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run_baySeq <- function(counts, conds, cutoff, n, runID) {
cl <- makeCluster(2, "SOCK")
# Preparing variables for baySeq run
NR1 = length(conds[conds == "N"])
NR2 = length(conds[conds == "T"])
groups <- list(NDE = c(rep(1, NR1 + NR2)), DE = c(rep(1,
NR1), rep(2, NR2)))
data <- as.matrix(counts)
CD <- new("countData", data = data, replicates = conds, groups = groups)
CD@libsizes <- getLibsizes(CD)
CDP.NBML <- getPriors.NB(CD, samplesize = 10000, estimation = "QL",
cl = cl)
CDPost.NBML <- getLikelihoods.NB(CDP.NBML, pET = "BIC", cl = cl)
all <- topCounts(CDPost.NBML, group = "DE", number = nrow(counts))
good <- all[all$FDR < cutoff, ]
# Preparing results to return
result <- new("Result")
result@data <- all[order(as.numeric(all$FDR)), ]
result@id <- rownames(result@data)
result@pval <- result@data$FDR
stopCluster(cl)
return(result)
}
run_baySeq_uqn <- function(counts, conds, cutoff, n, runID) {
cl <- makeCluster(2, "SOCK")
# Preparing variables for baySeq run
NR1 = length(conds[conds == "N"])
NR2 = length(conds[conds == "T"])
groups <- list(NDE = c(rep(1, NR1 + NR2)), DE = c(rep(1,
NR1), rep(2, NR2)))
CD <- new("countData", data = as.matrix(UQNnormalization(counts)$normCounts),
replicates = conds, groups = groups)
# CD@libsizes <- getLibsizes(CD);
meanLib <- mean(apply(CD@data, 2, sum))
libsizes <- rep(meanLib, times = dim(CD@data)[2])
names(libsizes) <- colnames(CD@data)
CD@libsizes <- libsizes
CDP.NBML <- getPriors.NB(CD, samplesize = 10000, estimation = "QL",
cl = cl)
CDPost.NBML <- getLikelihoods.NB(CDP.NBML, pET = "BIC", cl = cl)
all <- topCounts(CDPost.NBML, group = "DE", number = nrow(counts))
good <- all[all$FDR < cutoff, ]
# Preparing results to return
result <- new("Result")
result@data <- all[order(as.numeric(all$FDR)), ]
result@id <- rownames(result@data)
result@pval <- result@data$FDR
stopCluster(cl)
return(result)
}
run_baySeq_Mode <- function(counts, conds, cutoff, n, runID,
winSize) {
cl <- makeCluster(2, "SOCK")
# Preparing variables for baySeq run
NR1 = length(conds[conds == "N"])
NR2 = length(conds[conds == "T"])
groups <- list(NDE = c(rep(1, NR1 + NR2)), DE = c(rep(1,
NR1), rep(2, NR2)))
CD <- new("countData", data = as.matrix(normalizeData(counts,
conds, runID, winSize)$normCounts), replicates = conds, groups = groups)
# CD@libsizes <- getLibsizes(CD);
meanLib <- mean(apply(CD@data, 2, sum))
libsizes <- rep(meanLib, times = dim(CD@data)[2])
names(libsizes) <- colnames(CD@data)
CD@libsizes <- libsizes
CDP.NBML <- getPriors.NB(CD, samplesize = 10000, estimation = "QL",
cl = cl)
CDPost.NBML <- getLikelihoods.NB(CDP.NBML, pET = "BIC", cl = cl)
all <- topCounts(CDPost.NBML, group = "DE", number = nrow(counts))
good <- all[all$FDR < cutoff, ]
# Preparing results to return
result <- new("Result")
result@data <- all[order(as.numeric(all$FDR)), ]
result@id <- rownames(result@data)
result@pval <- result@data$FDR
stopCluster(cl)
return(result)
}
run_baySeq_nde <- function(counts, DElist, conds, cutoff, n,
runID) {
cl <- makeCluster(2, "SOCK")
# Preparing variables for baySeq run
NR1 = length(conds[conds == "N"])
NR2 = length(conds[conds == "T"])
groups <- list(NDE = c(rep(1, NR1 + NR2)), DE = c(rep(1,
NR1), rep(2, NR2)))
CD <- new("countData", data = as.matrix(normalizeNDE(counts,
DElist, runID)$normCounts), replicates = conds, groups = groups)
# CD@libsizes <- getLibsizes(CD);
meanLib <- mean(apply(CD@data, 2, sum))
libsizes <- rep(meanLib, times = dim(CD@data)[2])
names(libsizes) <- colnames(CD@data)
CD@libsizes <- libsizes
CDP.NBML <- getPriors.NB(CD, samplesize = 10000, estimation = "QL",
cl = cl)
CDPost.NBML <- getLikelihoods.NB(CDP.NBML, pET = "BIC", cl = cl)
all <- topCounts(CDPost.NBML, group = "DE", number = nrow(counts))
good <- all[all$FDR < cutoff, ]
# Preparing results to return
result <- new("Result")
result@data <- all[order(as.numeric(all$FDR)), ]
result@id <- rownames(result@data)
result@pval <- result@data$FDR
stopCluster(cl)
return(result)
}
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