getDESeqResults: Get DESeq2 results using reduced dispersion matrices

In BRGenomics: Tools for the Efficient Analysis of High-Resolution Genomics Data

Description

This function calls DESeq2::DESeq and DESeq2::results on a pre-existing DESeqDataSet object and returns a DESeqResults table for one or more pairwise comparisons. However, unlike a standard call to DESeq2::results using the contrast argument, this function subsets the dataset so that DESeq2 only estimates dispersion for the samples being compared, and not for all samples present.

Usage

getDESeqResults(
  dds,
  contrast.numer,
  contrast.denom,
  comparisons = NULL,
  sizeFactors = NULL,
  alpha = 0.1,
  lfcShrink = FALSE,
  args.DESeq = NULL,
  args.results = NULL,
  args.lfcShrink = NULL,
  ncores = getOption("mc.cores", 2L),
  quiet = FALSE
)

Arguments

dds	A DESeqDataSet object, produced using either getDESeqDataSet from this package or DESeqDataSet from DESeq2. If dds was not created using getDESeqDataSet, dds must be made with design = ~condition such that a unique condition level exists for each sample/treatment condition.
contrast.numer	A string naming the condition to use as the numerator in the DESeq2 comparison, typically the perturbative condition.
contrast.denom	A string naming the condition to use as the denominator in the DESeq2 comparison, typically the control condition.
comparisons	As an optional alternative to supplying a single contrast.numer and contrast.denom, users can supply a list of character vectors containing numerator-denominator pairs, e.g. list(c("B", "A"), c("C", "A"), c("C", "B")). comparisons can also be a dataframe in which each row is a comparison, the first column contains the numerators, and the second column contains the denominators.
sizeFactors	A vector containing DESeq2 sizeFactors to apply to each sample. Each sample's readcounts are divided by its respective DESeq2 sizeFactor. A warning will be generated if the DESeqDataSet already contains sizeFactors, and the previous sizeFactors will be over-written.
alpha	The significance threshold passed to DESeqResults, which is used for independent filtering of results (see DESeq2 documentation).
lfcShrink	Logical indicating if log2FoldChanges and their standard errors should be shrunk using lfcShrink. LFC shrinkage is very useful for making fold-change values meaningful, as low-expression/high variance genes are given low fold-changes. Set to FALSE by default.
args.DESeq	Additional arguments passed to DESeq, given as a list of argument-value pairs, e.g. list(fitType = "local", useT = TRUE). All arguments given here will be passed to DESeq except for object and parallel. If no arguments are given, all defaults will be used.
args.results	Additional arguments passed to DESeq2::results, given as a list of argument-value pairs, e.g. list(altHypothesis = "greater", lfcThreshold = 1.5). All arguments given here will be passed to results except for object, contrast, alpha, and parallel. If no arguments are given, all defaults will be used.
args.lfcShrink	Additional arguments passed to lfcShrink, given as a list of argument-value pairs. All arguments given here will be passed to lfcShrink except for dds, coef, contrast, and parallel. If no arguments are given, all defaults will be used.
ncores	The number of cores to use for parallel processing. Multicore processing is only used if more than one comparison is being made (i.e. argument comparisons is used), and the number of cores utilized will not be greater than the number of comparisons being performed.
quiet	If TRUE, all output messages from calls to DESeq and results will be suppressed, although passing option quiet in args.DESeq will supersede this option for the call to DESeq.

Value

For a single comparison, the output is the DESeqResults result table. If a comparisons is used to make multiple comparisons, the output is a named list of DESeqResults objects, with elements named following the pattern "X_vs_Y", where X is the name of the numerator condition, and Y is the name of the denominator condition.

Errors when ncores > 1

If this function returns an error, set ncores = 1. Whether or not this occurs can depend on whether users are using alternative BLAS libraries (e.g. OpenBLAS or Apple's Accelerate framework) and/or how DESeq2 was installed. This is because some DESeq2 functions (e.g. nbinomWaldTest) use C code that can be compiled to use parallelization, and this conflicts with our use of process forking (via the parallel package) when ncores > 1.

Author(s)

Mike DeBerardine

See Also

getDESeqDataSet, DESeq2::results

Examples

#--------------------------------------------------#
# getDESeqDataSet
#--------------------------------------------------#
suppressPackageStartupMessages(require(DESeq2))
data("PROseq") # import included PROseq data
data("txs_dm6_chr4") # import included transcripts

# divide PROseq data into 6 toy datasets
ps_a_rep1 <- PROseq[seq(1, length(PROseq), 6)]
ps_b_rep1 <- PROseq[seq(2, length(PROseq), 6)]
ps_c_rep1 <- PROseq[seq(3, length(PROseq), 6)]

ps_a_rep2 <- PROseq[seq(4, length(PROseq), 6)]
ps_b_rep2 <- PROseq[seq(5, length(PROseq), 6)]
ps_c_rep2 <- PROseq[seq(6, length(PROseq), 6)]

ps_list <- list(A_rep1 = ps_a_rep1, A_rep2 = ps_a_rep2,
                B_rep1 = ps_b_rep1, B_rep2 = ps_b_rep2,
                C_rep1 = ps_c_rep1, C_rep2 = ps_c_rep2)

# make flawed dataset (ranges in txs_dm6_chr4 not disjoint)
#    this means there is double-counting
# also using discontinuous gene regions, as gene_ids are repeated
dds <- getDESeqDataSet(ps_list,
                       txs_dm6_chr4,
                       gene_names = txs_dm6_chr4$gene_id,
                       ncores = 1)

dds

#--------------------------------------------------#
# getDESeqResults
#--------------------------------------------------#

res <- getDESeqResults(dds, "B", "A")

res

reslist <- getDESeqResults(dds, comparisons = list(c("B", "A"), c("C", "A")),
                           ncores = 1)
names(reslist)

reslist$B_vs_A

# or using a dataframe
reslist <- getDESeqResults(dds, comparisons = data.frame(num = c("B", "C"),
                                                         den = c("A", "A")),
                           ncores = 1)
reslist$B_vs_A

BRGenomics documentation built on Nov. 8, 2020, 8:03 p.m.