Generate statistics associated with pairwise differential expression from RNAseq count data
Description
When provided with an CountDataSet, comparisons are made between control and perturbation samples.
Usage
1 2  pairwise_DESeq(cds, vst, control_perturb_col = "condition",
control="control", perturb="perturbation", try.hard=FALSE)

Arguments
cds 
CountDataSet with all count data for a single instance, plus metadata on which samples are perturbation and control. 
vst 
Matrix of variancestabilized count data that must include columns with colnames matching the sampleNames of the cds object. The vst matrix may contain additional columns / samples, which will be ignored. 
control_perturb_col 
Column name in 
control 
String designating control samples in the

perturb 
String designating perturbation samples in the

try.hard 
Logical parameter indicating how to proceed when DESeq's parametric estimation of the dispersion parameter fails. If set to FALSE (default), the function exits with an error. If set to TRUE, the function will try a nonparametric approach instead. 
Value
The function returns a data frame with the following columns:
log_fc 
Moderated log2 foldchange between perturbed and control data. (A positive value denotes higher expression in the perturbed samples.) The change was calculated from the (mean) counts after variance stabilizing transformation. Please consult the DESeq vignette for details on the transformation. 
z 
For ease of comparison across instances with different
numbers of samples, the (uncorrected) DESEq pvalue is converted to
the standard normal scale. The result is reported here. As for

p 
pvalue for differential expression calculated by the

Note
To use this function, please install the suggested Bioconductor package 'DESeq'.