grandR (version 0.2.6)

PairwiseDESeq2: Perform Wald tests for differential expression

Description

Apply DESeq2 for comparisons defined in a contrast matrix, requires the DESeq2 package.

Usage

PairwiseDESeq2(
  data,
  name.prefix = mode,
  contrasts,
  separate = FALSE,
  mode = "total",
  slot = "count",
  normalization = NULL,
  logFC = FALSE,
  genes = NULL,
  verbose = FALSE
)

Value

a new grandR object including a new analysis table. The columns of the new analysis table are

"M"

the base mean

"S"

the log2FoldChange divided by lfcSE

"P"

the Wald test P value

"Q"

same as P but Benjamini-Hochberg multiple testing corrected

"LFC"

the log2 fold change (only with the logFC parameter set to TRUE)

Arguments

data

the grandR object

name.prefix

the prefix for the new analysis name; a dot and the column names of the contrast matrix are appended; can be NULL (then only the contrast matrix names are used)

contrasts

contrast matrix that defines all pairwise comparisons, generated using GetContrasts

separate

model overdispersion separately for all pairwise comparison (TRUE), or fit a single model per gene, and extract contrasts (FALSE)

mode

compute LFCs for "total", "new", or "old" RNA

slot

which slot to use (should be a count slot, not normalized values)

normalization

normalize on "total", "new", or "old" (see details)

logFC

compute and add the log2 fold change as well

genes

restrict analysis to these genes; NULL means all genes

verbose

print status messages?

Details

DESeq2 by default performs size factor normalization. When computing differential expression of new RNA, it might be sensible to normalize w.r.t. to total RNA, i.e. use the size factors computed from total RNA instead of computed from new RNA. This can be accomplished by setting mode to "new", and normalization to "total"!

Normalization can also be a mode.slot! Importantly, do not specify a slot containing normalized values, but specify a slot of unnormalized values (which are used to compute the size factors for normalization!) Can also be a numeric vector of size factors with the same length as the data as columns. Then each value is divided by the corresponding size factor entry.

See Also

LFC,GetContrasts

Examples

# \donttest{
sars <- ReadGRAND(system.file("extdata", "sars.tsv.gz", package = "grandR"),
                  design=c(Design$Condition,Design$dur.4sU,Design$Replicate))
sars <- subset(sars,Coldata(sars,Design$dur.4sU)==2)
sars<-PairwiseDESeq2(sars,mode="total",
                              contrasts=GetContrasts(sars,contrast=c("Condition","Mock")))
sars<-PairwiseDESeq2(sars,mode="new",normalization="total",
                              contrasts=GetContrasts(sars,contrast=c("Condition","Mock")))
head(GetAnalysisTable(sars,column="Q"))
# }