HTSFilter-package {HTSFilter} | R Documentation |
This package implements a filtering procedure for replicated transcriptome sequencing data based on a global Jaccard similarity index in order to identify genes with low, constant levels of expression across one or more experimental conditions.
Package: | HTSFilter |
Type: | Package |
Version: | 1.19.3 |
Date: | 2017-11-16 |
License: | Artistic-2.0 |
LazyLoad: | yes |
Andrea Rau, Melina Gallopin, Gilles Celeux, and Florence Jaffrezic
Maintainer: Andrea Rau <andrea.rau@inra.fr>
R. Bourgon, R. Gentleman, and W. Huber. (2010) Independent filtering increases detection power for high- throughput experiments. PNAS 107(21):9546-9551.
P. Jaccard (1901). Etude comparative de la distribution orale dans une portion des Alpes et des Jura. Bulletin de la Societe Vaudoise des Sciences Naturelles, 37:547-549.
A. Rau, M. Gallopin, G. Celeux, F. Jaffrezic (2013). Data-based filtering for replicated high-throughput transcriptome sequencing experiments. Bioinformatics, doi: 10.1093/bioinformatics/btt350.
library(Biobase) data("sultan") conds <- pData(sultan)$cell.line ######################################################################## ## Matrix or data.frame ######################################################################## filter <- HTSFilter(exprs(sultan), conds, s.len=25, plot=FALSE) ######################################################################## ## DGEExact ######################################################################## library(edgeR) dge <- DGEList(counts=exprs(sultan), group=conds) dge <- calcNormFactors(dge) dge <- estimateCommonDisp(dge) dge <- estimateTagwiseDisp(dge) et <- exactTest(dge) et <- HTSFilter(et, DGEList=dge, s.len=25, plot=FALSE)$filteredData ## topTags(et) ######################################################################## ## DESeq2 ######################################################################## library(DESeq2) conds <- gsub(" ", ".", conds) dds <- DESeqDataSetFromMatrix(countData = exprs(sultan), colData = data.frame(cell.line = conds), design = ~ cell.line) ## Not run: ## ## dds <- DESeq(dds) ## filter <- HTSFilter(dds, s.len=25, plot=FALSE)$filteredData ## class(filter) ## res <- results(filter, independentFiltering=FALSE)