correlate_and_plot: Calculate correlation between covariates and principal...

In Sage-Bionetworks/sageseqr: Identify differentially expressed genes counts data

Calculate correlation between covariates and principal components (PCs)

Description

Identify covariates that significantly, as defined by a false discovery rate (FDR) threshold, correlate with PCs computed from gene counts. This function wraps 'plot_pcs_with_covariates()' to return a heatmap to visualize this relationship.

Usage

correlate_and_plot(
  principal_components,
  clean_metadata,
  correlation_type,
  weights = NULL,
  to_plot = FALSE,
  exclude_missing_data = NULL,
  maximum_fdr = 0.1
)

Arguments

principal_components	Center, scaled and rotated principal components matrix.
clean_metadata	A data frame with sample identifiers as rownames and variables as factors or numeric as determined by "sageseqr::clean_covariates()".
correlation_type	Allowed values are "pearson", "spearman" and "kendall". See "psych::corr.test(method)".
weights	A numeric vector of the proportion of variance explained (PVE). Defaults to NULL.
to_plot	Logical indicating whether correlation values should be plotted. Defaults to FALSE.
exclude_missing_data	A vector with column names to exclude from 'clean_metadata'.
maximum_fdr	Maximum allowable false discovery rate (FDR). Defaults to 0.1.

Value

A list.

• plot - A customized heatmap of significant covariates and PCs correlated.

• significant_covariates - A vector of covariates where correlation p-value meets the FDR threshold.

• effects_significant_vars - A vector correlation values.

Sage-Bionetworks/sageseqr documentation built on June 13, 2024, 2:11 p.m.