Filter DESeqDataSet results for use with seed matching
and counting functions.
The filtering criteria are:
Filter out genes that are not expressed or counted at all: baseMean = 0 & pvalue = NA & log2FoldChange = NA
Filter out genes that are expressed, but there is not difference across groups: log2FoldChange = 0
Filter out genes with extreme outliers: pvalue = NA and padj = NA
Filter out genes that have been excluded by independent filtering. padj = NA
Filter results by the fdr.cutoff
Filter the results by the log2FoldChange
Filter the results by the baseMean
Remove NA gene_ids and log2FoldChange values
Usage
filter_deseq(
res,
fdr.cutoff = 1,
fc.cutoff = 0,
rm.na.name = FALSE,
rm.na.log2fc = FALSE,
baseMean.cutoff = 0
)Arguments
- res
The DESEQ2 results as a data frame
- fdr.cutoff
The false discovery rate cutoff to use.
- fc.cutoff
The fold change cutoff to use. The absolute value will be used as the cutoff and values greater-than-or-equal-to will be kept.
- rm.na.name
Remove na values from the gene_name column
- rm.na.log2fc
Remove na values from the log2FoldChange column
- baseMean.cutoff
The minimum baseMean expression cutoff
Examples
if (FALSE) { # interactive()
# Load test data
get_example_data("sirna")
sirna.data = load_example_data("sirna")
res <- sirna.data$Schlegel_2022_Ttr_D1_30mkg
# Filter DESeq2 results for SeedMatchR
res = filter_deseq(res, fdr.cutoff=1, fc.cutoff=0, rm.na.log2fc = TRUE)
}