plot_pcs_with_covariates: Explore relationship between principal components (PCs) and...

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

Explore relationship between principal components (PCs) and covariates

Description

Explore relationship between principal components (PCs) and covariates

Usage

plot_pcs_with_covariates(correlation_values, note_threshold = NULL)

Arguments

correlation_values	A data frame that includes the p-value of the correlation of a covariate to a PC as 'pvalue'. Additionally, the data frame should include a logical column 'significant_correlations' to indicate whether the FDR threshold is met for that variable.
note_threshold	A customized string to note method used to adjust p-values for multiple comparisons. Defaults to NULL.

Examples

## Not run: 
correlation_values <- data.frame(
  compare = "PC1 (23.16%)",
  covariates = "batch",
  pvalue = 0.56519736,
  r = "0.05729811",
  significant_correlations = FALSE
  )
note_threshold <- "FDR <= 0.1"

## End(Not run)

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