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Detect tissue heterogeneity in expression profiles with gene sets

Bioconductor version: Release (3.19)

BioQC performs quality control of high-throughput expression data based on tissue gene signatures. It can detect tissue heterogeneity in gene expression data. The core algorithm is a Wilcoxon-Mann-Whitney test that is optimised for high performance.

Author: Jitao David Zhang [cre, aut], Laura Badi [aut], Gregor Sturm [aut], Roland Ambs [aut], Iakov Davydov [aut]

Maintainer: Jitao David Zhang <jitao_david.zhang at>

Citation (from within R, enter citation("BioQC")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

BioQC Algorithm: Speeding up the Wilcoxon-Mann-Whitney Test HTML R Script
BioQC-benchmark: Testing Efficiency, Sensitivity and Specificity of BioQC on simulated and real-world data HTML R Script
BioQC-kidney: The kidney expression example HTML R Script
BioQC: Detect tissue heterogeneity in gene expression data HTML R Script
Comparing the Wilcoxon-Mann-Whitney to alternative statistical tests HTML R Script
Using BioQC with signed genesets HTML R Script
Reference Manual PDF


biocViews GeneExpression, GeneSetEnrichment, QualityControl, Software, StatisticalMethod
Version 1.32.0
In Bioconductor since BioC 3.3 (R-3.3) (8 years)
License GPL (>=3) + file LICENSE
Depends R (>= 3.5.0), Biobase
Imports edgeR, Rcpp, methods, stats, utils
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Suggests testthat, knitr, rmarkdown, lattice, latticeExtra, rbenchmark, gplots, gridExtra,, hgu133plus2.db, ggplot2, reshape2, plyr, ineq, covr, limma, RColorBrewer
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Follow Installation instructions to use this package in your R session.

Source Package BioQC_1.32.0.tar.gz
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macOS Binary (x86_64) BioQC_1.32.0.tgz
macOS Binary (arm64) BioQC_1.32.0.tgz
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