pairwise_compare: Generate statistics associated with pairwise differential expression

Description

When provided with an ExpressionSet, comparisons are made between control and perturbation samples.

Usage

pairwise_compare( eset, control_perturb_col = "cmap", control="control", perturb="perturbation")
pairwise_compare_limma( eset, control_perturb_col = "cmap", control="control", perturb="perturbation", limma.index=2)

Arguments

eset

ExpressionSet with all array data for a single instance, plus metadata on which arrays are perturbation and control.

control_perturb_col

Column name in phenoData of eset where control/perturbation designations are stored.

control

String designating control samples in the control_perturb_col column.

perturb

String designating perturbation samples in the control_perturb_col column.

limma.index

Integer specifying the index of the parameter estimate for which we to extract t and other statistics. The default corresponds to a two-class comparison with the standard parameterization. The function assumes that there was no missing data, so that test for all genes were performed on the same sample size.

Value

log_fc: Log fold-change between perturbed and control data. (A positive value denotes higher expression in the perturbed samples.)
z: When at least one condition has two or more samples, the pairwise_compare_limma functions uses lmFit, eBayes and topTable to compare the two classes and compute an (uncorrected) limma p-value. The pairwise_compare functions performs a standard t-test instead. For ease of comparison across instances with different numbers of samples, either p-value is converted to the standard normal scale. The result is reported here. As for fc, positive values denote higher expression in perturbed samples.
p: When at least one condition has two or more samples, the two-tailed standard (pairwise_compare) or limma p-value (pairwise_compare_limma), as computed by eBayes. Note that this p-value can also be computed from z, via pnorm (doubling for two tails).