affiXcanBs: Fit linear models and compute ANOVA p-values

In alussana/AffiXcan: A Functional Approach To Impute Genetically Regulated Expression

Description

Fit linear models and compute ANOVA p-values

Usage

affiXcanBs(
  exprMatrix,
  assay,
  regionAssoc,
  pca,
  cov = NULL,
  BPPARAM = bpparam(),
  trainingSamples
)

Arguments

exprMatrix	A SummarizedExperiment object containing expression data
assay	A string with the name of the object in SummarizedExperiment::assays(exprMatrix) that contains expression values
regionAssoc	A data.frame with the associations between regulatory regions and expressed genes, and with colnames = c("REGULATORY_REGION", "EXPRESSED_REGION")
pca	A list, which is the returningObject$pca from affiXcanPca()
cov	Optional; a data.frame with covariates values for the population structure where the columns are the PCs and the rows are the individual IDs; default is NULL
BPPARAM	A BiocParallelParam object. Default is bpparam(). For details on BiocParallelParam virtual base class see browseVignettes("BiocParallel")
trainingSamples	A vector of strings. The identifiers (e.g. row names of MultiAssayExperiment objects from tbaPaths) of the samples that have to be considered in the training phase, and not used for the cross-validation

Value

A list containing lists named as the EXPRESSED_REGIONS found in the param regionAssoc. Each of these lists contain 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 p-value 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

• corrected.p.val: The p-value of the model, corrected for multiple testing with benjamini-hochberg procedure

Examples

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)

bs <- affiXcanBs(exprMatrix=exprMatrix, assay=assay, regionAssoc=regionAssoc,
pca=pca, cov=trainingCovariates, trainingSamples=trainingSamples)
}

alussana/AffiXcan documentation built on Oct. 20, 2020, 12:08 a.m.