plot_PCA {BioNERO} | R Documentation

## Plot Principal Component Analysis (PCA) of samples

### Description

Plot Principal Component Analysis (PCA) of samples

### Usage

    plot_PCA(exp, metadata, log_trans = FALSE, PCs = "1x2", size = 2)

### Arguments

`exp` | A gene expression data frame with genes in row names and samples in column names or a 'SummarizedExperiment' object.
`metadata` | A data frame containing sample names in row names and sample annotation in the first column. Ignored if 'exp' is a 'SummarizedExperiment' object, since the function will extract colData.
`log_trans` | Logical. If TRUE, the expression data frame will be log transformed with log2(exp+1).
`PCs` | Principal components to be plotted on the x-axis and y-axis, respectively. One of "1x2", "1x3" or "2x3. Default is "1x2".
`size` | Numeric indicating the point size. Default is 2.

### Value

A ggplot object with the PCA plot.

### Author(s)

Fabricio Almeida-Silva

### See Also

`ggplot`

### Examples

    data(zma.se)
    plot_PCA(zma.se, log_trans = TRUE)

[Package BioNERO version 1.0.4 Index]