Automatize the use of results() for multiple comparisons

This function will extract the output of DESeq2::results() and DESeq2::lfcShrink() for multiple comparison using:

degComps(dds, combs = NULL, contrast = NULL, alpha = 0.05,
  skip = FALSE, type = "normal", pairs = FALSE, fdr = "default")

Arguments

dds	DESeq2::DESeqDataSet obcject.
combs	Optional vector indicating the coefficients or columns fom colData(dds) to create group comparisons.
contrast	Optional vector to specify contrast. See DESeq2::results().
alpha	Numeric value used in independent filtering in DESeq2::results().
skip	Boolean to indicate whether skip shrinkage. For instance when it comes from LRT method.
type	Type of shrinkage estimator. See DESeq2::lfcShrink().
pairs	Boolean to indicate whether create all comparisons or only use the coefficient already created from DESeq2::resultsNames().
fdr	type of fdr correction. default is FDR value, lfdr-stat is for local FDR using the statistics of the test, lfdr-pvalue is for local FDR using the p-value of the test

Value

DEGSet with unSrunken and Srunken results.

Details

• coefficients

• contrast

• Multiple columns in colData that match coefficients

• Multiple columns in colData to create all possible contrasts

Examples

library(DESeq2)
dds <- makeExampleDESeqDataSet(betaSD=1)
colData(dds)[["treatment"]] <- sample(colData(dds)[["condition"]], 12)
  design(dds) <-  ~ condition + treatment
dds <- DESeq(dds)
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
res <- degComps(dds, combs = c("condition", 2),
                contrast = list("treatment_B_vs_A", c("condition", "A", "B")))
#> Doing 3 element(s).
#> Doing results() for each element.
#> Doing lcfSrink() for each element.
#> using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
#> 
#> Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
#> See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
#> Reference: https://doi.org/10.1093/bioinformatics/bty895
#> using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
#> 
#> Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
#> See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
#> Reference: https://doi.org/10.1093/bioinformatics/bty895
#> using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
#> 
#> Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
#> See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
#> Reference: https://doi.org/10.1093/bioinformatics/bty895
res <- degComps(dds,contrast = list("treatment_B_vs_A"),
                fdr="lfdr-stat")
#> Doing 1 element(s).
#> Doing results() for each element.
#> Step 1... determine cutoff point
#> Step 2... estimate parameters of null distribution and eta0
#> Step 3... compute p-values and estimate empirical PDF/CDF
#> Step 4... compute q-values and local fdr
#> 
#> Doing lcfSrink() for each element.
#> using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
#> 
#> Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
#> See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
#> Reference: https://doi.org/10.1093/bioinformatics/bty895