRAM File | Scenario 2: Visualizing Local CRAM Files | Step 1: Start a Local Web Server | Step 2: Load the Track using "localhost" | Session Info

Introduction | Creation and masking | Analytic utilities | Exporting to file | Session Information

imageFeatureTCGA | Installation | Introduction | Importing HoverNet JSON Data | From Local Files | Import the H5ad file into a SpatialExperiment | Basic Overlay | Customized Overlay | Custom Color Palette | Visualizing Additional Features | Session Info

Key features of cross-domain analysis | Example with custom data | Interpretation

Installation | Available vignettes | Basic Usage | Analysis of American Gut data | Next steps

Key differences from standard SPIEC-EASI

Windows-specific considerations | Option 1: Use batch mode with snow | Option 2: Use serial processing | Option 3: Use batch mode | Batch Mode | Performance comparison | Key parameters | Platform-specific recommendations | Unix-like systems (Linux, macOS): | Windows systems:

Common issues and solutions | 1. Empty networks | 2. Very dense networks | 3. Computational issues | 4. Windows parallel processing issues | 5. Convergence issues | 6. Memory issues | Platform-specific considerations | Windows users: | Unix-like systems (Linux, macOS): | Diagnostic functions | Parameter tuning guidelines | For small datasets (< 100 samples, < 50 taxa): | For medium datasets (100-1000 samples, 50-200 taxa): | For large datasets (> 1000 samples, > 200 taxa): | Windows-specific recommendations:

Overview | Installation | Setup | Links to Data | Downloading Data | Loading Data into a SpaceMarkersExperiment | Filtering | Accessors | Undirected Analysis | Step 0: Visualize the starting patterns | Step 1: Find hotspots | Step 2: Calculate overlap scores | Step 3: Find pairwise interacting genes | Step 4: Calculate IM scores | Directed Analysis | Step 1: Calculate influence | Step 2: Find pattern hotspots | Step 3: Find influence hotspots | Step 4: Directed overlap scores | Step 5: Directed gene scores | Putting it together | Cleanup | Session Info

Overview | Installation | Setup | Links to Data | Downloading Data | Loading Data into a SpaceMarkersExperiment | Using load10X | Filtering | Running SpaceMarkers with the SME Object | Undirected Analysis | Inspecting Results | Directed Analysis | Visualization | Patterns on the tissue | Undirected hotspots | Undirected overlap scores | Undirected interaction overlay | IM scores bar plot and top genes | Directed hotspots | Directed overlap scores | Directed interaction overlays | Constructing a SpaceMarkersExperiment Manually | From an Existing SpatialExperiment | Backward Compatibility | Summary | Cleanup | Session Info

Introduction | Installation | Import and Export | The BiocFile Class | CompressedFile | For developers | Converting existing "File" Classes | Creating classes and methods that extend BiocFile's class and methods | Session info

Overview | Installation | Setup | Links to Data | Extracting Counts Matrix | load10xExpr | Obtaining CoGAPS Patterns | Obtaining Spatial Coordinates | load10XCoords | Executing SpaceMarkers | SpaceMarker Modes | Residual Mode | SpaceMarkers Step1: Hotpsots | SpaceMarkers Step2: Interacting Genes | DE Mode | Residual Mode vs DE Mode: Differences | Comparing residual mode to DE mode | Comparing DE mode to residual mode | Types of Analyses | Residual Mode vs DE Mode: Enrichment | Visualizing SpaceMarkers | Loading Packages | Code Setup | Get the Spatial Data | Generate Plots | Pattern_1 | Pattern_5 | Top SpaceMarkers | Negative SpaceMarkersMetric | Removing Directories | References | load10XExpr() Arguments | load10XCoords() Arguments | get_spatial_params_morans_i() Arguments | get_pairwise_interacting_genes() Arguments

Install carnation | Load libraries & airway dataset | Get more gene annotation | Create DESeqDataSet | Run differential expression analysis | Add functional enrichment results (optional) | Add pattern analysis (optional) | Compose carnation object | Data Organization | First Run | Initial setup | General layout | Feature overview | DE analysis | Functional enrichment | Pattern analysis | Gene scratchpad | Server Mode | Summary | sessionInfo

Introduction | Performance Considerations | Preparing the Input Data | Choosing a Method and Model of Evolution | Minimum Evolution Phylogenetic Trees | Maximum Likelihood Phylogenetic Trees | Maximum Parsimony Phylogenetic Trees | Calculating Bootstrap Support Values | More Examples of Manipulating Dendrograms | Inspecting the Inputs and Outputs | Exporting the Tree | Generating a Summary Tree | Session Information

1. Introduction | 2. Installation | 3. Run ANCOM-BC2 on a simulated dataset | 3.1 Generate simulated data | 3.2 Run ancombc2 function | 3.3 Power and FDR | 4. Benchmark the performance of ANCOM-BC2 on a null dataset | 4.1 Import example data | 4.2 Permute the bmi label | 4.3 Run ancombc2 function | 4.4 Visualization | 5. Run ANCOM-BC2 on a real cross-sectional dataset | 5.1 Import example data | 5.2 Run ancombc2 function using the phyloseq object | 5.3 Structural zeros (taxon presence/absence) | 5.4 ANCOM-BC2 primary analysis | Results for age | Results for bmi | 5.5 ANCOM-BC2 global test | 5.6 ANCOM-BC2 multiple pairwise comparisons | 5.7 ANCOM-BC2 multiple pairwise comparisons against a pre-specified group (Dunnett's type of test) | 5.8 ANCOM-BC2 pattern analysis | 5.9 Run ancombc2 function using the tse object | 5.10 Run ancombc2 function by directly providing the abundance and metadata | 6. Run ANCOM-BC2 on a real longitudinal dataset | 6.1 Import example data | 6.2 Run ancombc2 function using the phyloseq object | 6.3 ANCOM-BC2 primary analysis | 6.4 ANCOM-BC2 global test | 6.5 ANCOM-BC2 multiple pairwise comparisons | 6.6 ANCOM-BC2 Dunnett's type of test | 6.7 ANCOM-BC2 pattern analysis | 6.8 Run ancombc2 function using the tse object | 6.9 Run ancombc2 function by directly providing the abundance and metadata | 7. Bias-corrected log abundances | Session information | References

Installation | Examples | Preparing SNP data for genetic relationship matrix | Fitting the null model | P-value calculations | Manhattan and QQ plots for p-values | Session Information | GPU Acceleration (Optional) | References | See also

Introduction | Install | Basic usage | Example 1: Reproducing Fig.1d from [@prosit2019] | References | Session information

The QFeatures class | Converting tabular data | The single-set case | The multi-set case | Including sample annotations | Additional information | Sample names | Special case: empty samples | Reducing verbose | Under the hood | License | Reference

Introduction | Citing bnbc | Terminology | Dependencies | The ContactGroup class from bnbc | Alternatives | Getting your data into bnbc | Getting your data out of *.cooler files | Working with bnbc contact matrices | Per-Sample Adjustments | Cross Sample Normalization | sessionInfo() | References

Checking that that the induced tree is correct | Introducing a missing root node | Skipping over missing taxonomic assignments | Avoiding duplicated names across different taxonomic levels

Description | Amplicon Summary | Amplicon groups | Read distribution | Filtered Reads | Edit rates | Frameshift | Alignments plots

Description | ID Summary | Read distribution | Filtered reads | Edit rates | Frameshift | Alignments plots

Data requirements | Analysis time | Background | Setup: load required packages | Load data and plot data overview | Model dynamics | sd per time point | sd per condition | Model outputs | Differences of metabolite abundances between time points | Difference of dynamics between experimental conditions | Dynamic profiles | Cluster dynamics | Over-representation analysis of functional modules in dynamics clusters | Comparison of clusters of different experimental conditions | Dynamics | Metabolites | Combine both

About metabinR | Installation | Preparation | JVM heap size | The MetabinResult class | Abundance based binning example | Composition based binning example | Hierarchical (2-step ABxCB) binning example | In-memory inputs (Biostrings / ShortRead) | Session Info

About fastreeR | Function overview | Compressed VCF input | Installation | Preparation | Allocate RAM and load required libraries | Download sample vcf file | Download sample fasta files | Functions on vcf files | Sample Statistics | Calculate distances from vcf | Histogram of distances | Plot tree from fastreeR::dist2tree | Bootstrapping example | Bootstrap support explained | Interpretation guidance | Reproducibility note | Bootstrap example (small, runnable) | Optional: nicer plotting with ggtree | Command-line examples | JVM / rJava troubleshooting tips | Windowed VCF analysis | Windowed distance matrices | Windowed trees | Plot tree from stats::hclust | Hierarchical Clustering | Functions on fasta files | Calculate distances from fasta | Session Info

Introduction | Installation | Loading the package | Plot Single Molecules from SMF Data | Plot Single Molecules from dSMF Data | Plot Single Molecules from Nanopore Data | Listing Available Track | Session Info

Introduction | Prerequisites | Reading H5AD files and Zarr stores to a Seurat Object | Mapping between AnnData and Seurat | Writing a Seurat object to a H5AD file or Zarr store | Session info

Introduction | Prerequisites | Reading H5AD files and Zarr stores to a SingleCellExperiment object | Mapping between AnnData and SingleCellExperiment | Writing a SingleCellExperiment object to a H5AD file or Zarr store | Session info

Introduction | Relationship to other packages | Installation | Usage | Read from disk | Using AnnData objects | Interoperability | Manually create an AnnData object | Write to disk | Subsetting AnnData objects | Basic subsetting | Combined subsetting | Using different index types | Working with views | Citing anndataR | Session info

Introduction | Installing betterChromVAR | Computing motif deviations with betterChromVAR | Individual steps of betterChromVAR | Including fragment length bias | Note on single-cell data | Execution on Atlas-scale datasets | Bias-normalization of bulk ATAC-seq data | Appendix | References | Session info

Introduction | Background | Load packages | Data | Peptide table | Sample annotation | Convert to QFeatures | Data preprocessing | Encoding missing values | $\log_2$ transformation | Peptide-Filtering | Remove failed protein inference | Remove reverse sequences and contaminants | Remove highly missing peptides | Normalisation | Summarization to protein level | Data exploration and QC | Marginal distribution at precursor and protein level | Identifications per sample | Dimensionality reduction plot | Correlation matrix | Data Modeling (Robust Regression) | Sources of variation | Model Estimation | Statistical inference | Results table for significant proteins | Volcanoplots | Heatmaps | Assess performance | Real Fold changes | True and false positives | TPR - FDP curves | References

Load packages | Data | Precursor table | Sample annotation table | Convert to QFeatures | Data preprocessing | Encoding missing values | Precursor Filtering | Remove questionable identifications | Assay joining | Filtering: Remove highly missing precursors | Filter one-hit wonders | Log-transformation | Normalisation | Summarisation | Data exploration and QC | Marginal distribution at precursor and protein level | Charge state | Identifications per sample | Dimensionality reduction plot | Correlation matrix | Data Modeling (Robust Regression) | Sources of variation | Model Estimation | Statistical inference | Results tables for significant proteins | Volcanoplots | Heatmaps | Assess performance | Real Fold changes | True and false positives | TPR - FDP curves | Volcano-plots | References

Load packages | Data | Precursor table | Sample annotation table | Convert to QFeatures | Data preprocessing | Encoding missing values | Precursor Filtering | Remove questionable identifications | Assay joining | Filtering: Remove highly missing precursors | Filter one-hit wonders | Log-transformation | Normalisation | Summarisation | Data exploration and QC | Marginal distribution at precursor and protein level | Charge state | Identifications per sample | Dimensionality reduction plot | Correlation matrix | Data Modeling (Robust Regression) | Sources of variation | Model Estimation | Statistical inference | Results tables for significant proteins | Volcanoplots | Heatmaps | Assess performance | Real Fold changes | True and false positives | TPR - FDP curves | Volcano-plots | References

Abstract | Introduction | Moonlight's pipeline | Moonlight's proposed workflow | Installation | Installation from BioConductor | Installation from GitHub | Installation from GitHub with accompanying vignette | Load libraries | Obtain Input | Download: Get GEO data | getDataGEO: Search by cancer type and data type [Gene Expression] | FEA: Functional Enrichment Analysis | FEAplot: Functional Enrichment Analysis Plot | GRN: Gene Regulatory Network | URA: Upstream Regulator Analysis | PRA: Pattern Regognition Analysis | DMA: Driver Mutation Analysis | GMA: Gene Methylation Analysis | GLS: Gene Literature Search | Transcription factor-based layer of secondary evidence | loadMAVISp: Helper function for loading the MAVISp database | TFinfluence: Effect of mutations on transcription factors | Level of consequence: Effect of mutations on three different levels | plotNetworkHive: GRN hive visualization taking into account COSMIC cancer genes | plotDMA: Heatmap of the driver/passenger status of mutations in TSGs/OCGs | plotMoonlight: Heatmap of Moonlight Gene Z-scores for mutation-driven TSGs/OCGs | plotGMA: Heatmap of hypo/hyper/dual methylated CpG sites in TSGs/OCGs | plotMoonlightMet: Heatmap of Moonlight Gene Z-scores for methylation-driven TSGs/OCGs | plotMetExp: Visualize results from EpiMix of expression and methylation in genes | Moonlight Analysis: Case Studies | Case study n. 1: Predicting oncogenic mediators using Moonlight's primary layer | plotURA: Upstream regulatory analysis plot | Case study n. 2: Moonlight pipeline in one function | plotCircos: Moonlight Circos Plot | Case study n. 3: Moonlight with driver mutation analysis | Case study n. 4: Moonlight with gene methylation analysis | Citation | References

Overview | Why phylogeny-aware plots? | Setup | Taxonomy-based tree construction | Diet contrasts on day 7 | Interpretation | Longitudinal trajectories within HFHS | Interpretations | Alternative Sorting | Session Info

Overview | Citing AnVILWorkflow | Install and load package | Google Cloud SDK | Create Terra account | Major steps | Example in this vignette: bulk RNAseq analysis | Browse AnVIL resources | Setup | Clone workspace | Curated by this package | Any workspace you have access to | Prepare input | Current input | Update input | Run workflow | Monitor progress | Abort submission | Result | Session Info

Introduction | Statistical framework | Analysis workflow | Installation and setup | Install MSstatsResponse | Load required libraries | Data preprocessing with MSstats | Example MSstats workflow | Loading example data | Data preparation for dose-response analysis | Converting GROUP labels to numeric doses | Formatting data with MSstatsPrepareDoseResponseFit() | Drug-protein interaction detection | Isotonic regression | Running doseResponseFit() | Interpreting results | IC50 estimation | Understanding IC50 calculation | Running predictIC50() | Parallel processing for large datasets | Visualization | Individual protein dose-response curves | Comparing log2 vs ratio scale | Predicting at other response levels | Experimental design planning | Simulating future experiments | Sweeping experimental design parameters | Session information | References

Introduction | Why use goatea? | How goatea works | Running GOATEA: Shiny application - Graphical User Interface (GUI) | Web browser - HuggingFace Docker container | Local: via R(studio) or Docker | Installation | Running goatea Shiny GUI: via R(studio) | Running goatea Shiny GUI: via Docker | GOATEA GUI demonstration | Initialization - load data | Pre-enrichment visualization - Volcano plot | Pre-enrichment visualization - Overlap (UpSet) plot | Enrichment with GOAT | Post-enrichment visualization: split-dot plot | Post-enrichment visualization: termtree plot | Post-enrichment visualization: gene & genesets heatmap | Post-enrichment visualization: gene & effectsize heatmap | Post-enrichment visualization: protein-protein interaction graph | Information | Contact | License | Issues and contributions | GOAT reference | Session info

Introduction | Why use goatea? | How goatea works | Installation | Running GOATEA: Shiny application | Running goatea: automated analyses | Initialization | Loading genelists | Loading genesets | GOAT Enrichment Analysis | Genes overview table | Enrichment searching and filtering | Gene selection | Plotting | pre-enrichment: Volcano plot | pre-enrichment: UpSet overlap plot | Post-enrichment: termtree plot | Post-enrichment: split-dot plot | Post-enrichment: gene-effectsize heatmap | Post-enrichment: gene-genesets heatmap | Post-enrichment: protein-protein interaction network graph | Information | Contact | License | Issues and contributions | GOAT reference | Session info

Motivation | Creating BenchmarkInsights class | Visualization | Available plot | Interpretation benchmark result | Case Study: What is the overview of summary? | Case Study: What is the correlation between evidence/metric/method? | Case Study: What is the time and memory trend? | Case Study: Which metric is most effective on the method? | Case Study: How does method variability differ across datasets for a specific metric? | Cheatsheet | Session Info

Overview | For Benchmark Developer | Initialising the Study | Define mapping function and protocol function | Upload Study | For Method Developer | Loading the Study | Preparing for evaluation | Evaluate | Summary | Session Info

Preparing and Submitting a Trio | Step 1: GitHub Personal Access Token (PAT) | Set the PAT in R | Step 2: Build the submission bundle | Step 3: Submit the Trio | Step 4: Verify the submission | Step 5: Downloading an existing Trio | Notes | Session Info

Import microbiome data | Initialise a Trio object | Cross validation - using the trio split function | Without Cross-validation | Importing result to BenchmarkInsights | Session Info

Creating Trio object | Method 1: BenchHub datasets | Method 2: Source and ID | Method 3: Load an object directly | Bonus: Using a custom loader | Adding Components to Trio Sequentially | Adding metrics | Other Features | Caching | Data Splitting | Conclusion | Session Info

Introduction | Overview of PSM Annotation | Advantages of Aerith | Analysis of Unlabeled PSM at Natural ^13^C Abundance (1.07%) | Fragment Ion Annotation at MS2 Level | Interactive PSM Annotation Visualization | Comprehensive Fragment Analysis with Theoretical Overlays | Precursor Ion Analysis at MS1 Level | Analysis of Heavily Labeled PSM at 50% ^13^C Incorporation | Fragment Ion Annotation in Labeled Samples | Visualization of Heavy Labeling Effects | Theoretical vs Observed Fragment Comparison in Heavy Labeling | Precursor Analysis Under Heavy Labeling Conditions | High-Throughput Batch Processing of PSMs | Automated Batch Analysis Workflow | Summary and Best Practices | Key Takeaways | Parameter Selection Guidelines

Importing Data from biome Format | To Phyloseq | To TreeSummarizedExperiment | Importing Data from qiime Format | Importing Data from mothur Format | Importing Data from metaphlan Format | Conclusion | Session info

Introduction | Motivation | Comparison with Existing Packages | Features of HiCPotts | Installation | Workflow Overview | Step 1: Loading Hi-C Data | Step 2: Processing Data | Step 3: Running MCMC Simulations | Step 4: Computing Posterior Probabilities | Worked Examples: Hi-C and Micro-C Data | Example 1: Hi-C Data | Step 1 — Locate input files | Step 2 — Load Hi-C data and compute covariates | Step 3 — Process data into matrices | Step 4 — Run MCMC with a realistic sampler configuration | Step 5 — Compute posterior component probabilities | Example 2: Micro-C Data | Step 2 — Load Micro-C data | Step 3 — Process into matrices | Step 4 — Run MCMC | Step 5 — Posterior probabilities and classification | Practical Notes on Hi-C vs. Micro-C | Advanced Usage | Custom Priors | Parallel Processing | Other Distributions | Technical Notes | Conclusion

Call required libraries. | Getting a sample data for the GRanges object | Using the GRanges object for a plot using shiny.gosling | Method 1 - Using the track_data_gr function | Method 2 - Using the track_data_csv function | Session Info

Introduction | Installation | Prepare Data | Labeled Data | Score Samples | Scale and Transform Score | Marker Selection | Un-labeled Data | Compute Overall Score for Gene-set | SessionInfo

Introduction | Installation | Quick start to using GO-a-GO | Example dataset of gene pairs | Enrichment analysis of the example dataset | Plotting the enriched Gene Ontology terms | Comparison to unpaired Gene Ontology enrichment | Extracting gene pairs associated with terms of interest | Obtaining additional statistics | Extracting gene pairs from paired genomic regions | Citing GO-a-GO | Session information | Bibliography

Setup and Data Load | Heatmaps | Correlations between receptors and transcription factors | Heatmap of Transcription Factor Activation Scores | Heatmap of Incoming Signaling for a Cluster | Heatmap of Signaling Between Clusters | Network Plots | Network showing L - R - TF signaling between clusters | Network Showing Interaction Strength Across Data | Other Types of Plots | Chord Diagrams Connecting Ligands and Receptors | Scatter Plot to Visualize Correlation | Continued Development

Introduction | Installing CompensAID | Pre-processing | Final data | CompensAID: run tool | Secondary Stain Index Matrix | Identify flagged marker combinations | Dot plots | Session info

Load Data and Libraries | Pivot Sample | Pivot Feature | Pivot Experiment | Session Info

Exposure Metadata and Ontology Annotation | Codebook Setup | Ontology Choices | Ontology Annotation App | Session Info

Load Data and Libraries | Quality Control | Session Info

Installation | Command Structure | Quality Control | Missingness | Plot missing variables withing exposure group | Filtering Omics Features | Normality Check | Principal Component Analysis | Plot the correlation tile plot | Exposure Visualization | Sample-Exposure Association | Sample Clustering | Exposure Correlations | Exposure-wide association (ExWAS) | Differential Abundance | Exposure-Omics Association | Enrichment Analysis | Enrichment Visualizations | Enrichment Summary | DotPlot | Term Network Plot | Heatmap | Cnet Plot | Pipeline Summary | Saving Results | Session Info | References

Introduction | Key Features | Installation | Basic Usage | Simple Simulation | Accessing Results | Visualization | Understanding the Noise Model | Biological Noise: Beta Distribution | Technical Sequencing Noise | Depth Overdispersion | Binomial Read Sampling | Common Use Cases | Low Purity Tumor | High Coverage Sequencing | Low Coverage Sequencing | Ideal Data (No Noise) | Including Germline Variants | Bioconductor Integration | Export to GRanges | Export to VCF | Export for Other Tools | PyClone Format | SciClone Format | Simple CSV | Benchmarking Workflow | Advanced: Custom Clonal Structures | Complex Hierarchies | Accessing Clonal Structure | Session Information | References

Introduction | Installation | Loading iscream | tabix() | Setup | Making queries | Using r Biocpkg("GenomicRanges") | Setting column names | Rsamtools-style output | WGBS BED files | summarize_regions() | make_mat() | Further reading | Session info | References

Introduction | Features | Installation | Enriching native MultiAssayExperiment | Using omXplore | Individual built-in plots | Main UI | Session information

Introduction | Prepare Input Data | Methods | CompPASS | HGScore

Introduction | The RflomicsMAE and RflomicsSE classes | Methods for RflomicsMAE and RflomicsSE classes | Instantiating a RflomicsMAE object from a MAE object | Overview of the main methods | Executing the workflow : | Define the statistical settings: model and contrasts | Single-omics analysis | Data processing and quality control | Differential analysis: settings and results | Co-expression analysis: settings and results | Annotation enrichment analysis: settings and results | Multi-omics integration analysis | Generating a report from the RflomicsMAE object | Session information

Introduction | Locating the toy data | Installation | Loading the package | Building the database | Quick gene-locus inspection | Identifying putative enhancers | Cross-referencing against curated enhancer databases | Identifying enhanceosome regions | TF co-binding statistics | Visualising an enhanceosome with signal tracks | Gene-centred visualisation | Chromatin state classification | Moving on to the full dataset | Interactive showcases | Citation | Session information | References

Licensing | Citing | Introduction | Installation | General workflow | Gene-Term network loadable in Cytoscape | Run an enrichment analysis | Start Cytoscape | Create a gene-term network | Enrichment map | Create an enrichment map in a ggplot format | Using list of term IDs | Enrichment map customization | Create an enrichment map in an igraph format | Effect of seed value | Enrichment map with groups from different enrichment analyses | Create an enrichment map using multiple enrichment analyses in a ggplot format | Create an enrichment map using multiple enrichment analyses in an igraph format | Enrichment map with groups from same enrichment analysis or from complex designs | Create an enrichment map using mutliple subsections of one enrichment analysis in a ggplot format | Create an enrichment map using a complex design | Create an enrichment map using mutliple subsections of one enrichment analysis in a igraph format | Create an enrichment map using mutliple subsections of multiple enrichment analyses in a igraph format | Code Ocean Capsule | Acknowledgments | Session info | References

Overview | Data Loading | Benchmark Provenance | Methods Rationale | Input Files | Reference Counts | Best-Observed Summary | BamScale-versus-Comparator Fold Change | Single-File step1 | Single-File galignments | Multi-File step1 | Multi-File galignments | Single-File seqqual | Multi-File seqqual | Compact-versus-Compatible seqqual | Interpretation | Session Information

Introduction | Installation | Getting Started | Input data structure | Simulation | Simulating CNA data | Wavelet-based feature extraction | Visualising wavelet coefficients for a single profile | Classification using wavelet features | Comparison with PCA/ICA | Plotting classification error and AUC | Session Information

0. Installation | I. Introduction | II. Workflow | 1. Joining data | 2. Penalties | 1. make_pen | 2. fragment_HL_pen | 3. fragment_inty_pen | 4. score_fun_ave | 3. Fragmentation | 1. fragment_HL | 2. fragment_inty | 3. score_fun_ave | 4. Statistics | 5. Visualization | III. Outputs | 1. adjusting_HLToInt | IV. Plots | 1. plot_decay_synt | 2. plot_heatscatter | 3. plot_density | 4. plot_histogram | 5. plot_scatter | 6. plot_volcano | III. Additional functions | 1. score_fun_ave | 2. gff3_preprocess

Guiding principles | Scope | Zarr version | Functional programming and API design

IGVF background | Knowledge graph of variant function | The rigvf package | Data license | Catalog API | GRanges-based queries | ArangoDB API | Use with Bioconductor | Compute overlap with plyranges | Plot variants with plotgardener | Session info

Introduction | Prerequisites | Constructing cell sets | Assessing overlaps of pairs of cell sets | Identifying and scoring top overlaps | Scoring the cells | The runCSOA wrapper | Session information

Getting started | Custom settings | Applications | Session info

Preamble | Introduction | Input data | Methods | Some background knowledge | Overview | Key publications | Outline of this Vignette | Other methods not covered in this vignette | Let's get started | Installation | Load the package | Upload data | Quick start in mixOmics | PCA on the multidrug study | Load the data | Example: PCA | Choose the number of components | PCA with fewer components | Identify the informative variables | Sample plots | Variable plot: correlation circle plot | Biplot: samples and variables | Example: sparse PCA | Choose the number of variables to select | Final sparse PCA | Sample and variable plots | PLS on the liver toxicity study | Load the data | Example: sPLS1 regression | Number of dimensions using the $Q^2$ criterion | Number of variables to select in $\boldsymbol X$ | Final sPLS1 model | Sample plots | Performance assessment of sPLS1 | Example: PLS2 regression | Number of dimensions using the $Q^2$ criterion | Number of variables to select in both $\boldsymbol X$ and $\boldsymbol Y$ | Final sPLS2 model | Numerical outputs | Importance variables | Graphical outputs | Performance | PLS-DA on the SRBCT case study | Load the data | Example: PLS-DA | Initial exploration with PCA | Number of components in PLS-DA | Final PLS-DA model | Classification performance | Background prediction | Example: sPLS-DA | Number of variables to select | Final model and performance | Variable selection and stability | Sample visualisation | Variable visualisation | Take a detour: prediction | AUROC outputs complement performance evaluation | N-Integration | Block sPLS-DA on the TCGA case study | Load the data | Parameter choice | Design matrix | Number of components | Number of variables to select | Final model | Sample plots | plotDiablo | plotIndiv | plotArrow | Variable plots | plotVar | circosPlot | network | plotLoadings | cimDiablo | Model performance and prediction | P-Integration | MINT on the stem cell case study | Load the data | Example: MINT PLS-DA | Example: MINT sPLS-DA | Number of variables to select | Final MINT sPLS-DA model | Sample plots | Variable plots | Correlation circle plot | Clustered Image Maps | Relevance networks | Variable selection and loading plots | Classification performance | Take a detour | AUC | Prediction on an external study | Session Information | mixOmics version | Session info | References

Introduction | Installation | Cite our work | Preparing the genotype matrix | The CT-CBN model | The H-CBN model | Analysis of cancer progression pathways | The R-CBN algorithm | The B-CBN method | Analysis of fitness landscapes | Session Info | References

What is it for? | Main worfklow | Acknowledgements | Session Information

Introduction | Setup | Comparing Clustering Methods | Custom Embedding Workflows | Using Different ESM-2 Model Sizes | Embedding Both Chains | Integration with scRepertoire Clonotypes | Analyzing Selection Pressure | Combining Distance-Based Methods | Working with Large Datasets | HLA Association Analysis | Exporting Results | Best Practices | Session Info

Introduction | Installation | Python Dependencies | Quick Start | Loading the Package and Example Data | Extracting TCR Data | Summarizing TCR Repertoire | Analysis Functions | TCR Clustering with clusTCR | TCR Distance Calculations with tcrdist3 | Generation Probability with OLGA | Protein Language Model Embeddings | Metaclone Discovery with Metaclonotypist | Selection Inference with soNNia | Workflow Example | Session Info

Overview | Parallel Computing | Application Program Interface (API) | Preparing Data | Data Format used in SeqArray | Format Conversion from VCF Files | Export to VCF Files | Modification | Data Processing | Functions for Data Analysis | Get Data | Apply Functions Over Array Margins | Apply Functions in Parallel | Examples | The performance of seqApply | Missing Rates for Variants | seqApply | C++ Integration | seqBlockApply | seqBlockApply + Parallel | seqMissing | Missing Rates for Samples | Allele Frequency | seqAlleleFreq | Principal Component Analysis | Multi-process Implementation | Individual Inbreeding Coefficient | Resources | Session Information | References

Introduction | Once the analysis has been performed, the result is displayed via two graphicson the right-hand side of the panel (see Figure~\ref

Introduction | Develop a plot module for omXplore | Adding an existing module | From a R package | From R console | Session information

FLAMES | Creating a pipeline | Running the pipeline | HPC support | Visualizations | QC plots | Custom barcode designs | FLAMES on Windows | Citation | Session Info | References

Options and Setup | Data preparation | Installation | Loading TF and R - L data | Loading SCENIC Results | Load CellPhoneDB Database | Optional: Adding interactions manually | Analysis with Domino object | Required inputs from data set | Create Domino object | Build Domino Network | Visualization of Domino Results | Summarize TF Activity and Linkage | Cumulative signaling between cell types | Specific Signaling Interactions between Clusters | Continued Development

Abstract | Introduction | Installation | Load packages and example data | Run PARATI from a VCF file path | Explore returned results | Export result files | Output files and summary columns | Meaning of sim_perc_summary columns | Integration with Bioconductor VCF workflows | Inputs | 1. Trio genotype VCF | 2. Family index table | Notes | Session Info

Introduction | Installation | Loading packages | Import Prov-GigaPath tile level embeddings | Embedding PCA | Spatial Patterns | Adding HoverNet Nuclei Features | Visualizing Hovernet nuclei vs tile coordinates to see that they do not match | Scale factor between nuclei coordinates and tile coordinates | Session Info | References

Object contents | Access functions | Input data | Calculations | Linkages | Signaling Matrices | Build information | Continued Development

Introduction | Getting started | struct helper functions | Creating a new struct object | Changing the default methods | Changing the default show output | Class-based templates and struct objects | DatasetExperiment objects | model objects | using the model template | model_seq objects | iterator objects | metric objects | chart objects | entity and enum objects | Ontology | Session Info

Overview | Quick start | More experimental designs | More comparison types | Subsetting samples | More output options | Session information

Overview | Quick start | Multi-batch analyses | With proteomics | Cell type annotation | Session information

Installation | Class structure | Introduction | Geometries | Column and row | Annotation | Spatial graphs | Multiple samples | Object construction | From scratch | Space Ranger output | Vizgen MERFISH output | 10X Xenium output | Nanostring CosMX output | Other technologies | Coercion from SpatialExperiment | Coercion from Seurat | Operations | Non-geometric | Geometric | Crop | Transform | Limitations and future directions | Session info

Introduction | Installation | System requirements | ClustIRR algorithm | Input | Algorithm | Step 1. Compute TCR clonotype similarities in a repertoire with cluster_irr | Step 2-3. Construct TCR repertoire graphs and join them into $J$ | Run steps 1-3 with clustirr | Inspect the content of clust_irrs | Inspect graphs with plot_graph | You can evaluate $J$ with igraph | Step 4. community detection with detect_communities | Inspecting the outputs of detect_communities | Qualitative similarity between community abundance vectors with get_honeycombs | Quantitative similarity between community abundance vectors with get_cosine_similarity | Summary of communities is provided in the detect_communities outputs | Special functions: decoding communities with decode_community | Step 5. differential community occupancy (DCO) with dco | Step 6. Inspect results | Visualizing the distribution of $\beta$ with get_beta_violin_ag | Compare $\beta$s of clonotypes specific for CMV, EBV, flu or MLANA? | Posterior predictive checks | Differential community abundance results $\rightarrow$ par. $\delta$ | Conclusion: you can also use custom community occupancy matrix for DCO!

Introduction | What is VISTA? | Dataset Overview | Installation and Setup | Data Preparation | Load the airway dataset | Extract counts and metadata | Prepare data for VISTA | Create VISTA Object | Using DESeq2 backend | Validate object integrity | Alternative: Using edgeR backend | Alternative: Using limma-voom backend | Advanced: covariates, design formula, and consensus mode | Add gene annotations | Explore the Results | Access differential expression results | Count significant genes | Quality Control Visualizations | Sample Correlation Heatmap | Basic correlation heatmap | Customize color scheme | Show correlation values | Principal Component Analysis (PCA) | Basic PCA with labels | PCA colored by different metadata | PCA with top variable genes | PCA with custom circle size | PCA without labels | Multidimensional Scaling (MDS) | Basic MDS plot | MDS with top variable genes | MDS with custom shapes | Uniform Manifold Approximation and Projection (UMAP) | Basic UMAP plot | UMAP colored by a user-defined metadata column | Differential Expression Visualizations | DEG Count Summary | Basic count barplot | Faceted by regulation | Volcano Plot | Basic volcano plot | Customize cutoffs and labels | Custom colors | MA Plot | Basic MA plot | Label top genes | Custom cutoffs | Expression Pattern Analysis | Prepare gene sets | Expression Heatmaps | Basic heatmap | Heatmap with explicit gene set | Heatmap with k-means clustering | Heatmap with column annotations | Heatmap with multiple column annotations and cluster_by | Heatmap showing each replicate | Expression Barplots | Basic barplot | Log-transformed with statistics | Per-sample barplot for selected genes | Compare up and down regulated genes | Expression Boxplots | Basic boxplot | Boxplot without faceting | Boxplot with faceting by gene | Boxplot with gene facets AND statistics | Pooled genes with statistics | Log-transformed with p-values | Expression Violin Plots | Basic violin plot | Violin with log2 transformation | Violin with z-score transformation | Additional Expression Plots | Density plot | Scatter plot (sample vs sample) | Line plot (expression across samples) | Lollipop plot | Per-sample lollipop plot | Joyplot by treatment group | Joyplot by sample | Raincloud plot (expression) | Functional Enrichment Analysis | MSigDB Enrichment | Hallmark gene sets - Upregulated | Hallmark gene sets - Downregulated | Enrichment Visualizations | VISTA dotplot (default) | Barplot (clusterProfiler native) | Dotplot with customization | Network plot (clusterProfiler native) | Chord diagram (gene--pathway relationships) | Pathway-Specific Expression Heatmaps | Extract genes from top pathways | Heatmap of genes from top enriched pathways | GO Enrichment | Biological Process | GO Visualization | Gene Set Enrichment Analysis (GSEA) | GSEA with MSigDB Hallmark gene sets | GSEA with GO Biological Process | GSEA enrichment overview | GSEA plot for top pathway | GSEA plot for multiple pathways | GSEA with GO visualization | KEGG Pathway Enrichment | KEGG upregulated genes | KEGG downregulated genes | KEGG Visualization | Fold-Change Analysis | Fold-change Matrix | Fold-change Barplot and Lollipop | Per-gene fold-change barplot | Per-gene fold-change lollipop | Fold-change Raincloud | Fold-change Heatmap | Basic FC heatmap | FC heatmap for selected genes | FC heatmap with gene names | FC heatmap for specific gene set | Export Results | Export DE results to file | Save VISTA object | Summary | Workflow Completed | Key Features Demonstrated | Plotting Functions Used | QC Plots | DE Visualization | Expression Plots | Enrichment Plots | Fold-Change | Next Steps | Session Information | References

Overview | Installation | Quick Start | Vignettes | Workflow Vignettes | Building Trees from Taxonomies | Customizing Style | Exporting Views | Runtime Evaluation | Case-Study Vignettes | HFHS Diet and the Mouse Microbiome | Atlas 1006 — Tipping Points in the Human Gut | Diet Analysis with DESeq2 Normalization | Global Patterns — phyloseq and TreeSummarizedExperiment | COVID-19 Immunology — Beyond Microbiome Data | Function Reference | Contact

Introduction | Installation | Import dataset | Filtering | Normalization | Imputation | Aggregation | Differential Analysis | Hypothesis test | Fold-change | Push p-value | P-value calibration | FDR control | Session information | References

Introduction | Overview of this vignette | Installation | Load packages | Load data | Input: Coordinate data frame and Normalized expression matrix | Alternative input: SpatialExperiment object | Alternative input: Seurat object | Neighborhood analysis for cluster 2 cells | Quick look at the boundaries of one cluster | Detect spatial boundaries | Obtain neighborhood ring regions | Statistics of cells inside rings | Neighborhood interaction of clusters inside rings | DE analysis of cluster 2 cells inside and outside boundaries | Spatial DE analysis of cells in cluster 0 along spatial weights | Compute and plot spatial weights | Perform spatial differential analysis along boundary weights | Spatial enrichment analysis for each gene | Session Info

Introduction | Speed and memory requirements | Data structure and content | Importing experiments | Exporting experiments/MS data | MS experiments | Spectra objects | Reporter ions | Chromatogram objects | Plotting raw data | MS data space | MS Spectra | MS Chromatogram | Tandem MS identification data | Adding identification data | Filtering identification data | Calculate Fragments | Quality control | Raw data processing | Cleaning spectra | Spectrum processing | MS2 isobaric tagging quantitation | Reporter ions quantitation | Importing quantitation data | Importing chromatographic data from SRM/MRM experiments | Peak adjustments | Processing quantitative data | Data imputation | Normalisation | Feature aggregation | Label-free MS2 quantitation | Peptide counting | Spectral counting and intensity methods | Spectra comparison | Plotting two spectra | Comparison metrics | Quantitative assessment of incomplete dissociation | Combining MSnSet instances | Combining identical samples | Combine different samples | Splitting and unsplitting MSnSet instances | Averaging MSnSet instances | MS^E^ data processing | Session information | References

Overview | Color Palette | Widget Size | Sample Label Styling | Tree-bar Layout Ratio | Legend Placement | Optional Hierarchical Reordering | Session Info

Introduction | Step 1: Exporting from Browser | Step 2: Open in Editor | Step 3: Modify and Save

Setup | phyloseq Inputs | Phylogenetic Tree Tree | Using Taxonomic Hierarchy | TreeSummarizedExperiment Inputs

Setup | Interpretation | Session Info

Setup | Interpretation | Session Info

Introduction | Calculating fragment ions | Visualising fragment ions | Session information

Introduction | Quick clustering | Automated PC choice | Detecting correlated genes | Session information

Background | How to get help | How to cite | Quick start | Example analysis | Quantification without protein summaries | Data input | The detection probability curve | Quantification | Differential expression | Working with data already summarized at the protein-level | Post-translational modifications (PTMs) or immunoprecipitation mass spectrometry (IP-MS) | Differential peptide usage | Very large datasets | Further documentation | Acknowledgements | Funding | Cited references

Introduction | Computing feature-level statistics | Making gene symbols unique | Creating a data.frame | Scaling normalization | Median-based normalization | Pooling normalization | Spike-in normalization | Session information

Introduction | Installation | Demonstration | Sanity check | SummarizedExperiment wrapper; subsetting with GenomicRanges | Session information

Introduction | Citation | Disclaimer | Algorithm Overview | Computational Considerations | LimROTS Function Parameters | UPS1 Case Study | Dataset Description | Loading the Data | Running LimROTS | Volcano Plot | Quality Control | References

Introduction | Citation | Dataset Description | Loading the Data | Cox Proportional Hazards Analysis | Running LimROTS_survival | Results | Competing Risks Analysis | Preparing the Data | Quality Control | References

Introduction | Objectives | Installation | Load data | Samples summary | The entry point to plot | Plot base peak chromatogram (BPC) | Plot total ion chromatogram (TIC) | Total ion current | Intensity maps | Plot chromatograms | Plot retention time alignment effect | Plot grouped peaks across samples (features) | Plot peak density alongside chromatograms | Plot chromatograms with mass traces | Plot chromatograms from raw files | Plot spectra | Select closest scan to a specified RT | Select scan closest to a detected peak apex | Select multiple scans across a detected peak | Plot standalone spectra | Interoperability | Compound Discoverer | MZmine | MS-DIAL | Session Info

Installation instructions | Introduction | Handling and processing identification data | Loading PSM data | Keeping all matches | Filtering data | Remove decoy hits | Keep first rank matches | Remove shared peptides | All filters in one function | The mzR and mzID parsers | Session information

Introduction | Install and load the package | ZarrArray objects | Construction | Array and matrix operations | Other operations | Write an array-like object to disk in Zarr format | Session information

Introduction | Peptide-protein relation | Visualising adjacency matrices | Colouring the graph nodes | Colouring protein nodes | Colouring peptide nodes | Using quantitative data | Prioritising connected components | Session information

Introduction | Loading Libraries | Acquiring and Preparing Repertoire Data | getIMGT | formatGenes | inferCDR | Generating and Augmenting Sequence Sets | generateSequences | mutateSequences | Feature Engineering from Repertoires | calculateFrequency | calculateEntropy | calculateProperty | calculateGeneUsage | calculateMotif | probabilityMatrix | adjacencyMatrix | buildNetwork | Basic Usage | Standard BCR/TCR Network | Finding Clonotypes with V Gene Filtering | Encoding Sequences for Model Input | sequenceEncoder | onehotEncoder | propertyEncoder | geometricEncoder | tokenizeSequences | sequenceDecoder | Training a Model | Example 1: Classifying Sequences with Random Forest | Example 2: Unsupervised Clustering with PCA and Geometric Encoding | Example 3: Identifying Sequence Communities with Network Analysis | Conclusion | Session Info

Introduction | Loading Libraries | Loading Data | What data to load into scRepertoire? | Demonstrating Manual Data Loading (10x Genomics) | Other alignment workflows | Supported Formats and Expected File Names | Multiplexed Experiment | Important Considerations for createHTOContigList() | Example Data in scRepertoire | Combining Contigs into Clones | combineBCR | How combineBCR() Groups Related Clones | Advanced: Grouping by Alignment | Filtering and Cleaning Data | Additional Processing | addVariable: Adding Variables for Plotting | subsetClones: Filter Out Clonal Information | exportClones: Save Clonal Data | clonalBin: Bin Clones by Frequency or Proportion | Basic Usage with Proportion | Using Frequency-Based Binning | Grouping by a Variable | annotateInvariant | Basic Clonal Visualizations | cloneCall: How to call clones. | clonalQuant: Quantifying Unique Clones | clonalAbundance: Distribution of Clones by Size | clonalLength: Distribution of Sequence Lengths | clonalCompare: Clonal Dynamics Between Categorical Variables | clonalScatter: Scatterplot of Two Variables | Visualizing Clonal Dynamics | clonalHomeostasis: Examining Clonal Space | clonalProportion: Examining Space Occupied by Ranks of Clones | Summarizing Repertoires | percentGeneUsage | vizGenes: Flexible Gene Usage Visualization | percentGenes: Quantifying Single Gene Usage | percentVJ: Quantifying V-J Gene Pairings | percentAA: Amino Acid Composition by Residue | positionalEntropy: Entropy across CDR3 Sequences | positionalProperty: Amino Acid Properties across CDR3 Sequence | percentKmer: Motif Quantification | Comparing Clonal Diversity and Overlap | clonalDiversity: Clonal Diversity Quantification | How clonalDiversity() Handles Downsampling and Bootstrapping | Available Diversity Metrics (metric) | clonalRarefaction: Sampling-based Extrapolation | Hill Numbers and Their Interpretation | plot.type Options | Rarefaction using Species Richness (q = 0) | Rarefaction using Shannon Diversity (q = 1) | clonalSizeDistribution: Modeling Clonal Composition | clonalOverlap: Exploring Sequence Overlap | method Parameters for clonalOverlap() | Combining Clones and Single-Cell Objects | Preprocessed Single-Cell Object | Note on Dimensional Reduction | Functions to Exclude VDJ Genes | combineExpression | Calculating cloneSize | Combining both TCR and BCR | Visualizations for Single-Cell Objects | clonalOverlay | clonalNetwork | Filtering Options for clonalNetwork() | highlightClones | clonalOccupy | alluvialClones | Basic Alluvial Plot | Filtering to Top Clones | Highlighting Specific Clones | Visual Customization | getCirclize and vizCirclize | Quick Visualization with vizCirclize | Manual Control with getCirclize | Multi-Level Hierarchical Grouping | Directional Flow | Different Similarity Methods | Subsetting and Proportion | Quantifying Clonal Bias | StartracDiversity | Indices Output from StartracDiversity() | Calculating a Single Index | Pairwise Migration Analysis | clonalBias | Clustering by Edit Distance | clonalCluster: Cluster by Sequence Similarity | Core Concepts | Understanding the threshold Parameter | Demonstrating Basic Use | Demonstrating Clustering with a Single-Cell Object | Returning an igraph Object: | Returning a Sparse Adjacency Matrix | Using Both Chains | Using Different Clustering Algorithms | Conclusion | Session Info

Introduction | Installation | Input data format | Platform Support | Two key steps | The detection phase | The classification phase | Helpful information on parameters | Platform selection | Example use cases | Parameters for detectEdgeArtifacts() | For all platforms | For standard Visium | For VisiumHD | Parameters for classifyEdgeArtifacts() | Platform Comparison Summary | Understanding the output columns | Example: Standard Visium workflow | Data preparation: converting to dense matrix | VisiumHD Workflow Example | VisiumHD 16µm Resolution Example | VisiumHD 8µm Resolution Example | Key VisiumHD Considerations | Visualization: QC Metrics and Detection Results | Classification Summary | Final Classification Summary | Raw Edge Detection Summary | Quality Control Validation | Filtering Out Problematic Spots (Optional) | Conclusion | Session Information

Introduction | Installation | Downloading Reference Sequences | Downloading HLA Sequences (IPD-IMGT/HLA) | Downloading TCR/BCR Sequences (IMGT) | Downloading Germline Sets from OGRDB (AIRR) | Working with the Cache | Listing Cached Files | Loading from Cache | Refreshing the Cache | Exporting Reference Databases for External Tools | Export to MiXCR | Export to TRUST4 | Export to Cell Ranger VDJ | Export to IgBLAST | Conclusion | Session Info

Introduction | Installation | Quick Start (Seurat Objects) | Load Data | Predict Cell Types | Evaluate Performance | Quick Start (SummarizedExperiment Objects) | Prepare Data | Session Info

Introduction | Installation | The Chromatograms object | Available backends | Chromatographic peaks data | Chromatograms metadata | Creating Chromatograms objects | Access data from a Chromatograms object | peaksData | chromData | Lazy Processing and Parallelization | Processing queue | Parallelization | Changing backend type | Choosing the right backend | Plotting chromatograms from a Spectra object | Understanding Factorization | Re-factorizing after metadata changes | Extracting chromatographic regions of interest | Basic extraction by retention time | Extraction with m/z filtering (ChromBackendSpectra only) | Imputing missing values in chromatograms | Available imputation methods | Extrapolation vs. Interpolation | Example: Imputing an extracted ion chromatogram (EIC) | Selecting the right imputation method | Imputation in lazy evaluation pipelines | Comparing chromatograms | Comparing chromatograms within a single object | Comparing two Chromatograms objects | Comparing groups of chromatograms | Session information

Overview | Minimal Workflow | From a Count Matrix | From a Seurat Object | From a SingleCellExperiment Object | Common Use Cases | Compare Multiple Dissimilarity Methods | Evaluate Multiple Consistency Metrics | Visualize Results | Using Marker Genes Instead of HVGs | Focus on Specific Gene Sets | Interpreting Results | What Low Scores Mean | Next Steps for Low-Scoring Cell Types | Available Methods and Metrics | Dissimilarity Methods | Consistency Metrics | Tips and Best Practices | Getting Help | Session Info

Introduction | Key Features | Quick Start | Generate Example Data | Core Workflow | Step 1: Create scTypeEval Object | Step 2: Process Data | Step 3: Extract Relevant Features | Highly Variable Genes | Cell Type Marker Genes | Custom Gene Lists (Optional) | Step 4: Dimensional Reduction (Optional but Recommended) | Step 5: Compute Dissimilarity Matrices | Pseudobulk-based Distances | Wasserstein Distance | Reciprocal Classification | View Available Dissimilarity Matrices | Step 6: Compute Consistency Metrics | Compute Silhouette Scores | Compute Neighborhood Purity | Compare Multiple Metrics | Visualization | Dissimilarity Heatmap | Pseudobulk PCA | Interpretation Guidelines | Identifying Problematic Annotations | Recommendations | Session Information | References

Abstract | Installation | Running Macarron | Input CSV files | Output Files | Run a demo in R | Using CSV files as inputs | Using dataframes as inputs | Running Macarron as individual functions | Advanced Topics | Generating the input chemical taxonomy file | Accessory output files | Macarron.log | modules_measures_of_success.csv | Maaslin2 results | Changing defaults | Filtering metabolic features based on prevalence | Minimum module size | Specifying fixed effects, random effects and reference | Command line invocation

1. Anatomy of a MPSE | 2. Overview of the design of MicrobiotaProcess package | 3. MicrobiotaProcess profiling | 3.1 bridges other tools | 3.2 alpha diversity analysis | 3.3 calculate alpha index and visualization | 3.4 The visualization of taxonomy abundance | 3.5 Beta diversity analysis | 3.5.1 The distance between samples or groups | 3.5.2 The PCoA analysis | 3.5.3 Hierarchical cluster analysis | 3.6 Biomarker discovery | 4. Need helps? | 5. Session information | 6. References

Introduction | MaxQuant File Overview | Workflow | Base R | Tidyverse | Session Info

Description | Barcode Summary | Groups IDs | Read distribution | Filtered Reads | Edit rates | Frameshift

Description | Group Summary | Read distribution | Filtered reads | Edit rates | Frameshift | Alignments plots

Description | guideRNA Summary | Read distribution | Filtered Reads | Edit rates | Frameshift | Alignments plots

Introduction | Running StatescopeR | Installation | Prepare scRNAseq data | Prepare StatescopeR input | Run StatescopeR | Evaluate Statescope | Downstream analysis/visualizations | Group expectations | Citing StatescopeR | Reproducibility

Introduction | Overview of PhILR Analysis | Loading and Preprocessing Dataset | Data preparation: TreeSE | Filter Extremely Low-Abundance OTUs | Process Phylogenetic Tree | Investigate Dataset Components | Transform Data using PhILR | Ordination in PhILR Space | Visualization with TreeSE | Identify Balances that Distinguish Human/Non-Human | Name Balances | Visualize Results | Use Balances for Dimension Reduction | Package versions | References

Introduction | Installation | Load Data and Perform Differential Expression Analysis | Using scToppR with Differential Expression Results | Visualizing ToppGene Results | Saving ToppGene Results

Introduction | Installation | Load Data | Plotting | Saving

Introduction | Installation | Load Data | Running scToppR with Differential Expression Results | Visualizing ToppGene Results | Saving ToppGene Results

Overview | Basics | Installing recount3 | Required knowledge | Asking for help | Citing r Biocpkg('recount3') | Quick start | Users guide | Available data | Terminology | Available annotations | Build a RSE | Explore the RSE | Sample metadata | Counts | Exon | Exon-exon junctions | BigWig files | Local files | Your own mirror | Teams involved | Project history | Other related tools | Reproducibility | Bibliography

Introduction | Configuration file | Default options | Files created during analysis | barcode_reads_filters.csv | config_summary.csv | RunParameters.txt | "alignments" folder | reports folder | Detailed analysis | Aligning reads | Normalization | Making reports | Plotting alignments events

Installation | Citation | Introduction | Disease Ontology Enrichment Analysis | Data Preparation | doEnrich Function | Weighted Enrichment Function | Classic Enrichment Function | Result description and Written | Enrichment description | Result Display | Result writing | Visualization of enrichment results | drawBarGraph function | drawPointGraph function | drawGraphViz function | drawHeatmap function | convenient drawing | Session information

Introduction | Installation | Quick start | Enhanced format support | Comma-separated coordinates | Space-delimited coordinates | Complex chromosome names | Automatic object type detection | Single positions vs. ranges | Genomic interactions | IRanges for non-genomic coordinates | Forcing object types | Working with vectors | Explicit conversion functions | Class detection without parsing | Session Information

Introduction | Installation | Prerequisites | System Dependencies | R Package Installation | A Complete Analysis Workflow | 1. Loading the Package and Example Data | 2. Running the Analysis | Basic Workflow | Advanced Workflow: Customizing the Analysis | 3. Exploring the Output | A First Look at the Results Table | Understanding the Fragment Status | Exploring Other Key Features | Visualizing Fragmentomic Features | Fragment Size Distribution | 1. Comparing Groups with a Histogram | 2. Using Density Curves | 3. Visualizing the Overall Distribution | Detailed 3-Base End Motif Proportions | 1. Hierarchical View (representation = "split_by_base") | 2. Differential Analysis (representation = "differential") | 3. Side-by-side Comparison (representation = "split_by_motif") | End Motif Sequence Logos | 1. Comparing Motifs Between Groups | 2. Visualizing Both Ends of a Single Group | Overall Nucleotide Frequency | Session Information

Introduction | Function overview | Prerequisites and Setup | Java version | Package options | Custom JAR setup | Quick Start | Core Function: fastFindKNN() | Output structure | Algorithm choice: ANN vs. exact | Distance metrics | Euclidean (default) | Cosine | Dot-product (ANN only) | Skipping distance computation | HNSW-DiskANN Tuning | Parameter reference | High-recall configuration | Product Quantization (pq.subspaces) | Thread management | Graph Construction | Shared Nearest-Neighbor graph | K-Nearest Neighbor graph | Community detection | Full Single-Cell Workflow | Using a real SingleCellExperiment | Comparing with BiocNeighbors | Drop-in BiocNeighbors integration | Timing comparison | Troubleshooting | Common errors | Debugging with verbose mode | Checking the Java runtime | Using a safe wrapper | Session Information

Abstract | Introduction and Motivation | Relation to existing Bioconductor packages | Installation | A small runnable example | Step 1: prepare_data() | Step 2: rank_genes() | Step 3: downstream utilities | Top genes | Signed importance (directionality) | Basic plots | Session information

Introduction | Installation | Quick Start with SummarizedExperiment | Load the package | Create a minimal object | Check passport on unstamped object | Log processing steps | Read the processing log | Stamping with interactive popup (Seurat workflow) | Logging Processing Steps | Custom Fields | Passport Fields Reference | Identity | Animal Info | Experiment Info | Lineage | Session Info

Introduction | Overview | Highly multiplexed imaging | Imaging mass cytometry | Data types | Segmentation and feature extraction | Example data | For spillover correction | Raw data in form of .txt files | ImcSegmentationPipeline output data | steinbock output data | Read in IMC data | Read in CellProfiler output | Read in steinbock output | Read raw .txt files into Image objects | Spillover correction | Read in the single-spot acquisitions | From txt | From tiff | Quality control on single-spot acquisitions | Consecutive pixel binning | Pixel filtering | Estimating the spillover matrix | Spatial analysis | Constructing graphs | Graph/cell visualization | Neighborhood aggregation | Spatial context analysis | Community detection | Border cells | Patch detection | Distance statistics to cells of interest | Neighborhood permutation testing | Summarizing interactions | Testing for significance | Visualizing interactions | Visualizing interactions when choosing method = "conditional" | Contributions | Session info | References

Introduction | Installation | Quick start: unpaired design | Step 1: Filter sparse donors | Step 2: Inspect pseudo-bulk profiles | Step 3: Run differential expression | Step 4: Visualise results | The donor weighting advantage | Paired design support | Simulated paired design | Pseudo-bulk by donor × condition | Running fastDE on paired data | Why paired design matters | Session information

Overview | A Mental Model | Which Function Should You Use? | The Example Data | Core Overlap Operations | Return all hit pairs | Count matches per query | Ask only whether a match exists | Important Arguments | type | max_gap | min_overlap | ignore_strand | threads and deterministic | Bioconductor Compatibility | What is currently supported? | What is not currently supported? | Empty ranges | Direct vs indexed usage | deterministic | Return Types You Will See Most Often | Reusing the Same Subject with an Index | Overlap Joins | Nearest and Directional Queries | Grouped Overlap Summaries | Count overlaps by group | Aggregate a subject score over overlaps | Self-Overlaps, Clusters, and Sliding Windows | Range Algebra | Coverage and Binned Coverage | Iterating over Large Query Sets | Saving and Loading an Index | A Small Benchmark Example | Benchmark Resources | Package Example Files | Session Info

Introduction | Alternative software for data quality assessment | Installation | Questions and bugs | Create Spectra, MsExperiment, and Chromatograms objects | Create Spectra and MsExperiment objects from mzML files | Chromatograms example | Create Spectra and MsExperiment objects from (feature-extracted) intensity tables | Calculating the quality metrics on Spectra, MsExperiment, and Chromatograms objects | List of included metrics | Metrics shared between Spectra and Chromatograms | Chromatograms-specific metrics | Obtain the metrics in data.frame-format | Obtain the metrics in mzQC-format | Remove empty spectra prior to the calculation | Visualizing the results | Appendix | Session information | References

Introduction | Feature Description | Installation | Functions | Methods | Model | The DeMixT algorithm for deconvolution | Examples | Simulated two-component data | Simulated three-component data | Real data: PRAD in TCGA dataset | Obtain raw read counts for the tumor and normal RNAseq data | Data preprocessing | Deconvolution using DeMixT | Deconvolution using normal reference samples from GTEx | Reference | Session Info

Introduction | Feature Description | Functions | Methods | Model | The DeMixNB Algorithm for Deconvolution | Real data Example | Spatially Resolved Transcriptomics | Results | Session Information

Background | Setup: load required packages | Load data | Model dynamics | Extract estimates and visualize results | Clustering dynamics | Over-Representation Analysis (ORA) | Compare dynamics clusters | Dynamics | Metabolites | Combine both

Introduction | Installation | Dependencies | Basic Usage | Loading Required Packages | Input Data Formats | Quick Example | Understanding the Output | Visualization | Output Files | Session Information

Introduction | Installation and additional packages | Initialization | Statistics and data preprocessing | Estimation of cluster number | Run final clustering

Introduction | Basic use | Affymetrix data - before preprocessing | Affymetrix data - after preprocessing | ExpressionSet and ExpressionSetIllumina | Two colour arrays, NChannelSet, RGList, MAList | Loading data from ArrayExpress | Making the report more informative by adding a factor of interest | Extended use | Spatial layout of the array | Mapping of the reporters | RNA quality | Session Info | References

Introduction | Data preparation | Module generating functions | Outlier detection | Rendering the report | Session Info

Introduction | Installation | Getting started | Loading the data | Visualising the raw data | Spatial Inference | Spatial Statistics Curves | Functional Boxplot | Functional GAMM | Model evaluation

Overview | Cache directory | Data structures | Cache records | Versions | The cache database (cache.json) | Core operations | Saving and loading | Searching | Removing entries | Cleanup | Cache key generation | Locking | Session info

Overview | Installation | Option 1: BiocManager | Option 2: devtools (GitHub) | External tools and acronyms | Workflow map | Load Reference Resources | Build Sample Manifest | Quick start wrapper (get_splicing_impact) | Read Splicing Events | Sample-Level QC and Exploration | Compare HITindex Between Conditions | PSI Distribution Overview | Differential Inclusion (DI) | Match Events to Sequences and Build Transcript Pairs | Sequence-Level Consequences | Domain-Level Changes and Domain Enrichment | PPI Rewiring | Gene Enrichment Foreground Probes | Visualize Transcript and Protein Consequences | Inspect a Single Event | Integrative Visualization | Output Description (hits_final, data, res) | hits_final (integrated event-level output) | data (raw sample-level input table) | res (differential inclusion output) | S4 Applications and Accessors | S4-first Main Workflow (Code Only) | Custom Input Entry Points | Add Manual Protein Features | Pre-DI Custom Table | Post-DI Custom Table | Post-DI rMATS Import | Transcript-Pair Entry Point | Session Info

BiocPkgDash | Introduction | Comparison to Bioconductor Build Results | Installation | Loading | Dashboard display | Filtering packages | GitHub topic filter | Badges | Downloading the badge wall | Status | Dependencies | Metadata | Session Information

1. Loading the example data | 2. Pre-processing | PCA plot and Heatmap | 3. Differential Metabolite Analysis | Volcano Plots | 3. Enrichment Analysis and Prior knowledge | Match IDs with PK | Run ORA | Volcano plot | 4. Metabolite Clustering Analysis | MCA-2Cond | MCA-CoRe | ORA on each metabolite cluster | Viz | Session information | Bibliography

Installation | Setup | Introduction | Structure reconstruction | Reconstruction of structures for one image | Reconstruction of structures for all images | Intersection with cells | Structure level metrics | Proportion of cell types within structures | Shape Metrics | Cell level metrics | Distance to structure border | Structure boundary vs FOV boundary | Session Info | References

Introduction | Installation | The Unimod database | Different types of PTM annotations | Add modifications to a sequence | Session information

Simple, flexible and reusable tab-delimited genome annotations | Supported organisms | Using the local database | Installation of sitadela | Setup the database | Use the database | Add a custom annotation | A complete build | Annotations on-the-fly | Session Info

Introduction | Installation | Getting started | Loading the data | Visualising the raw data | Calculating Spatial Statistics Metrics | Correlation | Spacing | Functional boxplot | Functional principal component analysis | Functional additive mixed models | Model evaluation

Introduction | Overview of the workflow | mRNA-seq | Total RNA-seq | Recommended analysis pipeline | Installation | Data Processing | Base-level coverage | Processed matrices | Data Analysis | Toy datasets | mRNA-seq example | Total RNA-seq example | Interoperability with Bioconductor | Summary | Session Information

Simple, flexible and reusable annotation for metaseqR2 pipeline | Supported organisms | Using the local database | Installation of metaseqR2 | Setup the database | Use the database | Add a custom annotation | A complete build | Annotations on-the-fly | Session Info

RNA-Seq data analysis using mulitple statistical algorithms with metaseqR2 | Getting started | Installation | Introduction | Types of analyses performed with metaseqR2 | Data filtering | Running the metaseqr2 pipeline | Analysis at the gene level with gene counts | Analysis at the gene level with exon counts | Estimating p-value weights | Combining p-values from multiple tests | metaseqR2 components | Brief description | Backwards compatibility | The report | Summary | Quality control | Normalization | Statistics | Results | References | Genome browser tracks | List of required packages | Session Info

Introduction | Installation | Available data | TripleTOF | sciex | PXD000001 | CPTAC | FAAH KO | DIA-NN software outputs | DIA-NN single-cell proteomics reports | Proteomics contaminant databases | FTICR-MS direct injection MS data | MRM data file | CE-MS data | TMT MS3 SPS data | Adding data to MsDataHub | Session information

Introduction | Installation | Preliminaries | Setup | Load data and cell ontology | Build the ontology | Preprocess data | Fit a classification model | Method 1: standard conformal inference | Algorithm | Obtain prediction matrices | Obtain conformal prediction sets | Method 2: exploit the cell ontology | Obtain hierarchical prediction sets | Visualization | R.session Info

Preamble | Introduction | Setup | Retrieving the data-set | Create the COTAN object | Alternative ways to create a COTAN object | Assign to each cell its origin | Data cleaning | Plots from the raw counts | Dropping problematic cells | Check all good | Vignette clean-up stage

Preamble | Introduction | Setup | Retrieving the data-set | Create the COTAN object | Drop low quality cells | COTAN analysis | Differential Expression Analysis | Existing clusterizations | How to manipulate a clusterization | Clusters dendogram | Relevance of given marker genes | Find differentially expressed genes | Relevance of marker genes for the merge clusters | DEA cluster vs. cluster | Vignette clean-up stage

Preamble | Introduction | Setup | Retrieving the data-set | Create the COTAN object | Drop low quality cells | Establish Uniform Transcript cells' clusters | COTAN analysis | Uniform Clustering | First UT clusterization | Extract split clusters' statistics | Relevance of marker genes for the split clusters | UMAP of the split clusterization | Merged UT clusterization | Extract merge clusters' statistics | Relevance of marker genes for the merge clusters | UMAP of the merge clusterization | Vignette clean-up stage

Introduction: | Installing Cicero | Constructing cis-regulatory networks | Running Cicero | The CellDataSet class | Create a Cicero CDS | Run Cicero | Visualizing Cicero Connections | Comparing Cicero connections to other datasets | Finding Cis-Coaccessibility Networks (CCANS) | Cicero gene activity scores | Single-cell accessibility trajectories | Constructing trajectories with accessibility data | Aggregation: the primary method for addressing sparsity | aggregate_nearby_peaks | Choose sites for dimensionality reduction | Choosing sites that define progress | Choose sites by differential analysis | Choose sites by dpFeature | Reduce the dimensionality of the data and order cells | Differential Accessibility Analysis | Visualizing accessibility across pseudotime | Running differentialGeneTest with single-cell chromatin accessibility data | aggregate_by_cell_bin | References | Citation | Session Info

Introduction | Generate a library entry | Session Info

Introduction | Installation | Example session | Feature table and data | Annotations | Save the annotations | Session Info

Introduction | Package download and installation. Package dependencies. | Dataset description and download. | Platforms supported by the library and preprocessing. | Survival and risk assessment (case study on Breast Cancer). | Analysis of individual genes as survival markers: geneSurv() | Discovery of genes associated with phenotypic factors: genePheno() | Identification of gene survival markers & patient risk prediction: patientRisk() | Risk Prediction for an independent set of patients: predict_PatientRisk() | Survival estimation from the COX regression for a test patient: predict_SurvCurve() | Brief comparison of ASURI and other related R tools | Session Info | References

Introduction | Downloading a transcriptome | Obtaining transcript to gene information | From Ensembl | From FASTA file | From GTF and GFF3 files | From TxDb | From EnsDb | Deprecation

Introduction | Benchmarking | Reading data | Data size | Accessing spectra | MS2 quantitation | Notable differences on-disk and in-memory implementations | MS levels | Serialisation | Data processing | Validity | Conclusions

Introduction | Load required library | Example dataset | Input data | ORF annotation | Prepare condition table | Run DOTSeq | Inspect results | Visualisation | Venn diagram: | Composite scatter plot: | Volcano plot: | Heatmap: | ORF usage plot: | Summary | References | Prepare annotations | Simulation and benchmarking | Session info

Background | Installation and Loading | Accessing Bioconductor Build Report Data | Getting All Available Tables | Getting Individual Tables | Remote Read vs Local Download | Package-Specific Queries | Package Release Information | Package Build Results | Package Error Counts | Package Failures Over Time | Exploratory Data Analysis | Package Growth Over Time | Build Status Distribution | Platform-Specific Analysis | Build Stage Analysis | Most Problematic Packages | Maintainer Analysis | Temporal Analysis | Bioconductor Report Overview | Get Bioconductor Build Report | Get List of Failing Packages | Conclusion

Installation | Citation | Input Data | Copy Number Variation (CNV) File | Single Nucleotide Variant (SNV) File | Package Output | Datatables (.txt) | Graphs (.pdf) | Structure | LACHESIS Main Workflow | Importing Data | Importing Copy Number Information (readCNV) | Handling Missing Allele Information | Input | Output | Example using output from ACESeq as copy number information | Example using output from ASCAT as copy number information | Example using output from PURPLE as copy number information | Importing Variant Information (readVCF) | Example: SNV calls obtained with Mutect | Example: SNV calls obtained with Strelka | Example: SNV calls obtained with the dkfz SNV calling workflow | Plotting Variant Allele Frequencies (plotVAFdistr) | Example | Assigning copy number states to single nucleotide variants (nbImport) | Assignment of mutational signatures | Example using all variants from vcf file | Example using variants associated with specific SBS mutational signatures from vcf file | Plotting Imported Data (plotNB) | Example using variants assosciated with specific SBS mutational signatures from vcf file | Processing Clonal Mutations | Counting Clonal Mutations (ClonalMutationalCounter) | Normalizing Clonal Mutation Counts (normalizeCounts) | Inferring Clonal Evolution | Estimating Mutation Densities at ECA/MRCA (MRCA) | Input Parameters | Plotting Mutation Densities (plotMutationalDensities) | Estimating Clonality per SNV (estimateClonality) | Plotting Clonality per SNV (plotClonality) | LACHESIS Wrapper Function | Example with vectors | Example with tab-delimited sample-specification file | Example with multiple sample and data frame input | Plotting Cohort Analysis (plotLACHESIS) | Plotting Clinical Correlations (plotClinicalCorrelations) | Plotting Disease Trajectories (plotDiseaseTrajectories) | Plotting Survival (plotSurvival) | Classifying Tumor Evolution (classifyLACHESIS) | Quality control/ Filtering | Hypermutants | Ploidy and purity | VAF distribution | Positive Example | Negative Example | Warning Messages | readCNV | nbImport | MRCA | LACHESIS | plotLachesis | plotLachesis, plotClinicalCorrelations, plotSurvival | plotSurvival | How To Get Help | Session Info

Introduction | Installation | Example data description | Construction of spectral unmixing matrix | Spectral unmixing | Transformation | Investigation of possible unmixing artifacts | Correction of artifacts | Connecting to other non-flowCore compliant applications | Summary | Session information

Introduction | Installation | Launching the app | Running tripr as a shiny application | Home | Preprocessing | Preselection | Selection | Pipelines & Step dependencies | Clonotype computation | Highly similar clonotypes computation | Repertoires extraction | Highly Similar Repertoires extraction | Multiple value comparison | CDR3 with 1 amino acid length difference | Logo | Insert identity groups | Somatic hypermutation status | Alignment | Somatic hypermutations | Visualization | Overview | Running tripr via R command line | Usage | Output of Command Line tool | Example with run_TRIP() | Tool dependencies | Citation | Session info | Bibliography

Quick overview | About Ranges | Example: finding GWAS hits that overlap known exons | Session information

Ranges revisited | Constructing Ranges | Arithmetic on Ranges | Grouping Ranges | Restricting Ranges | Summarising Ranges | Joins, or another way at looking at overlaps between Ranges | Finding your neighbours | Example: dealing with multi-mapping | Grouping by overlaps | Data Import/Output | Learning more | Session information

Preamble | Introduction | Setup | Retrieving the data-set | Create the COTAN object | Drop low quality cells | Establish genes' clusters | COTAN analysis | Get data tables and stored COEX | Analysis of the elaborated data: GDI | Define genes lists | GDI Plot | Store the calibrated object for future analysis | Heatmaps | Establishing genes' clusters | UMAP plot | Vignette clean-up stage

Introduction | Installation | Basic workflow | Retrieve data and signatures | Preprocessing and imputation | Score and plot data

Introduction | Examples | Contributions | Session information | References

Introduction | Installation | How to run | Read the raw count values | Getting gene chromosomal location | Example code to get gene location information like above | Integrating gene expression and location into a single object. | Final results | Plot expression pattern of a neighbourhood of a gene | Dynamic neighbour | SessionInfo

Load libraries needed | Example data | Figure 2

Introduction | Data Preparation | Create mock GTF and Test Sets | Initial Filtering and Feature Extraction | Coding Potential Analysis | Aggregation and Venn Visualization | Performance Evaluation | Performance Visualization with calculateCM | Radar and Clock Plots | Functional Analysis and Interactions | Cis and Trans Interactions | Annotation and Sankey Diagram | Session Information

Overview | Input Data | Building the Subnetwork | Step 1 — Annotate proteins with INDRA metadata | Step 2 — Retrieve the interaction subnetwork | Filtering by Context: Tag Count | Defining the Query | Filtered nodes | Filtered edges | Evidence with scores | Defining a cutoff | Filtering by Context: Cosine score | Choosing a score Cutoff | Session Info

Introduction | Workflow | Getting started | Setting up a postNet analysis | Initializing a postNetData object using custom gene lists | Initializing a postNetData object using an Anota2seqDataSet object | Reference sequence annotations | Loading in-built reference sequence annotations | Retrieving or constructing reference sequence annotations from the NCBI RefSeq database | Using custom reference sequence annotations | Selecting transcript isoforms | Adjusting UTR sequence annotations | Analysis of mRNA sequence features | Statistical comparisons, selecting sequence sub-regions, and plotting options | Length of sequence regions | Nucleotide content | Folding energy | Upstream open reading frames | Motif discovery using STREME | Motif enumeration | Codon and amino acid usage | Generating a codon and amino acid count and frequency table | Codon usage indexes | Selecting enriched or depleted codons or amino acids | Enumerating selected codons or amino acids | Modelling and network analysis of post-transcriptional regulation | Selecting features for modelling | Cis-acting features | Trans-acting features | Creating custom features | Highly correlated variables and general features | Creating the feature list | Feature integration with forward stepwise linear regression | Phase 1: Univariate models | Phase 2: Stepwise regression model (omnibus) | Phase 3: Adjusted model | Feature correlations | Running feature integration with forward stepwise linear regression | Network visualizations | Feature integration with Random Forest classification | Pre-modelling and feature selection | Final modelling | Running feature integration with Random Forest classification | Individual feature visualizations | Predicting post-transcriptional regulation in new datasets | Visualizing relationships between features and regulatory effects with UMAP | Plotting UMAPs | Functional enrichment and threshold-independent signature analyses with postNet | Slope filtering when using an Anota2seqDataSet object | Performing GSEA with a postNetData object | GAGE analysis with a postNetData object | microRNA target enrichment analysis with a postNetData object | GO term analysis with a postNetData object | Analysis of gene signatures using eCDFs | Assessing gene signature regulation using the gene list workflow | Assessing gene signature regulation from the anota2seq object workflow | Generating gene signature regulation heatmaps | Citing postNet and implemented tools | Session info | References

Introduction | Prerequisites | Refining gene signatures: I | Refining gene signatures: II | Addressing noisy gene sets | Operating with gene sets that characterize multiple subpopulations | Visualizing gene participation in top overlaps | Session information

Introduction | Batch effects analysis in large-scale data | Analysis of large-scale data: steps before and after batch correction | Preparation for the analysis | IInstallation | Preparing the data for analysis | Loading the libraries | Input data formats | Example dataset | Preparing sample and peptide annotations | Defining the order of samples from running date and time | Generating peptide annotation from OpenSWATH data | Other utility functions | Transforming the data to long or wide format | Transforming the data to log scale | Defining the color scheme | Step-by-step workflow | Initial assessment of the raw data matrix | Plotting the sample mean | Plotting boxplots | Normalization | Median normalization | Quantile normalization | Diagnostics of batch effects in normalized data | Hierarchical clustering | Principal component analysis (PCA) | Principal variance component analysis (PVCA) | Peptide-level diagnostics and spike-ins | Correction of batch effects | Continuous drift correction | Discrete batch correction: combat or peptide-level median centering | Feature-level median centering | ComBat | Correct batch effects: universal function | Quality control on batch-corrected data matrix | Heatmap of selected replicate samples | Correlation distribution of samples | Correlation of peptide distributions within and between proteins | SessionInfo | Citation | References

Overview | Setup | Installation | Loading required packages | Loading and preparing the example dataset | Constructing a ProBatchFeatures object | Processing pipeline with diagnostics | Step 1 - filtering features with too many missing values | Step 2 - log2-transformation | Step 3 - median normalization | Step 4 - batch-effect correction | Step 5 - assess processed assays | Principal component analysis | Hierarchical clustering | Principal Variance Component Analysis (PVCA) | Inspecting operation log | Extracting data matrices from pbf object | Session info | Citation | References

Introduction | Import .msp library | Session Info

Introduction | Why do we need a new class? | Anatomy of DeeDeeExperiment | Creating a DeeDeeExperiment object | Getting started | DeeDeeExperiment on the macrophage dataset | DEA: DESeq2 framework | DEA: limma framework | DEA: edgeR framework | Creating DeeDeeExperiment the DeeDee way | Adding results to a dde object | Adding DEA results | Adding multi-contrast DEA results | Adding FEA results | Linking DE and FE Analysis in a dde object | Adding contextual information | Summary of a dde object | Renaming results in a dde object | Removing results in a dde object | Accessing results stored in a dde object | Downstream operations with DeeDeeExperiment objects | FAQs | Session info | References

Load packages | Introduction | Setup | Aggregation | Analysis | Handling and visualizing results | Stagewise anaysis | Comparison | Session info | References

Introduction | Support | Contents | 1. Installing R | Installing R for the first time | (Optional) the RStudio IDE | Important: the correct R version | 2. Installing MaAsLin 3 | 3. Microbiome association detection with MaAsLin 3 | 3.1 MaAsLin 3 input | 3.2 Running MaAsLin 3 | Running MaAsLin 3 on HMP2 data | Median comparisons | 3.3 MaAsLin 3 output | Significant associations | Full output file structure | Diagnostics | Replotting | 4. Advanced Topics | 4.1 Absolute abundance | Spike-in | Total abundance scaling | Computationally estimated absolute abundance | 4.2 Random effects (clustered samples) | Alternatives to random effects | 4.3 Interactions (differences in differences) | 4.4 Level contrasts | 4.5 Group-wise differences | 4.6 Contrast tests | 5. Command line #### | Tool comparison

Introduction | Installation | Prerequisites | Loading and preparing data | Extracting gene information | Representation analysis | Computing centers of mass of gene expression | Computing silhouette and normalized silhouette | Session information

Introduction | Requirements | Pipeline | Running fourSynergy | Installation | Getting started | fourSynergy Pipeline | Input | config | Output | R analysis | Load data | Base tool results | Ensemble results | Differential interaction analysis

Introduction | Installation | Data description | Assessing orthogroups | Protein domain-based orthogroup assessment | Reference-based orthogroup assessment | Visualizing summary statistics | Species tree | Species-specific duplications | Genes in orthogroups | Species-specific orthogroups | All in one | Orthogroup overlap | Orthogroup size per species | Session information | References

Introduction | New Functions in GBScleanR Version 2 | Prerequisites | Installation | Error while Handling a GDS File | Loading Data | Utility Methods | Getters | Data Summarization | Collect Basic Summary Statistics | Visualize Basic Summary Statistics | Getter Methods for Summary Statistics | Filtering and Subsetting | Filtering Data | Important Instruction for Filtering | Reset Filtering | Error Correction | Set Replicates | Set Parents | Data QC and Filtering | Genotype Estimation | Prepare Scheme Information | Update on April 4, 2023 | Update on March 4, 2026 | Execute Genotype Estimation | Get the Results | Plot Read Ratio and Dosage | Closing the Connection | Session Info

Introduction | Overview | Pre-requisites | Participation | R / Bioconductor packages used | Time outline | Workshop goals and objectives | Learning goals | Learning objectives | Workshop | Data from OmniPath | Networks | Igraph integration | Enzyme-substrate relationships | Protein complexes | Annotations | Combining networks with annotations | Intercellular communication roles | Metadata | Data from other resources | General purpose functionalities | Identifier translation | Gene Ontology | Useful tips | Further information | Session info | References

Introduction | What are Marker Genes? | How can we select marker genes? | The Mean Ratio Method | Goals of this Vignette | 1. Install and load required packages | Install DeconvoBuddies | Load Other Packages | 2. Download DLPFC snRNA-seq data. | 3. Find MeanRatio marker genes | 4. Find 1vALL marker genes | Run 1vALL DE to find markers for each cell type - takes 5min+ | marker_stats_1vAll <- findMarkers_1vAll( | sce = sce, # sce is the SingleCellExperiment with our data | assay_name = "counts", | cellType_col = "cellType_broad_hc", # column in colData with cell type info | mod = "~BrNum" # Control for donor stored in "BrNum" with mod | raw_logFC = TRUE # also retain raw logFC in addition to standardized | ) | load 1vAll data to save time, data is equivalent to the above code | 5. Compare Marker Gene Selection | Hockey Stick Plots | 6. Visualize Marker Genes Expression | Summary | Reproducibility | Bibliography

imageFeatureTCGA | Overview | Installation | Data structure and technical details | HoVerNet outputs | ProvGigaPath embeddings | Slide-level embeddings | Tile-level embeddings | Available Data | Formats | HoVerNet data | ProvGigaPath data | Importing HoVerNet data | Importing ProvGigaPath embeddings | Importing multiple ProvGigaPath files | Mixed level imports | See also | Shiny App: imageTCGA | Session Info

Abstract | Introduction and Motivation | Why Bioconductor? | Related Bioconductor Packages and Comparison | Using ExpoRiskR with SummarizedExperiment Objects | Creating and Aligning SummarizedExperiment Inputs | Preprocessing SummarizedExperiment-based Data | Network construction and visualization | Exposure perturbation ranking | Exposure feature importance | Risk ROC curve | Integration with Bioconductor Workflows | Summary | Session information

Introduction | What is it for? | Starting point | Interfaces between clusters | Intra-cluster layers | Inter/Intra-cluster trajectories | Spatial neighborhood | Ending point | Session Information

Installation | Standard imputation with Pirat | Intra-PG correlation analysis | Pirat extensions | -2, the 2-peptide rule | -S, samples-wise correlations | -T, transcriptomic integration | 5. Session info | References

Introduction | Scope of package | OWL interface | Methods | Conceptual overview of ontology with cell types | Illustration in a single-cell RNA-seq dataset | Labeling PBMC in the Seurat tutorial | Relationships asserted in the Cell Ontology | Subsetting SingleR resources using ontological mapping | A data.frame mapping from informal to formal terms | Binding formal tags to the HPCA data | Subsetting using the class hierarchy of Cell Ontology | Visually identifying differences between ontology versions for a set of given terms | Disease concept relationships | Related tools | Illustration with a phenotype ontology | References

Introduction | Illustration with Human Phenotype ontology

Introduction | Installation | Examples | Small Datasets | Large Datasets | Benchmarks | Test Datasets | R Codes in the Benchmark | Memory Usage and Elapsed Time | Miscellaneous | Save SCArrayAssay | Multicore or Multi-process Implementation | Downgrade SCArrayAssay | Conversion from Seurat to SingleCellExperiment | List of supported functions | Debugging | Session Information

Introduction | Prerequisites | Basic Integration with scanpy | Conversion to R objects | Multi-modal data with mudata | Session info | R | Python

Preamble | Package aim | Method summary | Installation | Integrated usage | Detailed usage | Loading data | Binning data | Correlation of the bins | Choosing a threshold | Working with SingleCellExperiment objects | Parameter values | max_zeros | top_window_size | other_window_size | n_random | threshold

Introduction | Getting started | Case Studies | Tutorials | Session Info

Introduction | What is MIRit | How to cite MIRit | Installation | Data preparation | Load example data | Paired vs unpaired data | Set up expression matrices | Define sample metadata | Create a MirnaExperiment object | Differential expression analysis | Visualize expression variability | Perform miRNA and gene differential expression | Available methods for RNA-Seq and microarrays | Model design | The performMirnaDE() and performGeneDE() functions | Advanced parameters | Add differential expression results from other technologies | Visualize differentially expressed features | Access differential expression tables | Create a volcano plot for miRNAs and genes | Produce differential expression bar plots | Functional enrichment analysis | Available approaches: ORA, GSEA and CAMERA | Available databases and categories | Supported species | Perform functional enrichment with the enrichGenes() function | Visualize enriched sets | Access results table | Enrichment dot plots and bar plots | Other plots for GSEA | Retrieve miRNA targets | Databases with miRNA-mRNA interactions | The mirDIP approach | Download predicted and validated interactions with getTargets() | Assess the effects of miRNAs on target genes | Correlation analysis for paired data | Statistical correlation coefficients | Perform a correlation analysis in MIRit | Account for the group effect in correlation analysis | Explore the succesfully integrated miRNA-target pairs | Visualize the correlation between miRNAs and genes | Association tests for unpaired data | Fisher's exact test | Boschloo's exact test | Perform one-sideded association tests in MIRit | Rotation gene-set tests for unpaired data | Session info | References

Overview | The bedMethyl format | RBedMethyl as a plug-and-play connector | Ingest a bedMethyl file | Subsetting example | Region-level summaries | Coercion to downstream classes | Session info

Quick start | Introduction | Overview of the GSVA functionality | Gene set definitions and containers | Importing gene sets from GMT files | Quantification of pathway activity in bulk microarray and RNA-seq data | Example applications | Molecular signature identification | Differential expression at pathway level | Data exploration at gene level | Running GSVA | Data exploration at pathway level | Interactive web app | Contributing | Session information | References

Overview | Quick Start | Package and data requirements | Building a dendrogram example | Converting the hclust object into a tree-and-leaf object | Setting graph attributes | Plotting a tree-and-leaf diagram | Setting the initial tree-and-leaf state with ggtree layouts | Building and plotting a phylo tree with ggtree layouts | Applying tree-and-leaf transformation to ggtree layouts | Case Study 1: visualizing a large dendrogram | Context | Building and plotting a large tree-and-leaf diagram | Case Study 2: visualizing a non-binary tree | Building and plotting a tree-and-leaf for a non-binary tree | Citation | Session information

Introduction | Installation | Using GRanges for structural variants: a breakend-centric data structure | Workflow | Loading data from VCF | Identifying Retrotransposed Transcripts | Visualising breakpoint pairs via circos plots | SessionInfo

Introduction | Reading subsets of data | Using the index argument | Using hyperslab selections | Irregular selections | Using hyperslab selection tools | Slowdown when selecting unions of hyperslabs | Summary | Writing in parallel | Example data | Serial writing of datasets | Parallel writing of datasets | Session info

Data Annotation Process for gDR Pipeline | Introduction | Annotation Files | Annotation File Locations | Annotation Requirements | Drug Annotation Requirements | Cell Line Annotation Requirements | Annotating SummarizedExperiment and MultiAssayExperiment Objects | Additional Information for Genentech/Roche Users | Conclusion | SessionInfo

Introduction | Enabling dplyr verbs | Typical use case | Related work | A Note on tidySummarizedExperiment | plyxp grammar | assay context | rows context | cols context | Multiple expressions enabled via plyxp | Advanced features | Object integrity | group_by() | Printing | renaming rows or columns | Troubleshooting and best practices | Community and support | Session info | References

Basics | Install DFplyr | Background | Quick start to using DFplyr | Citing DFplyr | Session Information.

Introduction | Principle | Installing BLASE | Setting up the Single Cell Experiment | Finding the most descriptive genes with tradeSeq | Parameter Tuning for BLASE | Inspect Bin Choice | Inspect Genes Choice | Mapping Bulk Samples to SC with BLASE | Plotting Heatmap of Mappings | Plotting Detailed Correlation Maps | Plotting Summary Plots of Mappings | Session Info

Load Data | Prepare BLASE | Create BLASE data object | Select Genes | Calculate Mappings | Transfer Mappings | Session Info

Load Data | Prepare BLASE | Create BLASE data object | Select Genes | Calculate Mappings | Calculate DE genes | Phenotype + Development (Bulk DE) | Development (SC DE) | Remove Developmental Genes | Session Info

Introduction | Installation | Importing data | modBAM data | Tabular data (Modkit, Megalodon, Nanopolish, f5c) | Modkit | Megalodon | Importing to tabix format | Quick start | Constructing the NanoMethResult object | Basic Plotting | Exporting data | bsseq and DSS | edgeR | Importing Annotations | Dimensionality reduction | Preparing data for dimensionality reduction | Package options | Site filtering | Region annotation colours

Why per-person enrichment? | How it works | Why z-scoring matters | When to use ssTaxSEA vs TaxSEA | Usage | From a count matrix | From a TreeSummarizedExperiment | With custom taxon sets | Interpreting the output | Practical considerations

Installation | Usage | Cluster-based approach | Multiple samples | Description of the method | Splitting captures | Reducing and clustering the data | Generating artificial doublets | Examining the k-nearest neighbors (kNN) of each cell | Training a classifier | Thresholding | Doublet origins and enrichments | Some important parameters | Expected proportion of doublets | Number of artificial doublets | Frequently-asked questions | I'm getting way too many doublets called - what's going on? | Should I use the cluster-based doublet generation or not? | The clusters don't make any sense! | 'Size factors should be positive' error | Identifying homotypic doublets | What is a sample exactly? Usage with barcoded and 10X Flex data. | How can I make this reproducible? | Can I use this in combination with Seurat or other tools? | How can I call scDblFinder from the command line? | Can this be used with scATACseq data? | Should I run QC cell filtering before or after doublet detection? | What about ambiant RNA decontamination? | Can I combine this method with others? | Session information

Introduction | Data loading | SpatialExperiment object creation | Load polygons | Quality Control | Compute QC metrics | Compute Quality Score (QS) | Single metric flagging | Visualization | Session Information

Introduction | What is ECS? | Installation | Data import | General Usage | Examples | VCF | Tabular | Region Metadata | Custom Column Names | Output | Variant Filtering | Germline Variants | Quality Assurance | Custom Filtering | Filtering by Regions | Example | Calculating MF | Mutation Counting | Grouping Mutations | Mutation Subtypes | Selecting Variation Types | Correcting Depth | Precalculated Depth | Summary Table | MF Plots | plot_mf | plot_mean_mf() | Exporting Results | Summary Report | Parameters | Report | References | Appendix | Session Info

Introduction | Installation | Getting data | Mass spectrometry experiment | Experiment files | Experimental design | Raw data | Third party applications | Saving and reusing experiments | Linking experimental data to samples | Subset and filter MsExperiment | Using MsExperiment with MsBackendSql | Session information

Introduction | Installation | Extracting data from Spectra objects with MassQL | MassQL definition | Type of data | Condition | Filter | Differences of the SpectraQL implementation to the MassQL definition | Examples | Filtering and subsetting | Choosing which data to return | Filtering peaks within spectra | Session information

Introduction | Memory requirements of different data representations | Chunk-wise and parallel processing | Notes and suggestions for parallel or chunk-wise processing | Spectra functions supporting or using parallel processing | Session information

Introduction | Installation | Load data | Denoising the data | Check results | Session information

An Example | An Initial Recipe | Preprocessing Steps | Differential Analysis | Prep | Bake and cool | Session info

Load dar package and data | Recipe initialization | Recipe QC and preprocessing steps definition | Define Differential Analysis (DA) steps | Prep recipe | Default results extraction | Exploration for consensus strategie definition | Define a consesus strategy using bake | Extract results | Session info

Introduction | Load dar package and data | Recipe Initialization | Recipe QC and Preprocessing Steps Definition | Define Differential Analysis (DA) steps | Prep recipe | Default results extraction | Exploration for consensus strategie definition | Define a consesus strategy using bake | Extract results | Session Info

Introduction | Installation | Creating and using MsBackendSql SQL databases | Performance considerations | Database systems and data storage modes | Database systems | MsBackendSql database layouts/storage modes | Performance comparison with other backends | Considerations for database systems/servers | Other properties of the MsBackendSql | Summary | Session information

Introduction | Installation | General description | Example use cases | Matching of m/z values | Matching of m/z and retention time values | Matching of SummarizedExperiment or QFeatures objects | Matching of MS/MS spectra | Using alternative spectra similarity functions | Query against multiple reference databases | Finding MS2 spectra for selected m/z and retention times | Performance and parallel processing | Utility functions | Creating mixes of standard compounds | Input format | Using the function | Session information | References

Installation | Overview | Data preparation | Cell profile matrices | Performing basic deconvolution with the spatialdecon function | Using the advanced settings of spatialdecon | Plotting deconvolution results | Other functions | Combining cell types: | Inferring an expression profile for a cell type omitted from the profile matrix | Reverse deconvolution | Session Info

Load packages | Setting Up Secure Credentials | Using operating system credential store | Using environmental variables with httr2 secret | Create and Store a Master Secret Key | Encrypt your EGA password | Store the encrypted password | Store your username | Restart R | Fill in the submission template | Data submission | Metadata parsing | Metadata validation | Running new_submission workflow | Manual client creation | Other workflows | Examples | Utilities | Notes | Bearer token authentication | Issues | Session Info

Installation | Introduction | Cross-species analysis and sequencing technologies (e.g.: Human vs Mouse, ATAC-Seq vs RNA-Seq) | Compatibility | Using ClusterFoldSimilarity to find similar clusters/cell-groups across datasets | Analyzing graph communities to identify super-groups of similar cell populations | Retrieving the top-n similarities | Obtaining the top-n feature markers | Retrieving all the similarity values and plotting a similarity heatmap | Using ClusterFoldSimilarity across species and numerous datasets: | Similarity score calculation | Session information

Cellular neighborhood | Average Nearest Neighbour Index (ANNI) | You can access the vignettes for other modules of SPIAT here: | Reproducibility | Author Contributions

Overview | Finding nearest neighbors | Finding all neighbors within range | Saving indices to disk | Usage in downstream C++ libraries | Extending to new methods | Introduction | In R | In C++ | Session information

Introduction | Installation | Input Data | Example Data from the package | Using Your Own Data | Notes for Users | Step 1- Data Processing | Description | Required inputs | Example Code | Outputs | Step 2 - Split Data into Training and Test Subset | Step 3 - Data Normalization | Step 4a - Prognostic Index (PI) Score Calculation | Step 4b - Univariate Survival Significant Feature Selection | Step 5 - Prediction model development for survival probability of patients | Model for only Clinical features | Example Code | Model for PI | Model for Clinical features + PI | Model for Univariate + Clinical features | Step 6 - Survival curves/plots for individual patient | Required Inputs | Step 7 - Predicted mean and median survival time of individual patients | Step 8 – Risk-Group Prediction of Test Samples Based on Selected Features | Step 9 – Visual Overlay of Predicted Test Sample on Kaplan-Meier Curve | Step 10 - Nomogram based on Key features | SessionInfo | References

Introduction | Installation | GitHub Install | Bioconductor Install | Environment Setup | Workflow Overview | Import to SummarizedExperiment | Experiment Structure | Assay Matrices | Coldata | Rowdata | Metadata | Reporting Results | Analysis by Logistic Regression | Data Import | Optimizing Models to PCR Curves | Deriving PCR Results from the Model | Interoperability | NormqPCR | Session Info

Introduction | Background | Motivation and Design | History of GES Databases | Terminology | Getting Started | Package Install | Reference Databases | Signature Searches (GESS) | Test Query and Database | CMAP Search Method | LINCS Search Method | gCMAP Search Method | Fisher Search Method | Correlation-based Search Method | CORall | CORsub | Summary of Search Results | GESS Result Visualization | Batch Processing of GESSs | Sequential Processing | Parallelization with Multiple CPU Cores | Parallelization with Multiple Computer Systems | Functional Enrichment (FEA) | TSEA | Hypergeometric Test | With GO | With Reactome | mGSEA Method | MeanAbs Method | DSEA | GSEA Method | Comparing FEA Results | Summary of FEA Results | Drug-Target Network Visualization | Run Workflow | Additional Databases | Gene Set Databases | Overview | Gene Sets as Queries | Gene Sets as Database | MSigDB | GSKB | Supplemental Material | Construction of Toy Database | Session Info | Funding | References

Introduction | Installation | Quick Start | 1. Load example data | 2. Build glycan trait matrices | 3. Differential analysis | 4. Trait distribution visualization | Define Your Own Glycan Motifs | 1. Parse and plot glycan trees | 2. A Simple User Motif (Example 1) | 3. Motif Containing Fucose (Example 2) | 4. Integrating Motifs with glycoTraitR (for Trait Computation) | Conclusion

Introduction | Sample usage | gcat | Data Input | References

Introduction | Sample usage | lfa | af | Data Input | References

Introduction | Installation | path_enrich | interactions | compl_data | rshiny | Conclusion: how to use famat | References | Session Info

Overview | CosMx (Nanostring) | Route A — Direct loading with SpaceTrooper | CosMx protein | Route B — Via SpatialExperimentIO, then standardize | Xenium (10x Genomics) | File layout notes | Session info

Introduction | Quality Control pipeline | Load example data | Field of View (FOVs) visualization | Load polygons | Add QC metrics | Compute Quality Score | Data visualization | Conclusion | Session Information

Introduction | Quality Control pipeline | Load example data | Field of View (FOVs) visualization | Load polygons | Add QC metrics | Compute Quality Score | Data visualization | Conclusion | Session Information

Motivation | Installation | Drift correction | Flagging low quality compounds | Imputation of missing values | Utilities | Authors & Acknowledgements | Session information | References

Project setup | Set up path and logging | Read data | Tabular data preprocessing | Preprocessing by mode | Preprocessing for the complete dataset | Feature selection | Univariate analysis | Session information | References

Introduction | Current Features | Processing speed | Reference-free processing | Sample data | Amplicon-based methylation NGS data | Capture-based methylation NGS data | Long-read native NGS data (adaptive sampling) | Manually creating sample BAM files | Typical workflow | Requirements | Short-read sequencing | Long-read sequencing | Reading the data | Specific considerations for long-read sequencing data: | Optional calling of cytosine methylation | Making cytosine reports | Making VEF reports for a set of genomic regions | Linearized MHL reports | Exploring DNA methylation patterns | Exploring sequence variants in epialleles | Plotting the distribution of per-read beta values | Other information | Citing the epialleleR package | The data underlying epialleleR manuscript | Our experimental studies that use the package | Session Info | References

Background | Installation | Loading the package | Helper functions for common queries | Retrieve Maintainer and Package Information | List Maintainer Emails | Retreive Database | Session Info

Background | Installation | Loading the package | Launching the Shiny Application | Filtering | Optional Columns | Session Info

Introduction | Installation | Importing MS/MS data from MassBank files | Accessing the MassBank MySQL database | Pre-requisites | Direct access to the MassBank database | Session information

Introduction | Installation | Quick start | SessionInfo

Introduction | Installation | Quick start | SessionInfo

Why streaming? | Which chunk size to use? | Overview of the streaming API | Streaming using default callbacks | Assumptions | Obtaining the callbacks | Running the demultiplexing | Displaying the result from streaming | Streaming the barcode table | Considerations about size of tables | Subsetting the frequency table | Writing to database | Streaming using custom callbacks | Desired properties | The state object | The loader | The archiver | Putting it all together | Verifying results | Reproducibility | Bibliography

Overview | Quick start | Considering directionality | Differential gene set tests | More output options | Session information

Introduction | Motivation | Overview | Installation | Preview of the NCI-60 dataset | Running and reviewing the MCIA output | Brief overview of the Global Scores Matrix ($F$) | Brief overview of the Global Loadings Matrix ($A$) | Part 1: Interpreting Global Factor Scores | nipals_multiblock() Generates Basic Visualizations | Visualizing a Factor Plot with Only Global Factor Scores | Visualizing the Clustering of Samples by Factor Scores | Part 2: Interpreting Global Loadings | Pseudoeigenvalues Representing the Contribution of Each Omic to the Global Factor Score | Visualize All Feature Loadings on Two Axes | Scree Plot: Visualizing the Top Features per Factor | Factor 1 | Factor 2 | Factor 4 | Pathway Analysis for the Top Factors using Data from Gene-Centric Omics Blocks | Gather Data and Generate the Report | Investigating the GSEA Summary Table | Session Info

Introduction | Installation | Vignette Pipeline | Data | All Sources | Bioconductor | 10x Genomics and Seurat | MCIA | Metadata | Running the decomposition | Visualization | Define colors | Eigenvalue scree plot | Projection plot | Global scores heatmap | Block weights heatmap | Loadings | Top features | Factor 1 | Factor 4 | Deep dive: Seurat analysis | Read in and process the data | Quality control | Metrics summary | GEX QC metrics | Before filtering | After filtering | Standard Seurat pipeline | Dimensionality reduction | Load in the processed object | PCA | UMAP | Marker overlays | Load marker genes | Dot plots | GEX | ADT | Feature plots | Violin plots | Annotate cell clusters | Annotations | UMAPs | Check the annotations | Save for MCIA | Session Info

Introduction | Session Info

Introduction | Installation | Input data | Performing batch (study) effect adjustment with adjust_batch | Meta-analytical differential abundance testing with maaslin_meta | Identifying discrete population structures with discrete_discover | Identifying continuous population structures with continuous_discover | Sessioninfo | References

Introduction | Installation | Included models | Basic pipeline to identify cell types in a scRNA-seq dataset using scAnnotatR | Preparing the data | Cell classification | Parameters | Result interpretation | Result visualization | Session Info

Introduction | Quick start: Transcript discovery and quantification with bambu | Installation | General Usage | A complete workflow to identify and quantify transcript expression from Nanopore RNA-Seq data | Input data | Aligned reads (bam files) | Genome sequence (fasta file/ BSGenome object) | Genome annotations (bambu annotations object/ gtf file / TxDb object) | Transcript discovery and quantification | Running bambu | Running multiple samples | Modulating the sensitivity of discovery (pre and post analysis) | Visualise results | Obtain gene expression estimates from transcript expression | Save data (gtf/text) | Advanced usages for different use cases | Using a pre-trained model | De-novo transcript discovery | Storing and using preprocessed files (rcFiles) | Tracking read-to-transcript assignment | Training a model on another species/dataset and applying it | Including single exons | Downstream analysis | Identifying differentially expressed genes | Identifying differential transcript usage | Getting help | Citing bambu | Session Information

Introduction | Identification of gene modules | Load BRCA sample data | module_WGCNA | module_GFA | module_igraph | module_ProNet | module_NMF | module_clust | module_biclust | Discovery of miRNA sponge modules | Inference of sample-specific miRNA sponge modules | Modular analysis of miRNA sponge modules | Functional analysis of miRNA sponge modules | Cancer enrichment analysis of miRNA sponge modules | Validation of miRNA sponge interactions in miRNA sponge modules | Co-expression analysis of miRNA sponge modules | Distribution analysis of sharing miRNAs | Predicting miRNA-target interactions | Identifying miRNA sponge interactions | Conclusions | References | Session information

The challenge with interpreting differences in microbiome data | The idea behind Taxon Set Enrichment Analysis | But isn’t this what functional profiling tools (e.g. HUMAnN3) do?

Installation | Foreword - Preparation of pipeline results to be visualized | Introduction | Example dataset (more details in CytoPipeline vignette) | Example of pre-processing and QC pipelines (more details in CytoPipeline vignette) | Interactive visualizations | Visualizing pipeline runs at different steps | Visualization of scale transformations | Session information | References

Overview | Import quantitative dataset | Quality control | Peptide intensity plots | Relative log intensity boxplot | Sample correlation plot | Hierachical clustering dendrogram | Principle component analysis scatterplot | Bait protein coverage plot | Data normalization | Aggregation of peptide intensities into protein intensities | Merging of similar peptides/sites into unified intensities | Regression Analysis | Differential statistical analysis | Session Information

ORA vs Enrichment (two ways to run TaxSEA) | Enrichment (rank-based) | What it does (in plain terms) | When to use it (recommended in most cases) | Pros | Cons | ORA (Over-Representation Analysis) | When to use it | Recommended practice

What problem does TaxSEA actually solve? | Is TaxSEA a replacement for differential abundance? | Can I use TaxSEA even when there are no individual species level differences? | How is this different from functional profiling tools like HUMAnN3? | Does this only work for the human gut microbiome? | Do I need differential abundance results first? | Why use ranks instead of thresholds? | Isn’t this just gene set enrichment, but for microbes? | Will this work if my DA results are messy or inconsistent? | Have you shown TaxSEA is accurate? | When should I not use TaxSEA? | Can I use TaxSEA with 16S rRNA gene sequencing data? | Why does TaxSEA use species-level data instead of strain-level?

Data | Select target genes | Assess target gene panels | Session information

Introduction | List of functions | Example Analysis | Example 1: Differentially methylated probes from EWAS | Example 2: Enrichment analysis for arrays other than 450K and EPIC | Example 3: Enrichment analysis with user provided geneset | Example 4: Enrichment analysis for gene expression type of data that do not | need to combine test statistics | Gene set and pathway databases | Kyoto Encyclopedia of Genes and Genomes (KEGG) | Gene Ontology (GO) | The Molecular Signatures Database (MSigDB) | Reactome | Types of gene set enrichment analyses | Method options to combine p-value: | References

Introduction | Creating CompDb databases | CompDb from HMDB data | CompDb from custom data | CompDb from MoNA data | CompDb by sequentially filling with data | Extending CompDb databases | Session information

SpaNorm | Load count data | Normalise count data | Using Seurat objects | Computing alternative adjustments using a precomputed SpaNorm fit | Varying model complexity | Enhancing signal | Exploring learnt functions | Identifying spatially variable genes | GLM-PCA | Session information | References

Introduction | Quick Start | Read in data and create initial dataset | Use labelQC to obtain labels for each obsevation | Individual functions | Beads | Debris | Doublets | Dead cells | UMAP for exploration | SessionInfo

Overview | Quick start | Explaining parameters | Gene-level statistics | More output options | Session information

Introduction | Quick start | Writing the template | Representing data inputs | Computing results | Session information

Installing and starting the program | Quick reference of psichomics functions | Exploration of clinically-relevant, differentially spliced events in breast cancer | Downloading and loading TCGA data | Filtering and normalising gene expression | Quantifying alternative splicing | Data grouping | Principal component analysis (PCA) | NUMB exon 12 inclusion and correlation with QKI gene expression | Differential inclusion of NUMB exon 12 | Correlation between NUMB exon 12 inclusion and QKI expression | Differential splicing analysis | Performing multiple survival analysis | Differential gene expression | UHRF2 exon 10 inclusion | Survival analysis | Differential expression | Literature support and external database information | Interpretation | Loading data from other sources | Load GTEx data | Load SRA project data using recount | Load user-provided data | Feedback | References

Installing and starting the program | Exploration of clinically-relevant, differentially spliced events in breast cancer | Downloading and loading TCGA data | Filtering and normalising gene expression | Quantifying alternative splicing | Data grouping | Principal component analysis (PCA) | PCA performance | PCA plotting | NUMB exon 12 inclusion and correlation with QKI gene expression | Differential inclusion of NUMB exon 12 | Correlation between NUMB exon 12 inclusion and QKI expression | Differential splicing analysis | Filtering alternative splicing events | Performing multiple survival analysis | Differential gene expression | UHRF2 exon 10 inclusion | Survival analysis | Differential expression | Literature support and external database information | Interpretation | Exploring alternative splicing changes between human isogenic stem cells and fibroblasts | Load SRA and user-provided local files | Feedback | References

Supported file formats | Prepare SRA Run Selector data | Prepare tables based on RNA-seq data using STAR | Download FASTQ files (optional) | Align RNA-seq data to quantify splice junctions | Index the genome using STAR | Align against genome index using STAR | Prepare output for psichomics | Prepare VAST-TOOLS data | Prepare FireBrowse data | Prepare GTEx data | Prepare data from any source | Sample information | Subject information | Gene expression | Exon-exon junction quantification | Alternative splicing quantification (also known as inclusion levels) | Load user-provided data into psichomics | Load using the visual interface | Load using the command-line interface (CLI) | Feedback | References

Creating custom alternative splicing annotation | SUPPA annotation | rMATS annotation | MISO annotation | VAST-TOOLS annotation | Combining annotation from different sources | Quantifying alternative splicing using the custom annotation | Feedback

Estimating pairs filtering thresholds | Filtering pairs using appropriate thresholds | Computing distance law from pairs | Plotting distance law: first try | Second try: switching to logarithmic scale | Third try: aggregating data before plotting | With some polishing | Reproducibility

Abstract | Citation | Introduction | Data profiling | Coverage plot | Profile of data in specific regions | Profile of one sample of one reference region | Visualization of base modification | Visualization of gene track | Visualization of motif | Peak Annotation | Visualize Genomic Annotation | Visualize distribution of TF-binding loci relative to TSS | Functional enrichment analysis | ChIP peak data set comparison | ChIP peak annotation comparision | Functional profiles comparison | Overlap of peaks and annotated genes | Statistical testing of ChIP seq overlap | Shuffle genome coordination | Peak overlap enrichment analysis | Data Mining with ChIP seq data deposited in GEO | GEO data collection | Download GEO ChIP data sets | Overlap significant testing | Need helps? | Session Information | References

Preparation | The openSesame Pipeline | Data Preprocessing | Preprocessing Function Code | Lift over across platforms | Project probe IDs | Project beta values | Project signal SigDFs | Collapse to cg prefixes | Session Info

Sex, XCI | Age & Epigenetic Clock | Copy Number | Cell Count Deconvolution | Genomic Privacy | De-identify by Masking | De-identify by Scrambling | Re-identify | Session Info

Calculate Quality Metrics | Rank Quality Metrics | Quality Control Plots | Session Info

Species Inference | Mouse Strain Inference | Quality Masking | Human-mouse Mixture | Session Info

Differential Methylation | Test Interpretation | Goodness of Fit | Pairwise Comparison | Continuous Predictors | DMR | Session Info

Introduction | Package Overview | Installation | Demo | Load Required Packages | SummarizedExperiment | Opting In and Out of Tidy Print | Opt In to Tidy Print | Opt Out of Tidy Print | Messaging function | Session info

Brief Introduction | Packages installation and loading | Case study description | Data pre-processing | Pre-filtering | Transformation | Batch effect detection | PCA | Boxplots and density plots | Heatmap | pRDA | Batch effect correction | PLSDA-batch | Regression mode | Canonical mode | sPLSDA-batch | Assessing batch effect correction | Methods that detect batch effects | Other methods | Variable selection | Session Information | References

Introduction | Using built-in references | Using single-cell references | Annotation diagnostics | FAQs | Session information | References

Overview | Installation | Loading Processed Single-Cell Data | Getting Gene Sets | Option 1: Built-In gene sets | Option 2: MSigDB via getGeneSets() | Option 3: Define personal gene sets | Option 4: Using msigdbr | Performing Enrichment Calculation | ssGSEA | GSVA | AUCell | UCell | escape.matrix | runEscape | performNormalization | Visualizations | heatmapEnrichment | geyserEnrichment | ridgeEnrichment | splitEnrichment | gseaEnrichment | densityEnrichment | scatterEnrichment | Statistical Analysis | Principal Component Analysis (PCA) | Precomputed Rank Lists | Why do this? | Example enrichIt() workflow | Visualising the enrichment table | Differential Enrichment | Conclusions

Installation | Load data | Overview of the data | Custom themes | Session Info

Installation | Introduction | Examples | Mouse genes | Infer the species | Rat genes | Create example data | Human genes | Additional test_species | Session Info

orthogene: Interspecies gene mapping | Installation | Examples | Convert orthologs | Note on non-1:1 orthologs | Map species | Report orthologs | Map genes | Aggregate mapped genes | Get all genes | Session Info

Overview | Installation | Usage | Prepare the gene lists | Convert gene symbol IDs to Entrez IDs | Run enrichment queries | View enrichment of publication top-ranked gene | Convert drug database identifiers to PubChem CIDs | Change default limits of enrichment queries | Session Info | References

Introduction | Installation | Visualizing RNA-Seq data with volcano plots | Visualizing fold changes across comparisons | Building and visualizing PPI networks | Enriching networks and extracting subnetworks | Performing pathway enrichment | Plotting pathway enrichment results | Generating networks from enriched pathways | Supplemental materials | Gene-pair signatures | Why are there different p-value cut-offs for sigora vs. ReactomePA/Hallmark? | Citations | Session information

Introduction | Motivation | News in Version PIUMA 1.6 | Installation | Scope of this Vignette | The testing dataset | The TDA object | Preparing data for Mapper | TDA Mapper | Nodes Similarity and Enrichment | Network Assessment | Cluster assignment | Geometry-guided Community Mining of TDA Mapper() Graph | Export data for Cytoscape | Session Info | References

Introduction | Installation | Scope of this Vignette | Seurat pbmc3k testing data | PIUMA TDA clustering | Biological Validation: GZMK+ CD8+ T Subset | Quantitative Comparison | Conclusion | Session Info | References

One file | Multiple files | Parsing records into a data frame | Multiple files as data frame | All benchmarks | Runtime | Session info

Setup | Input BED files | Regions | Multithreading | tabix() | summarize_regions | make_mat | Session info | References

Setup | Download the data | Using tabix() | Get the Transcription start sites and flanking regions | Make a tabix query of the TSS flanking regions | Summarize average methylation profile around TSS | Plot average methylation profiles around the TSS | Using summarize_regions | Session info

r Biocpkg("GenomicRanges") | From tabix queries | From summarize_regions() | From make_mat | r Biocpkg("SummarizedExperiment") | Making BSseq objects | Session info

BugSigDB: a comprehensive database of published microbial signatures | Obtaining published microbial signatures from BugSigDB | Extracting microbe signatures | Writing microbe signatures to file in GMT format | Displaying BugSigDB signature and taxon pages | Ontology-based queries for experimental factors and body sites | Session info

Overview | Basic analysis | Blocking on batches | Combining multiple modalities | Other useful functions | Session information

Introduction | Benchmarking | Comparison | Computational Performance | Score Concordance | Replicating Other Methods | Conclusions | Session Info

Introduction | Motivation | Example: Single-cell RNA-seq hallmark scoring | Preparing data | Preparing gene sets | Calculating the score | Very large matrices | Differential expression testing using dualGSEA | Replicating ssGSEA, singscore and scSE | Replicating singscore | Replicating ssGSEA | Replicating the scSE score | Compare scores | Session info

What is it? | How to get help | Further reading

Introduction | Installation | CoverageExperiment and AggregatedCoverage classes | CoverageExperiment | AggregatedCoverage | Manipulate CoverageExperiment objects | Create a CoverageExperiment object | Bin a CoverageExperiment object | Expand a CoverageExperiment object | Plot coverage of a set of tracks over a single genomic locus | Manipulate AggregatedCoverage objects | Aggregate a CoverageExperiment into an AggregatedCoverage object | AggregatedCoverage over multiple tracks / feature sets | Plot aggregated coverages with ggplot2 | Use a tidy grammar | Example workflow using tidy grammar | Example use case: AnnotationHub and TxDb resources | Recover TSSs of forward mouse genes | Recover H3K4me3 coverage track from AnnotationHub | Compute the aggregated coverage of H3K4me3 ± 3kb around the TSSs of forward mouse genes | With more genomic tracks | Session info

1. Introduction | 2. Installation | 3. Data Structure | 4. From raw_data to norm_data | 4.1 Data Preprocessing | 4.2 Data Analysis | 4.2.1 Individual Well Application | 4.2.2 96-Well Plate Application | 4.3 Data Summary | 5. From norm_data to tsar_data | 5.1 Merge Replicates | 5.1.1 Jumpstart to Graph | 5.2 Graphic Analysis | 5.2.1 Tm Boxplot | 5.2.2 TSA Curve Visualization | 5.2.3 Curves by Condition | 5.2.4 First Derivative Comparison | 6. Session Info | 6.1 Citation | 6.2 Session Info

1. Introduction | 2. Installation | 3. Load Data | 4. Data Pre-Processing | 5. 96-well Analysis Application | 6. Intermediate Data Output | 7. Complete Dataset with Ligand and Protein Information | 8. Merge Data across Biological Replicates | 9. Tm Estimation Shift Visualization | 10. TSA Curve Visualization | 11. Session Info | 11.1 Citation | 11.2 Session Info

Introduction | Loading the example data | Note on package design | The regression_functions argument | The extra_args_regression_params argument | Neural networks | Conclusion | Information about the R session when this vignette was built

Load packages | Methods | Creation of the evidence bins | P-value adjustment | Example usage | Generating input data | Differential State (DS) analysis | Generating fake bulk data | bbhw | Session info | References

Overview | Initialize igvR | Load alignments for the currently displayed genomic region | Display the alignment | Narrow Peaks from MACS | Motif Matching | Display the Matches | H3K4Me3 Histone Marks | Zoom in to one interesting, and possibly functional CTCF binding site

Basics | When and why to use this package | Required knowledge | Limitations | Ways to interact with this package | Install posDemux | Asking for help | Citing posDemux | An example with PETRI-seq | Quick overview of the method | Sequence annotation | Data loading | Running the demultiplexer | Error correction | Filtering by barcode frequency | Exporting results | Reproducibility | Bibliography

Motivation | Installation | Creating QTLExperiment instances | Manually | From QTL summary statistics for each state | From mashr data format | Basic object manipulation | 4. Working with assays | Working with critical meta data | Session Info

1. Introduction | 2. Installation | 3. Load TSAR Package | 4. Data Pre-Processing | 5. Data Analysis | 6. Data Visualization | 7. Session Info | 7.1 Citation | 7.2 Session Info

Introduction | Installation | Use Cases | Package Design | Pipeline Components | Use Case 1: Identifying Candidate Drugs from an Input Signature | Step 1: Get the Signature | Step 2: Prepare the Signature | Step 3: Filter the Signature | Step 4: Get the Concordant Signatures | Step 5: Get the list of Consensus Concordant Signatures | Alternate One-Step Method | Environment Setup

Basic workflow | Creating a data set | Calculating performance scores | Preparing performance object for plotting | Plotting | Modifications to the basic workflow | Stratification | Modification of plots | Custom color assignment | Interactive exploration | Session info

Introduction | Installation and loading dependencies | Dataset | Generation of input files for the Shiny application | Visualization of the MDS projection | fsap dataset methods | Data mining and assembly of a reference dataset | Pairwise alignments using primary structure and hydrophobicity | Physicochemical parameters distinguishing FSAP | Acknowledgement | Session information | References

Installation and loading dependencies | Introduction | Illustrative dataset | Generation of input files | Visualization of the MDS projection | General settings | Session information | References

Introduction: Basic concepts of Dockstore and Bioconductor | Working with the Dockstore API in Bioconductor | Appendix | Acknowledgments | Session info

Installation | Workflow setup: DESeq2 | Setting up the workspace and choosing a workflow | Retrieving the configuration | Updating workflows | Changing the inputs / outputs | Update configuration locally | Set a workflow configuration for reuse in AnVIL | Running and stopping workflows | Running a workflow | Monitoring workflows | Stopping workflows | Managing workflow output | Workflow files | Workflow information | Session information

Introduction | Installation | Core functions | Plot gene/metagene with 5'UTR, CDS and 3'UTR | Plot along the ranges of genomic features | Plot genomic loci (start, end or center of a feature) | Plot peak annotation statistics | Auxillary functions | Plot bam correlations | Plot bed overlaps | Appendix | Session info

Overview | Quick start | For R administrators | Session information

Introduction | Supported Formats | Tested 10X Products | Bioconductor implementations | Installation | Load the package | Description | Procedure | Dataset versioning | File classes | TENxFile | ExperimentHub resources | TENxH5 | import TENxH5 method | TENxMTX | import MTX method | TENxFileList | TENxPeaks | TENxFragments | Session Information

Differential State (DS) analysis with muscat & DeeDeeExperiment | DeeDeeExperiment on the Kang dataset | Session info | References

Introduction | Installation | Terminology | CRISPRko design | Nuclease specification | Target DNA specification | Designing spacer sequences | Sequence features characterization | Off-target search | Iterative spacer alignments | Faster alignment by removing repeat elements | Off-target scoring | On-target scoring | Restriction enzymes | Gene annotation | TSS annotation | SNP information | Filtering and ranking gRNAs | CRISPRa/CRISPRi design | CRISPR base editing with BE4max | CRISPR knockdown with Cas13d | Design for optical pooled screening (OPS) | Design of gRNA pairs with the PairedGuideSet object | Miscellaneous design use cases | Design with custom sequences | Off-target search in custom sequences | Adding non-targeting controls (NTCs) | Session Info | References

Introduction | Load packages | Data | Input data | Quality control/filtering | Clustering | Manual annotation | Spatially resolved (multi-sample) clustering | Single sample clustering | DSP testing | Gene-level test | Individual cluster test | Visualization | Abundance trend | Spatial expression | Smooth splines to model time | Predicted trend | Session info | References

Introduction | Load multiWGCNA library | Load astrocyte Ribotag RNA-seq data | Network construction | Compare modules by overlap | Identify a module of interest | Draw the multiWGCNA network | Observe differential co-expression of top module genes | Follow up with a preservation analysis | Determining if preservation value is significant | Conclusion

Introduction | Install the multiWGCNA R package | Load microarray data from human post-mortem brains | Perform network construction, module eigengene calculation, module-trait correlation | Compare modules by overlap across conditions | Perform differential module expression analysis | Perform the module preservation analysis | Summarize interesting results from the analyses | Print the session info

Introduction | Overview of HPiP | An Example of Predicting HP-PPIs | Data Set Preparation | Gold Standard Reference Dataset of Host-Pathogen PPIs | FASTA Sequence extraction | Sequence-based Features Extraction | Amino acid Composition (AAC) Descriptor | Dipeptide Composition (DC) Descriptor | Tripeptide Composition (TC) Descriptor | Tripeptide Composition (TC) from Biochemical Similarity Classes Descriptor | Quadruplets Composition from Biochemical Similarity Classes Descriptor | F1/F2 Composition Descriptor | Composition/Transition/Distribution (CTD) Descriptors | Composition (C) Descriptor | Transition (T) Descriptor | Distribution (D) Descriptor | Conjoint Triad Descriptor | Autocorrelation (Auto) Descriptors | k-Spaced Amino Acid Pairs | Binary encoding | BString Object as Data Input | Generate a SummarizedExperiment Objects | Table of Summary Descriptors | Combine Host-Pathogen Interaction Descriptors | Data Processing | Prediction Algorithm | Nework Visualization | GO Enrichment Analysis | Complex Prediction | Session info | References

Installation | Citing BreastSubtypeR | Brief description | Features | Implemented approaches | Quick start | Guidance & best practices | Input types | When to use AUTO | Interpretation | Shiny app | Launch the local Shiny app | Example data (for Shiny & scripts) | Limitations | Appendix | Sources & support | References | Session information

Overview | Installation and getting started | Software requirements | OS Requirements | Installation from Bioconductor | Installation from GitHub | Python scoring algorithms | Installing conda environments | Getting started | On-targeting efficiency scores | Cas9 methods | Rule Set 1 | Rule Set 3 | DeepHF | CRISPRscan | CRISPRater | Cas12a methods | enPAM+GB score | Cas13d methods | CasRxRF | Off-target specificity scores | MIT score | CFD score | Indel prediction score | Lindel score (Cas9) | License | Reproducibility | References

Introduction | Retrieval | Importing | Xenium | CosMx | Visium | VisiumHD | Chromium | Session

Overview | Introduction | Data | Installation | Running MotifPeeker | Load the package | Load the example datasets | Prepare input data | Peak Files | Alignment Files | Genome Build | Motif Files | Run MotifPeeker | Required Inputs | Optional Inputs | Other Options | Runtime Guidance | Outputs | Troubleshooting | Future Enhancements | Session Info

Introduction | Installation | Input files and function parameters | Example | Import FCS | Compensation | Transformation | Pre-gating | Extract intensity matrix | Gating on T-cell subsets | Pseudo-negative control | Empirical gating | Visualizing the gates | LAG3 for all CD3+ | LAG3 by CD4 and CD8 subsets | CCR7 for all CD3+ | CCR7 by CD4 and CD8 subsets | CD45RA for all CD3+ | CD45RA by CD4 and CD8 subsets | CD127 for all CD3+ | CD127 by CD4 and CD8 subsets | CD28 for all CD3+ | CD28 by CD4 and CD8 subsets | Getting percentage data | Optional: Adding density gates back to GatingSet | Session info | References

Overview | Installation | Data normalization; computing and plotting feature dominance | Normalization functions (e.g. quantile normalization) | Example 1: SummarizedExperiment object | Example 2: dataframe object | Calculation functions (e.g. entropy) | Visualization functions (2-, 3-, and N-dimensions) | Example 1: Two Dimensions with plot_rope() | Example 2: Three Dimensions with plot_triangle() | Example 3: N-Dimensions with plot_circle() | Session Info

Introduction | Quick start | Method overview | Steps before bootstrapping | Import excluded regions | Genome segmentation | Bootstrapping ranges | Assessing properties of bootstrap samples | Bootstrapping and plyranges | Counting the total number of overlaps | Computing the sum of signal value for nearby peaks | Block bootstrapping one region | Visualizing bootstrap types | Session information | References

Introduction | Data types | Case Study: Batch correction in Japanese datasets | Important options | Changing sensitivity | Use of the ExpressionSet | Parallelizing | Caching

terraTCGAData | Installation | Overview | Data | Requirements | Loading packages | gcloud sdk installation | Default Data Workspace | Clinical data resources | Clinical data download | Assay data resources | Summary of sample types in the data | Intermediate function for obtaining only the data | MultiAssayExperiment | Session Info

Introduction | Installation | Usage | Example - single-cell RNA-seq clustering benchmark | Programmatic interface | Exporting data from the app | Additional examples | Session info

Overview | What's New in Version 1.6.0 | Server Mode for JSON Uploads | JSON Serialization | Browser localStorage Caching | Cache Versioning | URL Parameters for Programmatic Loading | Server Mode Basics | Starting a Server | What Happens When You Start Server Mode | User Interface | Creating JSON Files | From Data Frames | From Existing SummarizedExperiment | Reading JSON Files | Two Loading Methods | Load from URL (jsonUrl) | Load from File Path (jsonFile) | Browser Cache Management | How Caching Works | Manual Cache Control | Clear Cache Button | Cache Versioning (Administrators) | Setting Up Versioning | Version String Format | User Experience | Session info

ClusterGVis | Usage | Basic examples: | Integration with seurat object: | Integration with SingleCellExperiment object: | Session Info

r BiocStyle::Biocpkg("mspms") | Introduction | Quickstart | Overview | Pre-processing data | A note on colData | PEAKS | Fragpipe | Proteome Discoverer | Data Processing | Accessing data stored in QFeatures | Statistics | Data Visualizations | QC Checks | Tidying our data | Heatmap | PCA | Volcano plots | Cleavage Position Plots | iceLogo | Plotting a Time Course

Introduction | Getting started | All set! | Description of the GeDi user interface | Header (navbar) | Sidebar | Body | The GeDi functionality | The Welcome panel | The Data Input panel | The Distance Scores panel | The Clustering Graph panel | The Report panel | Additional Information | Additional example data | FAQs | Session Info | References

Explanation of variables | Total reads | Read Quality | Read assignment | Edits | Reads by barcode | Summary Table

Overview | For package developers | Setting defaults | Historical comments | Session information

Overview | Background | Install and load pacakge | Citing lefser | Analysis example | Prepare input | Terminal node | Relative abundance | Run lefser | Visualization using lefserPlot | Benchmarking against other tools | Interoperating with phyloseq | Session Info

Introduction | Defining the base editor object | Designing spacer sequences | Allele prediction | gRNA-level aggregate variant scores | Session Info | References

Introduction | High level R-HDF5 functions | Creating an HDF5 file and group hierarchy | Writing and reading objects | Writing and reading objects with file, group and dataset handles | Saving multiple objects to an HDF5 file (h5save) | List the content of an HDF5 file | Dump the content of an HDF5 file | Reading HDF5 files with external software | Removing content from an HDF5 file | 64-bit integers | Low level HDF5 functions | Creating an HDF5 file and a group hierarchy | Writing data to an HDF5 file | Session Info

Loading necessary libraries | Quick run | Performance considerations | Using Reactome pathways | Starting from files | Over-representation test | Session info

Overview of scoring functions | Unlabeled PSM at 1.07% ^13^C | Labeled PSM at 50% ^13^C | Annotate B and Y ion fragments | Guidance on parameter selection and interpretation | Why Aerith stands out

Overview | Monte Carlo method | FFT-based algorithm | Summary

Overview | Unlabeled Spectra | Get precursor mass | Plot precursor's theoretical spectra | Get peptide fragments' mass | Plot peptide fragments' theoretical spectra | Labeled Spectra

Introduction | Installation | Example workflow | 1. Load example ChIP-seq peak sets (genomic) | 2. Compute overlaps between genomic regions | 3. Visualization | Venn diagram | UpSet plot | Export visualization | 4. Extract elements per overlap group | Overlap group naming | Extract one particular group | Export overlap groups | For all overlap types (genomic or gene sets): | For genomic overlaps only: | Customization examples | Custom fills with transparency | Colored edges, no fills (colored borders only) | Custom labels and counts + percentages | Legend at the bottom with custom text | Combining multiple custom options | Session info | References | Example A549 ChIP-seq dataset | Supporting packages

Introduction | Acquiring and installing TENET and associated packages | Loading TENET | Running TENET without internet access | Input data | Expression SummarizedExperiment object | Methylation SummarizedExperiment object | MultiAssayExperiment colData data frame (optional) | MultiAssayExperiment sampleMap data frame | Overview of main TENET functions | easyTENET: Run the step 1 through step 6 functions with default arguments | step1MakeExternalDatasets: Create a GRanges object representing putative regulatory element regions, based on the data sources selected for inclusion, to be used in later TENET steps | step2GetDifferentiallyMethylatedSites: Identify differentially methylated RE DNA methylation sites | step3GetAnalysisZScores: Calculate Z-scores comparing the mean expression of each gene in the case samples that are hyper- and/or hypomethylated for each RE DNA methylation site identified in step 2 | step4SelectMostSignificantLinksPerDNAMethylationSite: Select the most significant RE DNA methylation site-gene links to each RE DNA methylation site | step5OptimizeLinks: Find final RE DNA methylation site-gene links using various optimization metrics | step6DNAMethylationSitesPerGeneTabulation: Tabulate the total number of RE DNA methylation sites linked to each gene | TCGADownloader: Download TCGA gene expression, DNA methylation, and clinical datasets and compile them into a MultiAssayExperiment object | TENETCacheAllData: Cache all online datasets required by TENET examples and optional features | Overview of downstream step 7 functions | step7ExpressionVsDNAMethylationScatterplots: Create scatterplots displaying the expression of the top genes and the methylation levels of each of their linked RE DNA methylation sites, optionally incorporating copy number variation, somatic mutation, and purity data | step7LinkedDNAMethylationSiteCountHistograms: Create histograms displaying the number of total genes and transcription factor genes linked to a given number of RE DNA methylation sites | step7LinkedDNAMethylationSitesMotifSearching: Search for transcription factor motifs in the vicinity of DNA methylation sites and/or within custom regions defined by the user | step7SelectedDNAMethylationSitesCaseVsControlBoxplots: Generate boxplots or violin plots comparing the methylation level of the specified RE DNA methylation sites in case and control samples | step7StatesForLinks: Identify which of the case samples harbor each of the identified regulatory element DNA methylation site-gene links | step7TopGenesCaseVsControlExpressionBoxplots: Generate boxplots or violin plots comparing the expression level of the top genes and transcription factors in case and control samples | step7TopGenesCircosPlots: Generate Circos plots displaying the links between the top identified genes and each of the RE DNA methylation sites linked to them | step7TopGenesExpressionCorrelationHeatmaps: Generate mirrored heatmaps displaying the correlation of the expression values of the top genes and TFs | step7TopGenesDNAMethylationHeatmaps: Generate heatmaps displaying the methylation level of all RE DNA methylation sites linked to the top genes and transcription factors, along with the expression of those genes in the column headers, in the case samples within the supplied MultiAssayExperiment object | step7TopGenesOverlappingLinkedDNAMethylationSitesHeatmaps: Generate binary heatmaps displaying which of the top genes and transcription factors share links with each of the unique regulatory element DNA methylation sites linked to at least one top gene/TF | step7TopGenesSurvival: Perform Kaplan-Meier and Cox regression analyses to assess the association of patient survival with the expression of top genes and transcription factors and methylation of their linked RE DNA methylation sites | step7TopGenesTADTables: Create tables using user-supplied topologically associating domain (TAD) information which identify the TADs containing each RE DNA methylation site linked to the top genes and transcription factors, as well as other genes in the same TAD as potential downstream targets | step7TopGenesUCSCBedFiles: Create BED-formatted interact files which can be loaded on the UCSC Genome Browser to display links between top genes and transcription factors and their linked RE DNA methylation sites | step7TopGenesUserPeakOverlap: Identify if RE DNA methylation sites linked to top genes and transcription factors are located within a specific distance of specified genomic regions | Datasets included in the TENET package | humanTranscriptionFactorList: Human transcription factor list | humanTranscriptionFactorDb: Human transcription factor database | Acknowledgements | Session info

Introduction | Functions for Evaluation of Dataset Alignment | Statistical Measures to Assess Dataset Alignment | Marker Gene Alignment | Purpose and Applications | Preliminaries | Evaluation of Dataset Alignment | comparePCA() | comparePCASubspace() | plotPairwiseDistancesDensity() | Purpose | Functionality | Interpretation | calculateWassersteinDistance() | Code Example | calculateCramerPValue() | calculateHotellingPValue() | calculateAveragePairwiseCorrelation() | regressPC() | Query-only with Batch Information | Query + Reference with Batch Information | Diagnostic Value | calculateHVGOverlap() | How the Function Operates | calculateVarImpOverlap() | Overview | Usage | Interpretation: | R Session Info

Overview | The tidyomics ecosystem | Core packages | Additional manipulation packages | Analysis packages | Installation

Overview | Installation | Software requirements | OS Requirements | Getting started | Nuclease class | Examples | Accessor functions | CrisprNuclease class | CrisprNuclease objects provided in CrisprBase | CRISPR arithmetics | CRISPR terminology | Cut site | Obtaining spacer and PAM sequences from target sequences | Obtaining genomic coordinates of protospacer sequences using PAM site coordinates | BaseEditor class | CrisprNickase class | RNA-targeting nucleases | Additional notes | dCas9 and other "dead" nucleases | License | Reproducibility | References