cor_expression: Calculate cell line response with gene expression

In dwassarman/cellpanelr: Analyze Data from Cell Line Panels

Calculate cell line response with gene expression

Description

For each of the 19.177 genes present in the data_expression data set, perform Spearman's correlation between given response data and RNA expression levels across cell lines. The non-parametric Spearman's correlation is chosen, rather than Pearson's correlation, because it does not assume a linear relationship between the two variables.

Usage

cor_expression(data, response, ids = "depmap_id", fdr = 0.05)

Arguments

data	A tibble.
response	Column containing response values
ids	Column containing DepMap IDs of cell lines
fdr	False discovery rate. Number between 0 and 1 representing the likelihood that a gene predicted to be significant is actually a false- positive.

Value

A tibble with 19,177 rows and 4 columns. Each row contains the correlation values for a single gene.

gene

Hugo gene symbol

rho

Spearman's correlation coefficient

p.value

Probability that the null hypothesis is true (there is no relationship between gene expression and cell line response)

significant

Whether the correlation is deemed significant after multiple hypothesis correction with the given false discovery rate

Examples

# Setup example data set
df <- tibble::tibble(
  CellLine = c("LS513", "253-J", "NIH:OVCAR-3"),
  DepMapID = c("ACH-000007", "ACH-000011", "ACH-000001"),
  logIC50 = c(-2.8, -4.04, -6.23)
)

cor_expression(
  data = df,
  response = "logIC50",
  ids = "DepMapID"
)

dwassarman/cellpanelr documentation built on Jan. 3, 2023, 8:27 a.m.