This function is a shortcut for first calling PairwiseDESeq2 and then LFC.
Usage
Pairwise(
data,
name.prefix = mode,
contrasts,
LFC.fun = lfc::PsiLFC,
slot = "count",
mode = "total",
normalization = mode,
genes = NULL,
verbose = FALSE
)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
- LFC.fun
function to compute log fold changes (default: PsiLFC, other viable option: NormLFC)
- slot
the slot of the grandR object to take the data from; should contain counts!
- mode
compute LFCs for "total", "new", or "old" RNA
- normalization
normalize on "total", "new", or "old" (see details)
- genes
restrict analysis to these genes; NULL means all genes
- verbose
print status messages?
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
Details
Both PsiLFC and NormLFC) by default perform normalization by subtracting the median log2 fold change from all log2 fold changes. When computing LFCs of new RNA, it might be sensible to normalize w.r.t. to total RNA, i.e. subtract the median log2 fold change of total RNA from all the log2 fold change of 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.