q.pca: Visualize gene expression similarity using principal...

In QuESTr: Constructing Quantitative Environment Sensor using Transcriptomes

Visualize gene expression similarity using principal coordinate analysis

Description

Visualize gene expression similarity using principal coordinate analysis

Usage

q.pca(x, y, method = "linear", lower.thr = 0, n.gene = ncol(x), size = 1)

Arguments

x	A data matrix (row: samples, col: genes).
y	A vector of an environment in which the samples were collected.
method	A string to specify the method of regression for calculating R-squared values. "linear" (default), "quadratic" or "cubic" regression model can be specified.
lower.thr	The lower threshold of R-squared value to be indicated in a PCA plot (default: 0).
n.gene	The number of candidate genes for QuEST model to be indicated in a PCA plot (default: ncol(x)).
size	The size of symbols in a PCA plot (default: 1).

Value

A PCA plot

Author(s)

Takahiko Koizumi

Examples

data(Pinus)
train <- q.clean(Pinus$train)
target <- Pinus$target
q.pca(train, target)

QuESTr documentation built on March 18, 2022, 5:09 p.m.