Calculate gene significance for a given group of genes

Usage

gene_significance(
  exp,
  metadata,
  metadata_cols = NULL,
  genes = NULL,
  alpha = 0.05,
  cor_method = "pearson",
  min_cor = 0.2,
  use_abs = TRUE
)

Arguments

exp: A gene expression data frame with genes in row names and samples in column names or a `SummarizedExperiment` object.
metadata: A data frame containing sample names in row names and sample annotation in the first column. Ignored if `exp` is a `SummarizedExperiment` object, since the function will extract colData.
metadata_cols: A vector (either numeric or character) indicating which columns should be extracted from column metadata if exp is a `SummarizedExperiment` object. The vector can contain column indices (numeric) or column names (character). By default, all columns are used.
genes: Character vector of genes to be correlated with traits. If not given, all genes in `exp` will be considered.
alpha: Significance level. Default is 0.05.
cor_method: Method to calculate correlation. One of 'pearson', 'spearman' or 'kendall'. Default is 'spearman'.
min_cor: Minimum correlation coefficient. Default is 0.2.
use_abs: Logical indicating whether to filter by correlation using absolute value or not. If TRUE, a min_cor of say 0.2 would keep all correlations above 0.2 and below -0.2. Default is TRUE.

Value

A data frame with correlation and correlation p-values for each pair of gene and trait, with the following variables:

gene: Factor, gene ID.
trait: Factor, trait name. Each trait corresponds to a variable of the sample metadata (if numeric) or levels of a variable (if categorical).
cor: Numeric, correlation.
pvalue: Numeric, correlation P-values.
group: Character, name of the metadata variable.

Author

Fabricio Almeida-Silva

Examples

data(filt.se)
gs <- gene_significance(filt.se)