calculate_mds_pca: MDS or PCA

In szymczak-lab/QCnormSE: Quality Control of Normalized Gene Expression Data

MDS or PCA

Description

Performs dimensionality reduction using multi-dimensional scaling (MDS) or principal component analysis (PCA) on the sample level.

Usage

calculate_mds_pca(
  se,
  assay = 1,
  method = "pca",
  dist = "euclidean",
  center = TRUE,
  scale = FALSE
)

Arguments

se	RangedSummarizedExperiment-class object
assay	Character or integer. Name or number of assay containing expression data to be used for dimensionality reduction.
method	Method to use: "pca" (default), "mds".
dist	Distance method to be used for MDS: see method argument in dist function.
center	Logical. Should the data be centered by mean? (default: TRUE).
scale	Logical. Should the data be scaled by standard deviation? (default: FALSE).

Value

list with components scores (data.frame with components of PCA or MDS analysis) and var.explained (vector with explained variance; only for PCA).

Examples

data("se.gene")

## PCA
res.pca = calculate_mds_pca(se = se.gene,
                            method = "pca")

## MDS
res.mds = calculate_mds_pca(se = se.gene,
                            method = "mds")

szymczak-lab/QCnormSE documentation built on March 25, 2023, 1:05 p.m.