computeBs {AffiXcan}R Documentation

Fit a linear model to impute a GReX for a certain gene

Description

Fit a linear model to impute a GReX for a certain gene

Usage

computeBs(assocRegions, pca, expr, covariates)

Arguments

assocRegions

A data.frame with the associations between regulatory regions and one expressed gene, and with colnames = c("REGULATORY_REGION", "EXPRESSED_REGION")

pca

The returningObject$pca from affiXcanTrain()

expr

A matrix containing the real total expression values, where the columns are genes and the rows are individual IIDs

covariates

A data.frame with covariates values for the population structure where the columns are the PCs and the rows are the individual IIDs

Value

A list containing three objects:

coefficients: An object containing the coefficients of the principal components used in the model, completely similar to the "coefficients" from the results of lm()

pval: The uncorrected anova pvalue of the model, retrieved from anova(model, modelReduced, test="F")$'Pr(>F)'[2]

r.sq: The coefficient of determination between the real total expression values and the imputed GReX, retrived from summary(model)$r.squared

Examples

if (interactive()) {
trainingTbaPaths <- system.file("extdata","training.tba.toydata.rds",
package="AffiXcan")

data(exprMatrix)
data(regionAssoc)
data(trainingCovariates)

assay <- "values"

assocRegions <- regionAssoc[regionAssoc$EXPRESSED_REGION==
"ENSG00000256377.1",]

regionsCount <- overlookRegions(trainingTbaPaths)

pca <- affiXcanPca(tbaPaths=trainingTbaPaths, varExplained=80, scale=TRUE,
regionsCount=regionsCount)

expr <- SummarizedExperiment::assays(exprMatrix)[[assay]]
expr <- t(as.data.frame(expr))

bs <- computeBs(assocRegions=assocRegions, pca=pca, expr=expr,
covariates=trainingCovariates)
}

[Package AffiXcan version 1.0.0 Index]