limmaModeClassifier: Interaction mode classification with matrix of counts and...

In taylo5jm/interactions: Classification of gene expression profile sets generated from combination stimuli

Description

Interaction mode classification with matrix of counts and data.frames for genes and metadata

Usage

limmaModeClassifier(counts, genes, metadata, design, DRUG_COMBINATION = TRUE,
  ci = 0.99, null = FALSE, ...)

Arguments

counts	numeric matrix of counts where rows are genes and columns are libraries
genes	data.frame of gene metadata
metadata	data.frame with metadata retrieved for 4 conditions
design	design matrix constructed with model.matrix
ci	Confidence level for confidence intervals generated by limma
null	Boolean TRUE if null distribution should be simulated by permuting columns of counts matrix
drug_combination	Boolean TRUE when signal x, signal y, and signal x + y are conditions 2, 3, and 4 respectively

Value

list of interaction modes & classes, outcome vectors, EList (voom), lmFit() object, eBayes() object, design matrix and summary table

taylo5jm/interactions documentation built on May 31, 2019, 3:57 a.m.