matPCs {scReClassify} | R Documentation

## matPCs function

### Description

Performs PCA on a given matrix and returns a dimension reduced matrix which captures at least 80% (default) of overall variability.

### Usage

    matPCs(data, assay = NULL, percentVar = 0.8)

### Arguments

`data` | An expression matrix or a SingleCellExperiment object.
`assay` | An assay to select if `data` is a SingleCellExperiment object
`percentVar` | The percentage of variance threshold. This is used to select number of Principal Components.

### Details

This function performs PCA to reduce the dimension of the gene expression matrix limited from 10 to 20 PCs.

### Value

Dimensionally reduced matrix.

### Author(s)

Pengyi Yang, Taiyun Kim

### Examples

    data("gse87795_subset_sce")

    mat.expr <- gse87795_subset_sce

    mat.pc <- matPCs(mat.expr, assay = "logNorm")

    # to capture at least 70% of overall variability in the dataset,
    mat.dim.reduct.70 <- matPCs(mat.expr, assay = "logNorm", 0.7)

[Package scReClassify version 1.0.0 Index]