easyDifferentialGeneCoexpression {easyDifferentialGeneCoexpression}

Function that computes the differential coexpression of a list of probesets in a specific dataset and returns the most significant pairs

Description

Function that computes the differential coexpression of a list of probesets in a specific dataset and returns the most significant pairs

Usage

easyDifferentialGeneCoexpression(
  list_of_probesets_to_select,
  GSE_code,
  featureNameToDiscriminateConditions,
  firstConditionName,
  secondConditionName,
  batchCorrection = TRUE,
  verbose = FALSE
)

Arguments

list_of_probesets_to_select

list of probesets for which the differential coexpression should be computed

GSE_code

GEO accession code of the dataset to analyze

featureNameToDiscriminateConditions

name of the feature of the dataset that contains the two conditions to investigate

firstConditionName

name of the first condition in the feature to discriminate (for example, "healthy")

secondConditionName

name of the second condition in the feature to discriminate (for example, "cancer")

batchCorrection

says if the script should perform the batch correction with limma::removeBatchEffect() or not

verbose

prints all the intermediate message to standard output or not

Value

a dataframe containing the significantly differentially co-expressed pairs of genes

Examples

probesetList <- c("200738_s_at", "217356_s_at", "206686_at")
verboseFlag <- "TRUE"
batchCorrection <- "TRUE"
signDiffCoexpressGenePairs <- easyDifferentialGeneCoexpression(probesetList, 
"GSE3268", "description", "Normal", "Tumor", verboseFlag)