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, minPC = 5,
      correlation = "kendall", addCovDen = TRUE, 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.
`minPC` | 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.
`addCovDen` | boolean. Whether to add the covariates dendograme to the plot to see covariates relationship. It will show `degCorCov()` dendograme on top of the columns of the heatmap..
`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. * means pvalue < 0.05. Only variables with FDR value lower than the cutoff are colored. 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 1.16.0 Index]