ExposureGLM: Exposure Generalized Linear Model

In rvalieris/signeR: Empirical Bayesian approach to mutational signature discovery

Exposure Generalized Linear Model

Description

Fits a GLM to exposure data, with a given sample feature as the target of the model.

Usage

## S4 method for signature 'SignExp,numeric'
ExposureGLM(Exposures, feature, max_instances=200,
     cutoff_pvalue=0.05,quant=0.5, plot_to_file=FALSE,
     file="ExposureGLM_plot.pdf",colors=TRUE,...)

Arguments

Exposures	A SignExp object returned by signeR function or a matrix of exposures (with signatures in rows and a column for each sample).
feature	numeric feature associated with each sample, such as age, weight or the expression of a gene.
max_instances	Maximum number of the exposure matrix instances to be analyzed. If the number of available E instances is bigger than this parameter, a subset of those will be randomly selected for analysis.
cutoff_pvalue	threshold for p-values quantile for signatures to be considered as significant on the model.
quant	p-values quantile used to evaluate if signatures are significant. Default is 0.5, meaning that median p-values are adopted.
plot_to_file	Whether to save plots to the file parameter. Default is FALSE.
file	Output file to export p-values boxplot and scatterplots showing the correlations of exposures and the provided feature.
colors	Boolean variable, if TRUE p-values boxplots of significantly correlated signatures will be colored in green, cutoff line will be colored in red and line segments showing the transformed p-value quantile used for significance evaluation will be colored in blue. Otherwise the plot will be black & white.
...	additional parameters for test algorithm defined by the method parameter.

Value

A list with the following items:

Significance	boolean array with one entry for each signature, indicating whether it shows a significant contribution to the model.
Stats	matrix of model statistics, with one line for each signature.
Pvalues	vector of p-values used for significance evaluation.

Examples

# assuming signatures is the return value of signeR()


my_feature <- rnorm(30,100,20)+signatures$SignExposures@Exp[1,,1]
EGlm <- ExposureGLM(signatures$SignExposures, feature=my_feature)

# see also
vignette(package="signeR")

rvalieris/signeR documentation built on April 20, 2024, 2:08 p.m.