degCovariates {DEGreport}R Documentation

Find correlation between pcs and covariates

Description

This function will calculate the pcs using prcomp function, and correlate categorical and numerical variables from metadata.

Usage

degCovariates(counts, metadata, fdr = 0.1, scale = FALSE, min_pc_pct = 5,
  correlation = "spearman", plot = TRUE)

Arguments

counts

normalized counts matrix

metadata

data.frame with samples metadata.

fdr

numeric value to use as cutoff to determine the minimum fdr to consider significant correlations between pcs and covariates.

scale

boolean to determine wether counts matrix should be scaled for pca. default FALSE.

min_pc_pct

numeric value that will be used as cutoff to select only pcs that explain more variability than this.

correlation

character determining the method for the correlation between pcs and covariates.

plot

Whether to plot or not the correlation matrix.

Value

: list: a) significantCovars, covariates with FDR below the cutoff. b) plot, heatmap of the correlation found. c) corMatrix, correlation, p-value, FDR values for each covariate and PCA pais d) effectsSignificantcovars: that is PCs correlation between covariate and PCs, e) pcsMatrix: PCs loading for each sample

Author(s)

: Lorena Pantano, Kenneth Daily and Thanneer Malai Perumal

Examples

data(humanGender)
library(DESeq2)
idx <- c(1:10, 75:85)
dse <- DESeqDataSetFromMatrix(assays(humanGender)[[1]][1:1000, idx],
  colData(humanGender)[idx,], design=~group)
res <- degCovariates(log2(counts(dse)+0.5),
  colData(dse))
res$plot
res$scatterPlot[[1]]

[Package DEGreport version 1.14.1 Index]