Package: BatchQC 2.3.0

Jessica McClintock

BatchQC: Batch Effects Quality Control Software

Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. Moreover, BatchQC interactively applies multiple common batch effect approaches to the data and the user can quickly see the benefits of each method. BatchQC is developed as a Shiny App. The output is organized into multiple tabs and each tab features an important part of the batch effect analysis and visualization of the data. The BatchQC interface has the following analysis groups: Summary, Differential Expression, Median Correlations, Heatmaps, Circular Dendrogram, PCA Analysis, Shape, ComBat and SVA.

Authors:Jessica McClintock [aut, cre], W. Evan Johnson [aut], Solaiappan Manimaran [aut], Heather Selby [ctb], Claire Ruberman [ctb], Kwame Okrah [ctb], Hector Corrada Bravo [ctb], Michael Silverstein [ctb], Regan Conrad [ctb], Zhaorong Li [ctb], Evan Holmes [aut], Solomon Joseph [ctb]

BatchQC_2.3.0.tar.gz
BatchQC_2.3.0.zip(r-4.5)BatchQC_2.3.0.zip(r-4.4)BatchQC_2.1.2.zip(r-4.3)
BatchQC_2.3.0.tgz(r-4.4-any)BatchQC_2.1.2.tgz(r-4.3-any)
BatchQC_2.3.0.tar.gz(r-4.5-noble)BatchQC_2.3.0.tar.gz(r-4.4-noble)
BatchQC_2.3.0.tgz(r-4.4-emscripten)BatchQC_2.1.2.tgz(r-4.3-emscripten)
BatchQC.pdf |BatchQC.html
BatchQC/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/wejlab/batchqc/issues

Datasets:

On BioConductor:BatchQC-2.1.6(bioc 3.20)BatchQC-2.0.0(bioc 3.19)

batcheffectgraphandnetworkmicroarraynormalizationprincipalcomponentsequencingsoftwarevisualizationqualitycontrolrnaseqpreprocessingdifferentialexpressionimmunooncology

7.99 score 6 stars 51 scripts 244 downloads 29 exports 198 dependencies

Last updated 23 days agofrom:80296282b0. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 30 2024
R-4.5-winOKOct 30 2024
R-4.5-linuxOKOct 30 2024
R-4.4-winOKOct 30 2024
R-4.4-macOKOct 30 2024
R-4.3-winOKSep 19 2024
R-4.3-macOKSep 19 2024

Exports:batch_correctbatch_designBatchQCbatchqc_explained_variationbladder_data_uploadcolor_paletteconfound_metricscor_propscovariates_not_confoundedcramers_vDE_analyzedendrogram_alpha_numeric_checkdendrogram_color_palettedendrogram_plotterEV_plotterEV_tablegoodness_of_fit_DESeq2heatmap_num_to_char_converterheatmap_plotternormalize_SEPCA_plotterprocess_dendrogrampval_plotterpval_summaryratio_plotterstd_pearson_corr_coefsummarized_experimentvariation_ratiosvolcano_plot

Dependencies:abindannotateAnnotationDbiaskpassassortheadbackportsbase64encbeachmatBHBiobaseBiocGenericsBiocNeighborsBiocParallelBiocSingularBiostringsbitbit64bitopsblobblockmodelingblusterbriobroombslibcachemcallrcaToolscellrangerclicliprclustercodetoolscolorspacecommonmarkconflictedcpp11crayoncurldata.tableDBIdbplyrDelayedArraydescDESeq2diffobjdigestdplyrdqrngdtplyrEBSeqedgeRevaluatefansifarverfastmapfontawesomeforcatsformatRfsfutile.loggerfutile.optionsgarglegenefiltergenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggdendroggnewscaleggplot2gluegoogledrivegooglesheets4gplotsgtablegtoolshavenhighrhmshtmltoolshttpuvhttridsigraphIRangesirlbaisobandjquerylibjsonliteKEGGRESTKernSmoothknitrlabelinglambda.rlaterlatticelifecyclelimmalocfitlubridatemagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemetapodmgcvmimemodelrmunsellNCmiscnlmeopensslpheatmappillarpkgbuildpkgconfigpkgloadplogrplyrpngpraiseprettyunitsprocessxprogresspromisespspurrrR6raggrappdirsRColorBrewerRcppRcppArmadilloRcppEigenreaderreadrreadxlrematchrematch2reprexreshape2rlangrmarkdownrprojrootRSQLiterstudioapirsvdrvestS4ArraysS4VectorssassScaledMatrixscalesscranscuttleselectrshinyshinyjsshinythemesSingleCellExperimentsitmosnowsourcetoolsSparseArraystatmodstringistringrSummarizedExperimentsurvivalsvasyssystemfontstestthattextshapingtibbletidyrtidyselecttidyversetimechangetinytextzdbUCSC.utilsutf8uuidvctrsviridisLitevroomwaldowithrxfunXMLxml2xtableXVectoryamlzlibbioc

BatchQC Examples

Rendered fromBatchQC_examples.Rmdusingknitr::rmarkdownon Oct 30 2024.

Last update: 2024-07-16
Started: 2023-08-03

Introduction to BatchQC

Rendered fromBatchQC_Intro.Rmdusingknitr::rmarkdownon Oct 30 2024.

Last update: 2024-07-16
Started: 2022-04-29

Readme and manuals

Help Manual

Help pageTopics
Batch Correct This function allows you to Add batch corrected count matrix to the SE objectbatch_correct
This function allows you to make a batch design matrixbatch_design
Batch and Condition indicator for signature databatch_indicator
Run BatchQC shiny appBatchQC
Returns a list of explained variation by batch and condition combinationsbatchqc_explained_variation
Bladder data upload This function uploads the Bladder data set from the bladderbatch package. This dataset is from bladder cancer data with 22,283 different microarray gene expression data. It has 57 bladder samples with 3 metadata variables (batch, outcome and cancer). It contains 5 batches, 3 cancer types (cancer, biopsy, control), and 5 outcomes (Biopsy, mTCC, sTCC-CIS, sTCC+CIS, and Normal). Batch 1 contains only cancer, 2 has cancer and controls, 3 has only controls, 4 contains only biopsy, and 5 contains cancer and biopsybladder_data_upload
Helper function to save variables as factors if not already factorscheck_valid_input
Color palettecolor_palette
Combat Correction This function applies combat correction to your summarized experiment objectcombat_correction
Combat-Seq Correction This function applies combat-seq correction to your summarized experiment objectcombat_seq_correction
Combine std. Pearson correlation coefficient and Cramer's Vconfound_metrics
This function allows you to calculate correlation propertiescor_props
Returns list of covariates not confounded by batch; helper function for explained variation and for populating shiny app condition optionscovariates_not_confounded
This function allows you to calculate Cramer's Vcramers_v
Differential Expression AnalysisDE_analyze
Dendrogram alpha or numeric checkerdendrogram_alpha_numeric_check
Dendrogram color palettedendrogram_color_palette
Dendrogram Plotdendrogram_plotter
This function allows you to plot explained variationEV_plotter
EV Table Returns table with percent variation explained for specified number of genesEV_table
Helper function to get residualsget.res
This function calculates goodness-of-fit pvalues for all genes by looking at how the NB model by DESeq2 fit the datagoodness_of_fit_DESeq2
Heatmap numeric to character converterheatmap_num_to_char_converter
Heatmap Plotterheatmap_plotter
This function creates a histogram from the negative binomial goodness-of-fit pvalues.nb_histogram
This function determines the proportion of p-values below a specific value and compares to the previously determined threshold of 0.42 for extreme low values.nb_proportion
This function allows you to add normalized count matrix to the SE objectnormalize_SE
This function allows you to plot PCAPCA_plotter
This function formats the PCA plot using ggplotplot_data
Preprocess assay datapreprocess
Process Dendrogramprocess_dendrogram
Protein data with 39 protein expression levelsprotein_data
Batch and Condition indicator for protein expression dataprotein_sample_info
P-value Plotter This function allows you to plot p-values of explained variationpval_plotter
Returns summary table for p-values of explained variationpval_summary
This function allows you to plot ratios of explained variationratio_plotter
Signature data with 1600 gene expression levelssignature_data
Calculate a standardized Pearson correlation coefficientstd_pearson_corr_coef
This function creates a summarized experiment object from count and metadata files uploaded by the usersummarized_experiment
Creates Ratios of batch to variable variation statisticvariation_ratios
Volcano plotvolcano_plot