computeBs: Fit a linear model to impute a GReX for a certain gene
In AffiXcan: A Functional Approach To Impute Genetically Regulated Expression

Description Usage Arguments Value Examples

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

1	computeBs(assocRegions, pca, expr, covariates = NULL)

`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`	Optrional; a data.frame with covariates values for the population structure where the columns are the PCs and the rows are the individual IIDs; default is NULL

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

if (interactive()) {
data(exprMatrix)
data(trainingCovariates)
data(regionAssoc)

tbaPaths <- system.file("extdata","training.tba.toydata.rds",
package="AffiXcan")
regionsCount <- overlookRegions(tbaPaths)
assay <- "values"

sampleNames <- colnames(exprMatrix)
nSamples <- length(sampleNames)
sampGroups <- subsetKFold(k=5, n=nSamples)
for (i in seq(length(sampGroups))) {
     sampGroups[[i]] <- colnames(exprMatrix)[sampGroups[[i]]]
}

testingSamples <- sampGroups[[1]]
trainingSamples <- sampleNames[!sampleNames %in% testingSamples]

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

cov <- trainingCovariates
cov <- cov[rownames(cov) %in% trainingSamples,]

expr <- SummarizedExperiment::assays(exprMatrix)[[assay]]
expr <- expr[,colnames(expr) %in% trainingSamples]
expr <- t(as.data.frame(expr))

expressedRegions <- as.vector(unique(regionAssoc$EXPRESSED_REGION))
assocList <- BiocParallel::bplapply(X=expressedRegions, FUN=assoc2list,
regionAssoc)
names(assocList) <- expressedRegions
assocRegions <- assocList[[1]]

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