BioQC
Detect tissue heterogeneity in expression profiles with gene sets
Bioconductor version: 3.23 · Package version: 1.40.0
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.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("BioQC")Details
| Maintainer | Jitao David Zhang <jitao_david.zhang@roche.com> |
| Author | Jitao David Zhang [cre, aut], Laura Badi [aut], Gregor Sturm [aut], Roland Ambs [aut], Iakov Davydov [aut] |
| License | GPL (>=3) + file LICENSE |
| URL | https://accio.github.io/BioQC |
| Bug Reports | https://accio.github.io/BioQC/issues |
| Downloads rank | 431 |
| Source branch | RELEASE_3_23 |
| biocViews | GeneExpression, GeneSetEnrichment, QualityControl, Software, StatisticalMethod |
Documentation
- BioQC Algorithm: Speeding up the Wilcoxon-Mann-Whitney Test
- BioQC-benchmark: Testing Efficiency, Sensitivity and Specificity of BioQC on simulated and real-world data
- BioQC-kidney: The kidney expression example
- BioQC: Detect tissue heterogeneity in gene expression data
- Comparing the Wilcoxon-Mann-Whitney to alternative statistical tests
- Using BioQC with signed genesets
Download
Dependencies
Depends: R (>= 3.5.0), Biobase
Imports: edgeR, Rcpp, methods, stats, utils
LinkingTo: Rcpp
Suggests: testthat, knitr, rmarkdown, lattice, latticeExtra, rbenchmark, gplots, gridExtra, org.Hs.eg.db, hgu133plus2.db, ggplot2, reshape2, plyr, ineq, covr, limma, RColorBrewer