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process_data.R
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process_data.R
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process_ROSMAP <- function(study_id = "ROS") {
load("data/ROSMAP_RNA_workspace.RData")
ROSMAP_RNA_workspace$metadata <-
ROSMAP_RNA_workspace$metadata[
which(ROSMAP_RNA_workspace$metadata$Study == study_id),
]
selectedCovariates <- c("Study", "AOD", "PMI","MSex", "Race", "RIN",
"Batch", "CeradScore", "Education")
ret <- list()
CERAD <- list(
AD_definite = c(1, 4),
AD_likely = c(1, 2, 4),
AD_possible = c(1, 2, 3, 4)
)
CERAD_CTRL <- 4
for(cerad_cur in names(CERAD)) {
sample_id <- ROSMAP_RNA_workspace$metadata$Sample_ID
expr <-
ROSMAP_RNA_workspace$virusLevelCounts[, sample_id]
otherCovariates <-
ROSMAP_RNA_workspace$metadata[
match(colnames(expr),
ROSMAP_RNA_workspace$metadata$Sample_ID),
selectedCovariates
]
selected <- otherCovariates$CeradScore %in% CERAD[[cerad_cur]]
otherCovariates <- otherCovariates[selected,]
otherCovariates$MSex <- as.factor(otherCovariates$MSex)
otherCovariates$Race <- as.factor(otherCovariates$Race)
otherCovariates$Study <- as.factor(otherCovariates$Study)
otherCovariates$Batch <- as.factor(otherCovariates$Batch)
otherCovariates$status <- ifelse(
otherCovariates$CeradScore == CERAD_CTRL,
"Control",
"Case"
)
otherCovariates$status <- factor(otherCovariates$status,
levels = c("Control", "Case"))
otherCovariates$CeradScore <- as.factor(otherCovariates$CeradScore)
otherCovariates$AOD <- as.numeric(
gsub(x = otherCovariates$AOD,
pattern = "+",
replacement = "",
fixed = TRUE))
otherCovariates$PMI <- as.numeric(otherCovariates$PMI)
otherCovariates$RIN <- as.numeric(otherCovariates$RIN)
otherCovariates$Education <- as.numeric(otherCovariates$Education)
virMat <- expr[,selected]
fullReadsPerSample <- as.numeric(
ROSMAP_RNA_workspace$metadata$TotalReads[
match(colnames(virMat), ROSMAP_RNA_workspace$metadata$Sample_ID)]
)
ret[[cerad_cur]] <- list(
virMat = virMat,
covariates = droplevels.data.frame(otherCovariates),
fullReadsPerSample = fullReadsPerSample
)
}
ret
}
process_MSBB <- function(tissue) {
load("data/MSBB_RNA_workspace.RData")
ret <- list()
for(subset_i in 1:length(MSBB_RNA_workspace$pathologySampleSets)){
tissue_samples <-
MSBB_RNA_workspace$metadata$Sample_ID[
which(MSBB_RNA_workspace$metadata$Region == tissue)]
case_selected <-
intersect(
MSBB_RNA_workspace$pathologySampleSets[[subset_i]],
tissue_samples
)
caseMat <- MSBB_RNA_workspace$virusLevelCounts[,case_selected]
control_selected <-
intersect(
MSBB_RNA_workspace$controlSampleSets,
tissue_samples)
controlMat <- MSBB_RNA_workspace$virusLevelCounts[,control_selected]
selected_samples <-
match(c(colnames(controlMat),colnames(caseMat)),
MSBB_RNA_workspace$metadata$Sample_ID)
selected_covariates <-
c("RIN", "AOD", "Race", "Sex", "Batch", "PMI", "NP.1")
otherCovariates <- MSBB_RNA_workspace$metadata[selected_samples,
selected_covariates]
otherCovariates$AOD <- gsub(x = otherCovariates$AOD,
pattern = "+",
replacement = "",
fixed = TRUE)
otherCovariates$AOD <- as.numeric(otherCovariates$AOD)
otherCovariates$RIN <- as.numeric(otherCovariates$RIN)
otherCovariates$PMI <- as.numeric(otherCovariates$PMI)
otherCovariates$Race <- as.factor(otherCovariates$Race)
otherCovariates$Sex <- as.factor(otherCovariates$Sex)
otherCovariates$Batch <- as.factor(otherCovariates$Batch)
otherCovariates$status <- factor(x = c(rep("Control", ncol(controlMat)),
rep("Case", ncol(caseMat))),
levels = c("Control","Case"))
virMat <- cbind(controlMat,caseMat)
fullReadsPerSample <-
as.numeric(
MSBB_RNA_workspace$metadata$TotalReads[
match(colnames(virMat),
MSBB_RNA_workspace$metadata$Sample_ID)]
)
cur_AD_level <- names(MSBB_RNA_workspace$pathologySampleSets)[[subset_i]]
ret[[cur_AD_level]] <- list(
virMat = virMat,
covariates = otherCovariates,
fullReadsPerSample = fullReadsPerSample
)
}
ret
}
process_MAYO <- function() {
load("data/MAYO_TCX_RNA_workspace.RData")
# Viral level summary -----------------------------------------------------
exprMat <-
MAYO_RNA_workspace$virusLevelCounts[,
MAYO_RNA_workspace$metadata$Sample_ID]
selected_samples <-
match(
colnames(exprMat),
MAYO_RNA_workspace$metadata$Sample_ID)
selected_covariates <- c("RIN", "AOD", "Sex",
"Source", "Flowcell", "Diagnosis")
otherCovariates <-
droplevels.data.frame(
MAYO_RNA_workspace$metadata[selected_covariates])
selected_samples <-
otherCovariates$Diagnosis %in% c("AD", "Control")
otherCovariates <- otherCovariates[selected_samples,]
otherCovariates$status <-
ifelse(otherCovariates$Diagnosis == "AD", "Case", "Control")
otherCovariates$status <-
factor(otherCovariates$status, levels = c("Control", "Case"))
otherCovariates$AOD <-
as.numeric(
gsub(x = otherCovariates$AOD,
pattern = "_or_above",
replacement = "",
fixed = TRUE))
otherCovariates$RIN <- as.numeric(otherCovariates$RIN)
otherCovariates$Sex <- as.factor(otherCovariates$Sex)
otherCovariates$Diagnosis <- as.factor(
gsub(otherCovariates$Diagnosis,
pattern = " ",
replacement = "_"))
otherCovariates$Source <- as.factor(otherCovariates$Source)
otherCovariates$Flowcell <- as.factor(otherCovariates$Flowcell)
virMat <- exprMat
fullReadsPerSample <-
as.numeric(
MAYO_RNA_workspace$metadata$TotalReads[
match(
colnames(virMat),
MAYO_RNA_workspace$metadata$Sample_ID)
]
)
list(
AD_definite =
list(
virMat = virMat[,selected_samples],
covariates = otherCovariates,
fullReadsPerSample = fullReadsPerSample[selected_samples]
)
)
}