run_pca_and_plot_correlations: Compute principal component analysis (PCA) and plot...

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

Compute principal component analysis (PCA) and plot correlations

Description

Identify principal components (PCs) of normalized gene counts and correlate these PCs to interesting covariates. This function wraps 'correlate_and_plot()' to visualize, with a heatmap, the relationship between PCs and covariates that meet a false discovery rate (FDR) threshold and return a list of significant covariates.

Usage

run_pca_and_plot_correlations(
  normalized_counts,
  clean_metadata,
  scaled = TRUE,
  percent_p_value_cutoff = 1,
  correlation_type = "pearson",
  plot_covariates_vs_pca = TRUE,
  maximum_fdr = 0.1
)

Arguments

normalized_counts	A counts data frame normalized by CQN, TMM, or another preferred method, with genes as rownames.
clean_metadata	A data frame with sample identifiers as rownames and variables as factors or numeric as determined by "sageseqr::clean_covariates()".
scaled	Defaults to TRUE. Variables scaled to have unit variance before the analysis takes place.
percent_p_value_cutoff	The p-value threshold in percent.
correlation_type	Allowed values are "pearson", "spearman" and "kendall". See "psych::corr.test(method)".
plot_covariates_vs_pca	Defaults to TRUE. If false, no plot is returned.
maximum_fdr	Maximum allowable false discovery rate (FDR). Defaults to 0.1.

Value

A list.

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

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

• effects_significant_vars - A vector correlation values.

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