Package: ChromSCape 1.15.0

Pacome Prompsy

ChromSCape: Analysis of single-cell epigenomics datasets with a Shiny App

ChromSCape - Chromatin landscape profiling for Single Cells - is a ready-to-launch user-friendly Shiny Application for the analysis of single-cell epigenomics datasets (scChIP-seq, scATAC-seq, scCUT&Tag, ...) from aligned data to differential analysis & gene set enrichment analysis. It is highly interactive, enables users to save their analysis and covers a wide range of analytical steps: QC, preprocessing, filtering, batch correction, dimensionality reduction, vizualisation, clustering, differential analysis and gene set analysis.

Authors:Pacome Prompsy [aut, cre], Celine Vallot [aut]

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ChromSCape.pdf |ChromSCape.html✨
ChromSCape/json (API)
NEWS

# Install 'ChromSCape' in R:
install.packages('ChromSCape', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/vallotlab/chromscape/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On BioConductor:ChromSCape-1.15.0(bioc 3.20)ChromSCape-1.14.0(bioc 3.19)

bioconductor-package

82 exports 0.91 score 202 dependencies 1 mentions

Last updated 2 months agofrom:f4aa77176c

Exports:annotation_from_merged_peaksannotToCol2calculate_CNAcalculate_cyto_matcalculate_gain_or_losscalculate_logRatio_CNAchoose_cluster_scExpcolors_scExpcomparable_variablesCompareedgeRGLMCompareWilcoxconsensus_clustering_scExpcorrelation_and_hierarchical_clust_scExpcreate_project_foldercreate_scDataset_rawcreate_scExpdefine_featuredetect_samplesdifferential_activationdifferential_analysis_scExpenrich_TF_ChEA3_genesenrich_TF_ChEA3_scExpexclude_features_scExpfeature_annotation_scExpfilter_correlated_cell_scExpfilter_scExpfind_clusters_louvain_scExpfind_top_featuresgene_set_enrichment_analysis_scExpgenerate_analysisgenerate_coverage_tracksgenerate_reportget_cyto_featuresget_genomic_coordinatesget_most_variable_cytoget_pathway_mat_scExpgetExperimentNamesgetMainExperimenthas_genomic_coordinatesimport_scExpinter_correlation_scExpintra_correlation_scExplaunchAppnormalize_scExpnum_cell_after_cor_filt_scExpnum_cell_after_QC_filt_scExpnum_cell_before_cor_filt_scExpnum_cell_in_cluster_scExpnum_cell_scExpplot_cluster_consensus_scExpplot_correlation_PCA_scExpplot_coverage_BigWigplot_differential_summary_scExpplot_differential_volcano_scExpplot_distribution_scExpplot_gain_or_loss_barplotsplot_heatmap_scExpplot_inter_correlation_scExpplot_intra_correlation_scExpplot_most_contributing_featuresplot_percent_active_feature_scExpplot_pie_most_contributing_chrplot_reduced_dim_scExpplot_reduced_dim_scExp_CNAplot_top_TF_scExpplot_violin_feature_scExppreprocess_CPMpreprocess_feature_size_onlypreprocess_RPKMpreprocess_TFIDFpreprocess_TPMpreprocessing_filtering_and_reductionread_sparse_matrixrebin_matrixreduce_dims_scExprun_tsne_scExpsubsample_scExpsubset_bam_call_peakssummary_DAswapAltExp_sameColDatatable_enriched_genes_scExpwrapper_Signac_FeatureMatrix

Dependencies:abindALLanytimeaskpassbabelgenebase64encbatchelorbeachmatbeeswarmBHBiobaseBiocGenericsBiocIOBiocNeighborsBiocParallelBiocSingularBiostringsbitopsblusterbslibcachemCairocliclustercodetoolscolorRampscolorspacecolourpickercommonmarkConsensusClusterPluscoopcpp11crayoncrosstalkcurldata.tableDelayedArrayDelayedMatrixStatsdigestdplyrdqrngDTedgeRevaluatefansifarverfastmapFNNfontawesomeforcatsformatRfreshfsfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicAlignmentsGenomicRangesggbeeswarmggfittextgggenesggplot2ggrastrggrepelgluegridExtragridtextgtableherehighrhtmltoolshtmlwidgetshttpuvhttrigraphIRangesirlbaisobandjpegjquerylibjsonlitekableExtraKernSmoothknitrlabelinglambda.rlaterlatticelazyevallifecyclelimmalocfitmagrittrmarkdownMASSMatrixMatrixGenericsmatrixStatsmatrixTestsmemoisemetapodmgcvmimeminiUImsigdbrmunsellnlmeopensslpheatmappillarpkgconfigplotlypngpromisespurrrqsqualVR6raggRApiSerializerappdirsRColorBrewerRcppRcppAnnoyRcppEigenRcppHNSWRcppMLRcppParallelRcppProgressRcppTOMLRCurlResidualMatrixrestfulrreticulateRhtslibrjsonrlangrlistrmarkdownrprojrootRsamtoolsRSpectrarstudioapirsvdrtracklayerRtsneS4ArraysS4VectorssassScaledMatrixscalesscaterscranscuttleshadesshinyshinycssloadersshinydashboardshinydashboardPlusshinyFilesshinyhelpershinyjsshinyWidgetsSingleCellExperimentsitmosnowsourcetoolsSparseArraysparseMatrixStatsstatmodstringdiststringfishstringistringrSummarizedExperimentsvglitesyssystemfontstextshapingtibbletidyrtidyselecttinytexUCSC.utilsumaputf8uwotvctrsviporviridisviridisLitewaiterwithrxfunXMLxml2xtableXVectoryamlzlibbioc

ChromSCape

Pacome Prompsy, Pia Kirchmeier, Céline Vallot

Rendered fromvignette.Rmdusingknitr::rmarkdownon Jul 04 2024.

Last update: 2021-05-14
Started: 2020-09-10

Readme and manuals

Help Manual

Help pageTopics
Find nearest peaks of each gene and return refined annotationannotation_from_merged_peaks
annotToCol2annotToCol2
Helper binary column for anocol functionanocol_binary
Helper binary column for anocol functionanocol_categorical
Count bam files on interval to create count indexesbams_to_matrix_indexes
Count bed files on interval to create count indexesbeds_to_matrix_indexes
Estimate copy number alterations in cytobandscalculate_CNA
Calculate Fraction of reads in each cytobandscalculate_cyto_mat
Estimate the copy gains/loss of tumor vs normal based on log2-ratio of fraction of readscalculate_gain_or_loss
Calculate the log2-ratio of tumor vs normal fraction of reads in cytobandscalculate_logRatio_CNA
Calling MACS2 peak caller and merging resulting peakscall_macs2_merge_peaks
changeRangechangeRange
A data.frame with the number of targets of each TF in ChEA3CheA3_TF_nTargets
Check if matrix rownames are well formated and correct if neededcheck_correct_datamatrix
Choose a number of clusterschoose_cluster_scExp
Choose perplexity depending on number of cells for Tsnechoose_perplexity
Col2Hexcol2hex
Adding colors to cells & featurescolors_scExp
Combine two matrices and emit warning if no regions are in commoncombine_datamatrix
Run enrichment tests and combine into listcombine_enrichmentTests
Find comparable variable scExpcomparable_variables
Creates a summary table with the number of genes under- or overexpressed in each group and outputs several graphical representationsCompareedgeRGLM
CompareWilcoxCompareWilcox
Concatenate single-cell BED into clustersconcatenate_scBed_into_clusters
Wrapper to apply ConsensusClusterPlus to scExp objectconsensus_clustering_scExp
Correlation and hierarchical clusteringcorrelation_and_hierarchical_clust_scExp
Create a smoothed and normalized coverage track from a BAM file and given a bin GenomicRanges object (same as deepTools bamCoverage)count_coverage
Create ChromSCape project foldercreate_project_folder
Create a sample name matrixcreate_sample_name_mat
Create a simulated single cell datamatrix & cell annotationcreate_scDataset_raw
Wrapper to create the single cell experiment from count matrix and feature dataframecreate_scExp
Differential Analysis Custom in 'One vs One' modeDA_custom
Differential Analysis in 'One vs Rest' modeDA_one_vs_rest
Run differential analysis in Pairwise modeDA_pairwise
Define the features on which reads will be counteddefine_feature
Heuristic discovery of samples based on cell labelsdetect_samples
Find Differentialy Activated Features (One vs All)differential_activation
Runs differential analysis between cell clustersdifferential_analysis_scExp
distPearsondistPearson
Find the TF that are enriched in the differential genes using ChEA3 APIenrich_TF_ChEA3_genes
Find the TF that are enriched in the differential genes using ChEA3 databaseenrich_TF_ChEA3_scExp
enrichmentTestenrichmentTest
Remove specific features (CNA, repeats)exclude_features_scExp
Add gene annotations to featuresfeature_annotation_scExp
Filter lowly correlated cellsfilter_correlated_cell_scExp
Filter genes based on peak calling refined annotationfilter_genes_with_refined_peak_annotation
Filter cells and featuresfilter_scExp
Build SNN graph and find cluster using Louvain Algorithmfind_clusters_louvain_scExp
Find most covered featuresfind_top_features
Runs Gene Set Enrichment Analysis on genes associated with differential featuresgene_set_enrichment_analysis_scExp
Generate a complete ChromSCape analysisgenerate_analysis
Generate count matrixgenerate_count_matrix
Generate cell cluster pseudo-bulk coverage tracksgenerate_coverage_tracks
Generate feature namesgenerate_feature_names
From a ChromSCape analysis directory, generate an HTML report.generate_report
Get color dataframe from shiny::colorInputget_color_dataframe_from_input
Map features onto cytobandsget_cyto_features
Get SingleCellExperiment's genomic coordinatesget_genomic_coordinates
Retrieve the cytobands with the most variable fraction of readsget_most_variable_cyto
Get pathway matrixget_pathway_mat_scExp
Get experiment names from a SingleCellExperimentgetExperimentNames
Get Main experiment of a SingleCellExperimentgetMainExperiment
gg_fill_huegg_fill_hue
groupMatgroupMat
H1proportionH1proportion
Does SingleCellExperiment has genomic coordinates in features ?has_genomic_coordinates
hclustAnnotHeatmapPlothclustAnnotHeatmapPlot
Data.frame of chromosome length - hg38hg38.chromosomes
Data.frame of cytoBandlocation - hg38hg38.cytoBand
Data.frame of gene TSS - hg38hg38.GeneTSS
imageColimageCol
Import and count input files depending on their formatimport_count_input_files
Read single-cell matrix(ces) into scExpimport_scExp
Read index-peaks-barcodes trio files on interval to create count indexesindex_peaks_barcodes_to_matrix_indexes
Calculate inter correlation between cluster or samplesinter_correlation_scExp
Calculate intra correlation between cluster or samplesintra_correlation_scExp
Launch ChromSCapelaunchApp
Load and format MSIGdb pathways using msigdbr packageload_MSIGdb
Merge peak files from MACS2 peak callermerge_MACS2_peaks
Data.frame of chromosome length - mm10mm10.chromosomes
Data.frame of cytoBandlocation - mm10mm10.cytoBand
Data.frame of gene TSS - mm10mm10.GeneTSS
Normalize countsnormalize_scExp
Number of cells before & after correlation filteringnum_cell_after_cor_filt_scExp
Table of cells before / after QCnum_cell_after_QC_filt_scExp
Table of number of cells before correlation filteringnum_cell_before_cor_filt_scExp
Number of cells in each clusternum_cell_in_cluster_scExp
Table of cellsnum_cell_scExp
Run sparse PCA using irlba SVDpca_irlba_for_sparseMatrix
Plot cluster consensusplot_cluster_consensus_scExp
Plotting correlation of PCs with a variable of interestplot_correlation_PCA_scExp
Coverage plotplot_coverage_BigWig
Differential summary barplotplot_differential_summary_scExp
Volcano plot of differential featuresplot_differential_volcano_scExp
Plotting distribution of signalplot_distribution_scExp
Plot Gain or Loss of cytobands of the most variables cytobandsplot_gain_or_loss_barplots
Plot cell correlation heatmap with annotationsplot_heatmap_scExp
Violin plot of inter-correlation distribution between one or multiple groups and one reference groupplot_inter_correlation_scExp
Violin plot of intra-correlation distributionplot_intra_correlation_scExp
Plot Top/Bottom most contributing features to PCAplot_most_contributing_features
Barplot of the % of active cells for a given featuresplot_percent_active_feature_scExp
Pie chart of top contribution of chromosomes in the 100 most contributing features to PCA #'plot_pie_most_contributing_chr
Plot reduced dimensions (PCA, TSNE, UMAP)plot_reduced_dim_scExp
Plot UMAP colored by Gain or Loss of cytobandsplot_reduced_dim_scExp_CNA
Barplot of top TFs from ChEA3 TF enrichment analysisplot_top_TF_scExp
Violin plot of featuresplot_violin_feature_scExp
Preprocess scExp - Counts Per Million (CPM)preprocess_CPM
Preprocess scExp - size onlypreprocess_feature_size_only
Preprocess scExp - Read per Kilobase Per Million (RPKM)preprocess_RPKM
Preprocess scExp - TF-IDFpreprocess_TFIDF
Preprocess scExp - Transcripts per Million (TPM)preprocess_TPM
Preprocess and filter matrix annotation data project folder to SCEpreprocessing_filtering_and_reduction
Create a sparse count matrix from various format of input data.raw_counts_to_sparse_matrix
rawfile_ToBigWig : reads in BAM file and write out BigWig coverage file, normalized and smoothedrawfile_ToBigWig
Read a count matrix with three first columns (chr,start,end)read_count_mat_with_separated_chr_start_end
Read in one or multiple sparse matrices (10X format)read_sparse_matrix
Rebin Helper for rebin_matrix functionrebin_helper
Transforms a bins x cells count matrix into a larger bins x cells count matrix.rebin_matrix
Reduce dimension with batch correctionsreduce_dim_batch_correction
Reduce dimensions (PCA, TSNE, UMAP)reduce_dims_scExp
Remove chromosome M from scExprownamesremove_chr_M_fun
Remove non canonical chromosomes from scExpremove_non_canonical_fun
Resutls of hypergeometric gene set enrichment testresults_enrichmentTest
Retrieve Top and Bot most contributing features of PCAretrieve_top_bot_features_pca
Run pairwise testsrun_pairwise_tests
Run tsne on single cell experimentrun_tsne_scExp
A SingleCellExperiment outputed by ChromSCapescExp
Separate BAM files into cell cluster BAM filesseparate_BAM_into_clusters
Determine Count matrix separator ("tab" or ",")separator_count_mat
Smooth a vector of values with nb_bins left and righ valuessmoothBin
Subsample scExpsubsample_scExp
Peak calling on cell clusterssubset_bam_call_peaks
Summary of the differential analysissummary_DA
Swap main & alternative Experiments, with fixed colDataswapAltExp_sameColData
Creates table of enriched genes setstable_enriched_genes_scExp
Warning for differential_analysis_scExpwarning_DA
warning_filter_correlated_cell_scExpwarning_filter_correlated_cell_scExp
A warning helper for plot_reduced_dim_scExpwarning_plot_reduced_dim_scExp
Warning for raw_counts_to_sparse_matrixwarning_raw_counts_to_sparse_matrix
Wrapper around 'FeatureMatrix' function from Signac Packagewrapper_Signac_FeatureMatrix