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 1.2.0 Index]