estimate.expression.cna.correlation {iDOS}

estimate.expression.cna.correlation

Description

Estimate subtype specific correlation between mRNA and CNA profiles

Usage

estimate.expression.cna.correlation(
  exp.data = NULL, 
  cna.data.log2 = NULL, 
  corr.threshold = 0.3, 
  corr.direction = "two.sided", 
  subtypes.metadata = NULL, 
  feature.ids = NULL, 
  cancer.type = NULL, 
  data.dir = NULL, 
  graphs.dir = NULL
  )

Arguments

exp.data

Feature by sample mRNA abundance matrix

cna.data.log2

Feature by sample CNA log ratio matrix

corr.threshold

Threshold for Spearman's Rho to consider a feature as candidate driver

corr.direction

Whether to include positively (greater), negatively (less) or both (two.sided) correlated features. Defaults to two.sided

subtypes.metadata

Subtypes metadata list of lists. Must contain at least one subtype specific samples using list subtype.samples.list. If no subtypes are present, specify list element "All" with all samples

feature.ids

Vector of features to be used to estimate correlation

cancer.type

Name of the cancer type or dataset

data.dir

Path to output directory where mRNA and CNA correlation statistics will be stored

graphs.dir

Path to graphs directory

Value

A list of lists containing correlated features per cancer subtype

Author(s)

Syed Haider

Examples

# load test data
x <- get.test.data(data.types = c("mRNA.T", "CNA"));

# temporary output directory
tmp.output.dir <- tempdir();

# estimate mRNA and CNA correlation
correlated.features <- estimate.expression.cna.correlation(
  exp.data = x$mRNA.T$BLCA, 
  cna.data.log2 = x$CNA.log2$BLCA, 
  corr.threshold = 0.3, 
  corr.direction = "two.sided", 
  subtypes.metadata = list(
    "subtype.samples.list" = list("All" = colnames(x$mRNA.T$BLCA))
    ), 
  feature.ids = rownames(x$mRNA.T$BLCA), 
  cancer.type = "BLCA", 
  data.dir = paste(tmp.output.dir, "/data/BLCA/", sep = ""),
  graphs.dir = paste(tmp.output.dir, "/graphs/BLCA/", sep = "")
  );