setup_prev: Setup selections when many samples.

In crossmeta: Cross Platform Meta-Analysis of Microarray Data

Description

Function is useful when number of samples makes manual selection with diff_expr error prone and time-consuming. This is often true for large clinical data sets.

Usage

setup_prev(eset, contrasts)

Arguments

eset	List containing one expression set with pData 'group' and 'pair' (optional) columns. Name of eset should be the GSE name.
contrasts	Character vector specifying contrasts to analyse. Each contrast must take the form "B-A" where both "B" and "A" are present in eset pData 'group' column. "B" is the treatment group and "A" is the control group.

Value

List containing necessary information for prev_anal parameter of diff_expr.

Examples

library(lydata)
library(Biobase)

# location of raw data
data_dir <- system.file("extdata", package = "lydata")

# load eset
gse_name  <- c("GSE34817")
eset <- load_raw(gse_name, data_dir)

# inspect pData of eset
# View(pData(eset$GSE34817))  # if using RStudio
head(pData(eset$GSE34817))    # otherwise

# get group info from pData (differs based on eset)
group <- pData(eset$GSE34817)$characteristics_ch1.1

# make group names concise and valid
group <- gsub("treatment: ", "", group)
group <- make.names(group)

# add group to eset pData
pData(eset$GSE34817)$group <- group

# setup selections
sel <- setup_prev(eset, contrasts = "LY-DMSO")

# run differential expression analysis
# anal <- diff_expr(eset, data_dir, prev_anal = sel)

crossmeta documentation built on Nov. 8, 2020, 8 p.m.