other/TCGA.r
In cbg-ethz/nempi: Inferring unobserved perturbations from gene expression data
library(snowfall)
library(TCGAbiolinks)
if (is.null(types)) {
types <- TCGAbiolinks:::getGDCprojects()$project_id
## special "FM-AD"
dont <- paste0(unique(gsub("-.*", "", types)), "-")
dont <- dont[-grep("TCGA", dont)]
samplenr <- matrix(NA, length(types), 2)
rownames(samplenr) <- types
donr <- TRUE
snrcount <- 0
} else {
dont <- "AGCT"
donr <- FALSE
}
sizemat <- matrix(0, 1, 2)
colnames(sizemat) <- c("Tumor", "Normal")
rownames(sizemat) <- ""
path <- "mutclust/"
for (type in types) {
if (donr) {
snrcount <- snrcount + 1
}
if (length(grep(paste(dont, collapse = "|"), type)) > 0) { next() }
print(type)
if (file.exists(paste0(path, type, "_final.rda")) & !newmut & !newllr & !newsave) {
load(paste0(path, type, "_final.rda"))
} else {
summ <- TCGAbiolinks:::getProjectSummary(type)
library(SummarizedExperiment)
## get methylation:
if (file.exists(paste0(path, type, "_meth.rda"))) {
load(paste0(path, type, "_meth.rda"))
} else {
data <- GDCquery(project = paste(type, sep = ""),
sample.type = "Primary Tumor",
data.category = "DNA methylation",
data.type = "Methylation beta value",
legacy = TRUE
)
GDCdownload(data)
data <- GDCprepare(data, summarizedExperiment = 0)
## map methy sites to genes:
library(methyAnalysis)
if (is.data.frame(data)) {
meth2 <- as.matrix(data[, -(1:3)])
rownames(meth2) <- gsub(";.*", "", data[, 1])
} else {
meth <- data@rowRanges
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
probe2gene <- annotateDMRInfo(meth, 'TxDb.Hsapiens.UCSC.hg19.knownGene')
meth2 <- assay(data)
rownames(meth2) <- probe2gene$sigDMRInfo@elementMetadata@listData$GeneSymbol
}
meth <- meth2
meth <- meth[which(apply(meth, 1, function(x) return(any(is.na(x)))) == FALSE), ]
methm <- meth[which(duplicated(rownames(meth)) == FALSE), ]
count <- 0
for (i in which(duplicated(rownames(meth)) == FALSE)) {
count <- count + 1
methm[count, ] <- apply(meth[which(rownames(meth) %in% rownames(methm)[i]), , drop = FALSE], 2, median)
}
meth <- methm
meth <- meth[order(rownames(meth)), order(colnames(meth))]
colnames(meth) <- gsub("A$", "", lapply(strsplit(colnames(meth), "-"), function(x) { y <- x[1:4]; y <- paste(y, collapse = "-"); return(y) }))
methm <- meth[, which(duplicated(colnames(meth)) == FALSE)]
for (i in which(duplicated(colnames(meth)) == TRUE)) {
j <- which(colnames(methm) == colnames(meth)[i])
methm[, j] <- apply(meth[, which(colnames(meth) %in% colnames(methm)[i]), drop = FALSE], 2, median)
}
meth <- methm
meth <- meth[order(rownames(meth)), order(colnames(meth))]
save(meth, file = paste0(path, type, "_meth.rda"))
}
print("meth done")
## get copy number variation:
if (file.exists(paste0(path, type, "_cnv.rda"))) {
load(paste0(path, type, "_cnv.rda"))
} else {
data <- getGistic(gsub("TCGA-", "", type), type = "thresholded")
## data <- GDCquery(project = paste(type, sep = ""),
## sample.type = "Primary solid Tumor",
## data.category = "Copy Number Variation",
## data.type = "Copy Number Segment",
## )
## GDCdownload(data)
## data <- GDCprepare(data)
cnv <- data[, -(1:3)]
cnv <- apply(cnv, c(1,2), as.numeric)
rownames(cnv) <- data[, 1]
colnames(cnv) <- gsub("A$", "", lapply(strsplit(colnames(cnv), "-"), function(x) { y <- x[1:4]; y <- paste(y, collapse = "-"); return(y) }))
cnv <- cnv[order(rownames(cnv)), order(colnames(cnv))]
save(cnv, file = paste0(path, type, "_cnv.rda"))
}
print("cnv done")
## get expression
if (file.exists(paste0(path, type, "_query.rda"))) {
load(paste0(path, type, "_query.rda"))
} else {
if (length(grep("-AML$|-LAML$", type)) > 0) {
sampletype <- c("Primary Blood Derived Cancer - Peripheral Blood", "Solid Tissue Normal")
} else {
sampletype <- c("Primary solid Tumor", "Solid Tissue Normal")
}
data <- GDCquery(project = paste(type, sep = ""),
sample.type = sampletype,
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "HTSeq - Counts"
)
GDCdownload(data)
if (is.null(data)) {
print("is null")
next()
}
data <- GDCprepare(data)
save(data, file = paste0(path, type, "_query.rda"))
}
## process expression data:
D <- assay(data)
class <- data@colData@listData$definition
print("gene expression done")
## get mutation
if (file.exists(paste0(path, type, "_mut.rda"))) {
load(paste0(path, type, "_mut.rda"))
} else {
type2 <- gsub(paste(paste0(unique(gsub("-.*", "", types)), "-"), collapse = "|"), "", type)
library(data.table)
GDCquery_Maf(tumor = type2, save.csv = TRUE, pipeline = "varscan2") # mutation file
GDCquery_Maf(tumor = type2, save.csv = TRUE, pipeline = "muse") # mutation file
GDCquery_Maf(tumor = type2, save.csv = TRUE, pipeline = "somaticsniper") # mutation file
GDCquery_Maf(tumor = type2, save.csv = TRUE, pipeline = "mutect2") # mutation file
mut <- list()
count <- 1
type3 <- gsub("-", "\\.", type)
for (i in list.files("GDCdata")) {
if (length(grep(type3, i)) > 0 & length(grep("csv", i)) > 0) {
mut[[count]] <- fread(paste0("GDCdata/", i))
count <- count + 1
}
}
save(mut, file = paste0(path, type, "_mut.rda"))
}
## clinical
if (file.exists(paste0(path, type, "_clin.rda"))) {
load(paste0(path, type, "_clin.rda"))
} else {
clinical <- GDCquery_clinic(project = type, type = "clinical")
save(clinical, file = paste0(path, type, "_clin.rda"))
}
## process mutation data: (https://cancer.sanger.ac.uk/cosmic/help/mutation/overview)
if (file.exists(paste0(path, type, "_mut0.rda")) & !newmut) {
load(paste0(path, type, "_mut0.rda"))
} else {
n <- nmut # try something to get all patients with at least one mutation
library(snowfall)
countSamples <- function(i, mut.org, genes, mut.mat, coln) {
i <- which(rownames(mut.mat) %in% genes[i])
tmp <- mut.org[which(mut.org$Hugo_Symbol %in% rownames(mut.mat)[i]), coln]
tmp2 <- mut.mat[i, ]
tmp2[which(colnames(mut.mat) %in% tmp)] <- 1
return(tmp2)
}
typeSamples <- function(i, mut.org, genes, type.mat, coln, coln2) {
i <- which(rownames(type.mat) %in% genes[i])
tmp <- mut.org[which(mut.org$Hugo_Symbol %in% rownames(type.mat)[i]), coln]
tmp3 <- mut.org[which(mut.org$Hugo_Symbol %in% rownames(type.mat)[i]), coln2]
tmp2 <- type.mat[i, ]
tmp2[which(colnames(type.mat) %in% tmp)] <- tmp3
return(tmp2)
}
biggl <- list()
for (i in length(mut)) {
mutation <- mut[[i]]
biggl[[i]] <- mutation$Hugo_Symbol
}
freq <- sort(table(unlist(biggl)), decreasing = TRUE)
if (n == 0) {
allsub <- names(freq)
} else {
allsub <- names(freq)[1:n]
}
M <- Mtype <- list()
for (i in 1:length(mut)) {
mutation <- mut[[i]]
mut.mat <- matrix(0, length(allsub), length(unique(mutation$Tumor_Sample_Barcode)))
type.mat <- matrix("", length(allsub), length(unique(mutation$Tumor_Sample_Barcode)))
colnames(type.mat) <- colnames(mut.mat) <- sort(unique(mutation$Tumor_Sample_Barcode))
rownames(type.mat) <- rownames(mut.mat) <- allsub
coln <- which(colnames(mutation) %in% "Tumor_Sample_Barcode")
coln2 <- which(colnames(mutation) %in% "Variant_Classification")
mut.org <- mutation[which(mutation$Hugo_Symbol %in% allsub), ]
sfInit(parallel = T, cpus = 4)
sfExport("mut.mat", "coln")
tmp <- sfLapply(as.list(1:length(allsub)), countSamples, mut.org, allsub, mut.mat, coln)
sfStop()
tmp <- do.call("rbind", tmp)
rownames(tmp) <- allsub
colnames(tmp) <- colnames(mut.mat)
M[[i]] <- tmp
sfInit(parallel = T, cpus = 4)
sfExport("type.mat", "coln2")
tmp <- sfLapply(as.list(1:length(allsub)), typeSamples, mut.org, allsub, type.mat, coln, coln2)
sfStop()
tmp <- do.call("rbind", tmp)
rownames(tmp) <- allsub
colnames(tmp) <- colnames(mut.mat)
Mtype[[i]] <- tmp
}
samples <- intersect(intersect(intersect(colnames(M[[1]]), colnames(M[[2]])), colnames(M[[3]])), colnames(M[[4]]))
M0 <- M[[1]][, which(colnames(M[[1]]) %in% samples)]
Mtype0 <- Mtype[[1]][, which(colnames(Mtype[[1]]) %in% samples)]
for (i in 2:length(M)) {
M0 <- M0 + M[[i]][, which(colnames(M[[i]]) %in% samples)]
Mtype0 <- matrix(paste(Mtype0, Mtype[[i]][, which(colnames(Mtype[[i]]) %in% samples)]), nrow(Mtype0))
}
rownames(Mtype0) <- rownames(M0)
colnames(Mtype0) <- colnames(M0)
save(M0, Mtype0, file = paste0(path, type, "_mut0.rda"))
}
## process expression data:
D <- assay(data)
class <- data@colData@listData$definition
M <- M0
Mtype <- Mtype0
colnames(M) <- lapply(colnames(M), function(x) {
y <- unlist(strsplit(x, "-"))
y <- paste(y[1:4], collapse = "-")
y <- unlist(strsplit(y, ""))
y <- paste(y[1:(length(y)-1)], collapse = "")
return(y)
})
colnames(D) <- lapply(colnames(D), function(x) {
y <- unlist(strsplit(x, "-"))
y <- paste(y[1:4], collapse = "-")
y <- unlist(strsplit(y, ""))
y <- paste(y[1:(length(y)-1)], collapse = "")
return(y)
})
colnames(M) <- gsub("A$", "", lapply(strsplit(colnames(M), "-"), function(x) { y <- x[1:4]; y <- paste(y, collapse = "-"); return(y) }))
M <- M[order(rownames(M)), order(colnames(M))]
Mtype <- Mtype[order(rownames(Mtype)), order(colnames(Mtype))]
print("mutation done")
## log odds:
if (file.exists(paste0(path, type, "_llr.rda")) & !newllr) {
load(paste0(path, type, "_llr.rda"))
} else {
library(edgeR)
if (sum(class %in% "Solid Tissue Normal") < 10) {
distrParNC <- function(i, data) {
data[i, ] <- data[i, ] - median(data[, i])
llrcol <- numeric(ncol(data))
div0 <- quantile(data[i, ], 0.25)
div1 <- quantile(data[i, ], 0.75)
sigma <- sd(data[i, ])
for (j in 1:ncol(data)) {
if (data[i, j] <= 0) {
llrcol[j] <- log2(div0/data[i, j])
} else {
llrcol[j] <- log2(data[i, j]/div1)
}
}
return(llrcol)
}
highcounts <- which(apply(D, 1, median) >= 10)
DC <- D[highcounts, ]
genenames <- rownames(D)[highcounts, ]
nf <- calcNormFactors(DC)
DC <- t(t(DC)/nf) # DC <- DC2
## this is very adventurous:
## DC <- t(scale(t(DC)))
## DC <- abs(DC)
## pc <- 1-min(DC)
## tmp <- log2(DC+pc)
## hist(tmp)
sfInit(parallel = T, cpus = 4)
sfExport("DC")
tmp <- do.call("rbind", sfLapply(1:nrow(DC),
distrParNC, DC))
colnames(tmp) <- colnames(DC)
rownames(tmp) <- genenames
sfStop()
DF <- DC
} else {
library(ks)
mykcde <- function(x, k) {
a <- which.min(abs(k$eval.points - x))
b <- k$estimate[a]
b <- min(b, 1-b)
return(b)
}
distrParKs <- function(i, data, C) {
llrcol <- numeric(ncol(data))
ddistr <- list()
dogenes <- unique(colnames(data))[which(!(unique(colnames(data)) %in% ""))]
for (j in dogenes) {
D <- which(colnames(data) %in% j)
ddistr[[j]] <- kcde(data[i, D])
}
cdistr <- kcde(data[i, C])
for (j in which(!(colnames(data) %in% ""))) {
gene <- colnames(data)[j]
llrcol[j] <- log2(mykcde(data[i, j], ddistr[[gene]])/mykcde(data[i,j], cdistr))
}
llrcol <- llrcol[-C]
return(llrcol)
}
DN <- D[, which(class %in% "Solid Tissue Normal")]
DT <- D[, which(class %in% "Primary solid Tumor")]
DF <- cbind(DT, DN)
C <- (ncol(DT)+1):ncol(DF)
highcounts <- which(apply(DF, 1, median) >= 10)
DF <- DF[highcounts, ]
genenames <- rownames(DF)
colnames(DF)[1:ncol(DT)] <- "P" # not knock-down specific!
colnames(DF)[grep(paste(unique(gsub("-.*", "", types)), collapse = "|"), colnames(DF))] <- ""
nf <- calcNormFactors(DF)
DF <- t(t(DF)/nf)
sfInit(parallel = T, cpus = 4)
sfExport("DF", "C", "mykcde")
sfLibrary(ks)
tmp <- do.call("rbind", sfLapply(1:nrow(DF),
distrParKs, DF, C))
sfStop()
colnames(tmp) <- colnames(DT)
}
save(tmp, DF, file = paste0(path, type, "_llr.rda"))
}
rownames(tmp) <- rownames(DF)
par(mfrow=c(1,3))
hist(tmp)
tmp[which(is.na(tmp) | is.infinite(tmp))] <- 0
hist(tmp)
D <- tmp
print("expression done")
## sd.glob <- sd(tmp)
## tmp <- tmp[-which(apply(tmp, 1, sd) < sd.glob), ]
## hist(tmp)
## prep clinical data:
clinical[which(clinical$vital_status%in% "dead"), which(colnames(clinical) %in% "vital_status")] <- 1
clinical[which(clinical$vital_status%in% "alive"), which(colnames(clinical) %in% "vital_status")] <- 0
count <- 0
for (stage in sort(unique(clinical$tumor_stage))) {
clinical[which(clinical$tumor_stage%in% stage), which(colnames(clinical) %in% "tumor_stage")] <- count
count <- count + 1
}
clinical$tumor_stage <- as.numeric(clinical$tumor_stage)
clinical[which(is.na(clinical$days_to_death)), which(colnames(clinical) %in% "days_to_death")] <- clinical[which(is.na(clinical$days_to_death)), which(colnames(clinical) %in% "days_to_last_follow_up")]
clinical$vital_status<- as.numeric(clinical$vital_status)
print("clinical done")
save(clinical, D, M, Mtype, DF, class, meth, cnv, file = paste0(path, type, "_final.rda"))
}
print(table(class))
sizemat <- rbind(sizemat, table(class))
rownames(sizemat)[nrow(sizemat)] <- type
if (donr) {
samplenr[snrcount, 1] <- sum(class %in% "Primary solid Tumor")
samplenr[snrcount, 2] <- sum(class %in% "Solid Tissue Normal")
}
}
stop("done")
samplenr2 <- samplenr[-which(is.na(samplenr[, 1]) == TRUE), ]
barplot(t(samplenr2[order(apply(samplenr2, 1, sum)), ]), horiz = 1, space = 1, las = 2)
newllr <- 1; newmut <- 1; nmut <- 1; newsave <- 1; types <- c("TCGA-SKCM","TCGA-UVM"); source("~/Documents/testing/general/TCGA.r")
nonesolid <- c("TCGA-LAML")
solidnonormal <- c()
## analysis:
type <- "TCGA-BRCA"
path <- "mutclust/"
load(paste0(path, type, "_final.rda"))
M[which(M < 3)] <- 0
M[which(M > 0)] <- 1
M[grep("Silent", Mtype)] <- 0
M <- M[order(rownames(M)), order(colnames(M))]
cnv[which(cnv == 0)] <- 0
cnv[which(cnv != 0)] <- 1
cnv <- cnv[order(rownames(cnv)), order(colnames(cnv))]
meth[which(meth > 0.5)] <- 1
meth[which(meth <= 0.5)] <- 0
meth <- meth[order(rownames(meth)), order(colnames(meth))]
meth[is.na(meth)] <- 0
P <- matrix(0, length(unique(c(rownames(M), rownames(cnv), rownames(meth)))), length(unique(c(colnames(M), colnames(cnv), colnames(meth)))))
rownames(P) <- sort(unique(c(rownames(M), rownames(cnv), rownames(meth))))
colnames(P) <- sort(unique(c(colnames(M), colnames(cnv), colnames(meth))))
colnames(P) <- gsub("A$", "", lapply(strsplit(colnames(P), "-"), function(x) { y <- x[1:4]; y <- paste(y, collapse = "-"); return(y) }))
P <- P[, which(duplicated(colnames(P)) == FALSE)]
P[which(rownames(P) %in% rownames(M)), which(colnames(P) %in% colnames(M))] <- M[which(rownames(M) %in% rownames(P)), which(colnames(M) %in% colnames(P))]
Pmut <- P
P <- Pmut*0
P[which(rownames(P) %in% rownames(meth)), which(colnames(P) %in% colnames(meth))] <- P[which(rownames(P) %in% rownames(meth)), which(colnames(P) %in% colnames(meth))] + meth[which(rownames(meth) %in% rownames(P)), which(colnames(meth) %in% colnames(P))]
Pmeth <- P
P <- Pmeth*0
P[which(rownames(P) %in% rownames(cnv)), which(colnames(P) %in% colnames(cnv))] <- P[which(rownames(P) %in% rownames(cnv)), which(colnames(P) %in% colnames(cnv))] + cnv[which(rownames(cnv) %in% rownames(P)), which(colnames(cnv) %in% colnames(P))]
Pcnv <- P
P <- Pmut+Pmeth+Pcnv
P2 <- P # full abberations including cnv and meth
P <- Pmut
## Bailey et al Cell 2018
goi <- c("MAP2K4", "GATA3", "GPS2", "TBX3", "PTPRD", "NCOR1", "CBFB", "CDKN1B") # BRCA
P <- P[which(rownames(P) %in% goi), ]
P[which(P > 1)] <- 1
P <- apply(P, 2, function(x) return(x/sum(x)))
P[is.na(P)] <- 0
## data imputation:
library(naturalsort)
library(nem)
library(cluster)
library(Rcpp)
library(Rgraphviz)
library(mnem)
source("~/Documents/mnem/R/mnems.r")
source("~/Documents/mnem/R/mnems_low.r")
sourceCpp("~/Documents/mnem/src/mm.cpp")
source("~/Documents/nempi/R/nempi_main.r")
source("~/Documents/nempi/R/nempi_low.r")
Rho <- cbind(P, matrix(0, nrow(P), sum(!(colnames(D) %in% colnames(P)))))
colnames(Rho) <- c(colnames(P), colnames(D)[which(!(colnames(D) %in% colnames(P)))])
Rho <- Rho[, colnames(D)]
if (sum(apply(Rho, 1, sum) == 0) > 0) {
Rho <- Rho[-which(apply(Rho, 1, sum) == 0), ]
}
Rho[is.na(Rho)] <- 0
sum(apply(Rho, 2, sum) == 0)/ncol(Rho) # unlabelled
pdf("temp.pdf", width = 12, height = 6)
tmp <- Rho
colnames(tmp) <- NULL
epiNEM::HeatmapOP(tmp, col = "RdBu", Rowv = 0, bordercol = "transparent")
dev.off()
inc <- sort(apply(Rho, 1, sum))
D2 <- D[which(apply(D, 1, median) != 0), ]
D3 <- D2[, which(duplicated(colnames(D2)) == FALSE)]
Rho <- Rho[, which(duplicated(colnames(D2)) == FALSE)]
for (i in which(duplicated(colnames(D2)) == TRUE)) {
j <- which(colnames(D3) %in% colnames(D2)[i])
D3[, j] <- apply(D2[, which(colnames(D2) %in% colnames(D2)[i]), drop = FALSE], 1, median)
}
D2 <- D3
colnames(D2) <- c(rownames(Rho), sample(rownames(Rho), ncol(D2)-nrow(Rho), replace = TRUE))
converged <- 10
start <- Sys.time()
nempires <- nempi(D2, Gamma = Rho, full = TRUE, converged = converged)
end <- Sys.time()
print(end - start)
ures <- nempires
sum(ures$lls[2:length(ures$lls)] - ures$lls[1:(length(ures$lls)-1)] < 0)
pdf("temp.pdf", width = 12, height = 6)
epiNEM::HeatmapOP(ures$Gamma, bordercol = rgb(0,0,0,0), col = "RdBu")
#plot(ures, edgewidth = 30)
dev.off()
pdf("temp.pdf", width = 9, height = 6)
par(mfrow=c(2,3))
plotConvergence(ures, type = "b", col = "blue")
dev.off()
source("~/Documents/nempi/R/nempi_main.r")
D4 <- D2
colnames(D4) <- apply(Rho, 2, function(x) {
Sgenes <- paste(sort(rownames(Rho)[which(x > 0)]), collapse = "_")
return(Sgenes)
})
## run on hpc cluster:
path <- ""
type <- "TCGA-BRCA"
if (do == 1) {
library(e1071)
load(paste0(path, type, "_nempi.rda"))
D4 <- D2
colnames(D4) <- apply(Rho, 2, function(x) {
Sgenes <- paste(sort(rownames(Rho)[which(x > 0)]), collapse = "_")
return(Sgenes)
})
svmres <- classpi(D4, full = TRUE, method = "svm")
save(svmres, file = paste0("temp_", do, "_", as.numeric(Sys.time()), ".rda"))
}
if (do == 2) {
library(nnet)
load(paste0(path, type, "_nempi.rda"))
D4 <- D2
colnames(D4) <- apply(Rho, 2, function(x) {
Sgenes <- paste(sort(rownames(Rho)[which(x > 0)]), collapse = "_")
return(Sgenes)
})
nnres <- classpi(D4, full = TRUE, method = "nnet", MaxNWts = 50000, size = 5) # takes forever
save(nnres, file = paste0("temp_", do, "_", as.numeric(Sys.time()), ".rda"))
}
if (do == 3) {
library(CALIBERrfimpute)
load(paste0(path, type, "_nempi.rda"))
D4 <- D2
colnames(D4) <- apply(Rho, 2, function(x) {
Sgenes <- paste(sort(rownames(Rho)[which(x > 0)]), collapse = "_")
return(Sgenes)
})
mfdata <- cbind(as.data.frame(t(D4)), colnames(D4))
mfdata[which(mfdata == "", arr.ind = TRUE)] <- NA
micedata <- mfdata
colnames(micedata) <- paste0(LETTERS[1:ncol(micedata)], 1:ncol(micedata))
miceres <- mice(micedata, method = c(rep('rfcont', ncol(micedata)-1), 'rfcat'), m = 2, maxit = 2)
save(miceres, file = paste0("temp_", do, "_", as.numeric(Sys.time()), ".rda"))
}
if (do == 4) {
library(e1071)
load(paste0(path, type, "_nempi.rda"))
D4 <- D2
colnames(D4) <- apply(Rho, 2, function(x) {
Sgenes <- paste(sort(rownames(Rho)[which(x > 0)]), collapse = "_")
return(Sgenes)
})
rfres <- classpi(D4, full = TRUE, method = "randomForest")
save(rfres, file = paste0("temp_", do, "_", as.numeric(Sys.time()), ".rda"))
}
if (do == 5) {
library(e1071)
load(paste0(path, type, "_nempi.rda"))
D4 <- D2
colnames(D4) <- apply(Rho, 2, function(x) {
Sgenes <- paste(sort(rownames(Rho)[which(x > 0)]), collapse = "_")
return(Sgenes)
})
mfdata <- cbind(as.data.frame(t(D4)), colnames(D4))
mfdata[which(mfdata == "", arr.ind = TRUE)] <- NA
library(missForest)
mfimp <- missForest(mfdata)
D4 <- D2
colnames(D4) <- mfimp$ximp[, ncol(mfimp$ximp)]
tmp <- mynem(D4, multi = TRUE)
Gamma <- getGamma(D4)
ures <- list()
ures$Gamma <- apply(Gamma, 2, function(x) return(x/sum(x)))
ures$res <- list()
ures$res$adj <- tmp$adj
ures$null <- TRUE
ures$combi <- 1
mfres <- ures
save(mfres, file = paste0("temp_", do, "_", as.numeric(Sys.time()), ".rda"))
}
## knn
library(class)
train <- t(D4[, which(colnames(D4) != "")])
test <- t(D4[, which(colnames(D4) == "")])
knn0 <- 0
if (knn0) {
train <- rbind(train, NULL = rep(0, ncol(train)))
cl <- c(colnames(D4)[which(colnames(D4) != "")], "NULL")
} else {
cl <- colnames(D4)[which(colnames(D4) != "")]
}
knnres <- knn(train, test, cl, prob = TRUE)
D3 <- D4
colnames(D3)[which(colnames(D3) %in% "")] <- as.character(knnres)
tmp <- mynem(D3, multi = TRUE)
Gamma <- getGamma(D3)
ures <- list()
ures$Gamma <- Gamma # apply(Gamma, 2, function(x) return(x/sum(x)))
ures$res <- list()
ures$res$adj <- tmp$adj
ures$null <- TRUE
ures$combi <- 1
knnres <- ures
## save(nempires, knnres, rfres, mfres, svmres, nnres, Rho, D2, Pmut, Pmeth, Pcnv, file = paste0(path, type, "_nempi.rda"))
path <- "mutclust/"; type <- "TCGA-BRCA"
load(paste0(path, type, "_nempi.rda"))
## ## load("~/Mount/Euler/temp_1_1573814974.40659.rda") # old
## load("~/Mount/Euler/temp_1_1574076694.65703.rda")
## ## load("~/Mount/Euler/temp_4_1573819581.9749.rda") # old
## load("~/Mount/Euler/temp_4_1574080528.67352.rda")
## ## load("~/Mount/Euler/temp_2_1573821112.2412.rda") # old
## load("~/Mount/Euler/temp_2_1574084503.12431.rda")
## load("~/Mount/Euler/temp_5_1574081415.91547.rda")
## ures <- rfres
## ures <- mfres
## ures <- svmres
## ures <- nnres
## ures <- knnres
ures <- nempires
## check against methylation and cnvs:
pdf("nempi_gamma.pdf", width = 12, height = 6)
tmp <- ures$Gamma
colnames(tmp) <- NULL
epiNEM::HeatmapOP(tmp, bordercol = rgb(0,0,0,0), col = "RdBu", colorkey = NULL)
dev.off()
pdf("nempi_phi.pdf", width = 6, height = 6)
Pgenes <- sort(unique(colnames(D2)))
adjtmp <- ures$res$adj
colnames(adjtmp) <- rownames(adjtmp) <- Pgenes
plotDnf(adjtmp, edgelwd = 2)
dev.off()
## cnv/meth enrichment:
methods <- list("NEM$\\pi$" = nempires, knn = knnres, mf = mfres, nn = nnres, rf = rfres, svm = svmres)
mutinc <- 1
Lall <- Lcnv <- Lmeth <- Lmut <- list()
for (i in 1:length(methods)) {
if (i != 8) {
print(names(methods)[i])
ures <- methods[[i]]
newGamma <- ures$Gamma
} else {
print("random")
newGamma <- newGamma*0
newGamma[sample(1:length(newGamma), floor(0.45*length(newGamma)))] <- 1 # well that is included into the test...
}
hist(newGamma)
if (i == 1) {
rntmp <- rownames(newGamma); newGamma <- t(mytc(ures$res$adj))%*%newGamma; rownames(newGamma) <- rntmp
}
P <- Pmut+Pmeth+Pcnv
if (!mutinc) { # include mutations or not (0)
P[which(Pmut == 1)] <- 0
}
P <- P[which(rownames(P) %in% rownames(Rho)), which(colnames(P) %in% colnames(Rho))]
P <- P[order(rownames(P)), order(colnames(P))]
P[which(P > 1)] <- 1
Ptmp <- cbind(P, matrix(0, nrow(P), sum(!(colnames(Rho) %in% colnames(P)))))
colnames(Ptmp) <- c(colnames(P), colnames(Rho)[which(!(colnames(Rho) %in% colnames(P)))])
P <- Ptmp[, colnames(Rho)]
## fisher:
if (i %in% c(4,5)) {
cut <- 0.07
} else if (i == 6) {
cut <- 0.1
} else {
cut <- 1/8
}
newGamma[which(newGamma > cut)] <- 1
newGamma[which(newGamma <= cut)] <- 0
pmeth <- newGamma
pmeth[which(newGamma == 1 & P == 1)] <- 2
pmeth[which(newGamma == 0 & P == 1)] <- -2
if (!mutinc) { # include mutations or not (0)
pmeth[which(Rho > 0)] <- 0
}
colnames(pmeth) <- NULL
##pdf(paste0("FigS_", names(methods)[i], ".pdf"), height = 6, width = 12)
setEPS()
postscript(paste0("FigS_", names(methods)[i], ".eps"), height = 6, width = 12)
print(epiNEM::HeatmapOP(pmeth, bordercol = rgb(0,0,0,0), col = "RdBu", colorkey = NULL))
dev.off()
print("cnv")
P <- Pcnv
P <- P[which(rownames(P) %in% rownames(Rho)), which(colnames(P) %in% colnames(Rho))]
P <- P[order(rownames(P)), order(colnames(P))]
P[which(P > 1)] <- 1
Ptmp <- cbind(P, matrix(0, nrow(P), sum(!(colnames(Rho) %in% colnames(P)))))
colnames(Ptmp) <- c(colnames(P), colnames(Rho)[which(!(colnames(Rho) %in% colnames(P)))])
P <- Ptmp[, colnames(Rho)]
F <- matrix(c(sum(pmeth >= 1 & P == 1), sum(pmeth >= 1 & P == 0), sum(pmeth == -2 & P == 1), sum(pmeth == 0 & P == 0)), 2)
print(1 - phyper(F[1,1]-1, sum(F[, 1]), sum(F[, 2]), sum(F[1, ])))
Lcnv[[i]] <- F
print("meth")
P <- Pmeth
P <- P[which(rownames(P) %in% rownames(Rho)), which(colnames(P) %in% colnames(Rho))]
P <- P[order(rownames(P)), order(colnames(P))]
P[which(P > 1)] <- 1
Ptmp <- cbind(P, matrix(0, nrow(P), sum(!(colnames(Rho) %in% colnames(P)))))
colnames(Ptmp) <- c(colnames(P), colnames(Rho)[which(!(colnames(Rho) %in% colnames(P)))])
P <- Ptmp[, colnames(Rho)]
F <- matrix(c(sum(pmeth >= 1 & P == 1), sum(pmeth >= 1 & P == 0), sum(pmeth == -2 & P == 1), sum(pmeth == 0 & P == 0)), 2)
print(1 - phyper(F[1,1]-1, sum(F[, 1]), sum(F[, 2]), sum(F[1, ])))
Lmeth[[i]] <- F
print("mut")
P <- Pmut
P <- P[which(rownames(P) %in% rownames(Rho)), which(colnames(P) %in% colnames(Rho))]
P <- P[order(rownames(P)), order(colnames(P))]
P[which(P > 1)] <- 1
Ptmp <- cbind(P, matrix(0, nrow(P), sum(!(colnames(Rho) %in% colnames(P)))))
colnames(Ptmp) <- c(colnames(P), colnames(Rho)[which(!(colnames(Rho) %in% colnames(P)))])
P <- Ptmp[, colnames(Rho)]
F <- matrix(c(sum(pmeth >= 1 & P == 1), sum(pmeth >= 1 & P == 0), sum(pmeth == -2 & P == 1), sum(pmeth == 0 & P == 0)), 2)
print(1 - phyper(F[1,1]-1, sum(F[, 1]), sum(F[, 2]), sum(F[1, ])))
Lmut[[i]] <- F
Fmat <- matrix(c(sum(pmeth == 2), sum(pmeth == 1), sum(pmeth == -2), sum(pmeth == 0)), 2)
Lall[[i]] <- Fmat
## print(fisher.test(Fmat, alternative = "greater"))
print("p-value")
print(1 - phyper(Fmat[1,1]-1, sum(Fmat[, 1]), sum(Fmat[, 2]), sum(Fmat[1, ])))
}
## create tables:
for (i in 1:length(methods)) {
cat(paste0(names(methods)[i], " & ", Lcnv[[i]][1,1], " & ", Lcnv[[i]][2,1], " & ", Lcnv[[i]][2,2], " & ", Lcnv[[i]][1,2], "\\\\\n"))
}
for (i in 1:length(methods)) {
cat(paste0(names(methods)[i], " & ", Lmeth[[i]][1,1], " & ", Lmeth[[i]][2,1], " & ", Lmeth[[i]][2,2], " & ", Lmeth[[i]][1,2], "\\\\\n"))
}
for (i in 1:length(methods)) {
cat(paste0(names(methods)[i], " & ", Lmut[[i]][1,1], " & ", Lmut[[i]][2,1], " & ", Lmut[[i]][2,2], " & ", Lmut[[i]][1,2], "\\\\\n"))
}
for (i in 1:length(methods)) {
if (names(methods)[i] == "nn") { next() }
Fmat <- Lall[[i]]
ptmp <- 1 - phyper(Fmat[1,1]-1, sum(Fmat[, 1]), sum(Fmat[, 2]), sum(Fmat[1, ]))
if (ptmp == 0) {
ptmp <- "$< 2.2\\times10^{-16}$"
} else {
ptmp <- paste0("$", signif(ptmp), "$")
}
cat(paste0(names(methods)[i], " & ", Lall[[i]][1,1], " & ", Lall[[i]][2,1], " & ", Lall[[i]][2,2], " & ", Lall[[i]][1,2], " & ", ptmp, "\\\\\n"))
}
## check correlation
## P4 <- apply(P3, 2, function(x) return(x/sum(x)))
## P4[is.na(P4)] <- 0
## cor(as.vector(newGamma), as.vector(P3))
##
cormat <- matrix(0, nrow(pmeth), 2)
fishres <- numeric(nrow(pmeth))
names(fishres) <- rownames(pmeth)
for (i in 1:nrow(pmeth)) {
Fmat <- matrix(c(sum(pmeth[i, ] == 2), sum(pmeth[i, ] == 1), sum(pmeth[i, ] == -2), sum(pmeth[i, ] == 0)), 2)
fishres[i] <- fisher.test(Fmat, alternative = "g")$p.value
cormat[i, ] <- c(sum(Fmat[1, ]), sum(Fmat[, 1]))
}
## GATA3 & PTPRD:
Fmat <- matrix(c(sum(pmeth[3, ] %in% c(2,-2) & pmeth[7, ] %in% c(2,-2)),
sum(pmeth[3, ] %in% c(1,0) & pmeth[7, ] %in% c(-2,2)),
sum(pmeth[3, ] %in% c(-2,2) & pmeth[7, ] %in% c(1,0)),
sum(pmeth[3, ] %in% c(1,0) & pmeth[7, ] %in% c(1,0))), 2)
1 - phyper(Fmat[1,1]-1, sum(Fmat[, 1]), sum(Fmat[, 2]), sum(Fmat[1, ]))
## pca:
pca <- princomp(D2)
col <- apply(newGamma, 2, sum)
col <- col/max(col)
K <- kmeans(t(newGamma), nrow(newGamma))
plot(pca$loadings[, 1:2], col = K$cluster)#rgb(col,0,0,1))
## tsne:
sne <- tsne(t(newGamma))
plot(sne, col = K$cluster)
## R profiling:
Rprof("temp.txt", line.profiling=TRUE)
ures <- nempi(D2[1:20, ], Gamma = Gamma, complete = 1, full = TRUE, converged = converged, combi = combi)
Rprof(NULL)
summaryRprof("temp.txt", lines = "show")$sampling.time
head(summaryRprof("temp.txt", lines = "show")$by.self, 10)
##
type <- "TCGA-BRCA"
load(paste0(path, type, "_nempi.rda"))
pdf("Fig5.pdf", width = 10, height = 10)
plot(uresn, edgelwd = 2)
dev.off()
pdf("Fig6.pdf", width = 10, height = 5)
tmp <- uresn$Gamma
colnames(tmp) <- NULL
epiNEM::HeatmapOP(tmp, bordercol = rgb(0,0,0,0), col = "RdBu", colorkey = NULL)
dev.off()
pdf("Fig7.pdf", width = 10, height = 10)
phitmp <- mytc(uresn$res$adj)
tmp <- t(phitmp)%*%uresn$Gamma
colnames(tmp) <- NULL
rownames(tmp) <- rownames(uresn$Gamma)
tmp2 <- tmp
colnames(tmp) <- NULL
tmp <- Gamma
colnames(tmp) <- NULL
tmp3 <- tmp
tmp4 <- tmp2
p1 <- epiNEM::HeatmapOP(tmp2, bordercol = rgb(0,0,0,0), col = "RdBu", clusterx = tmp2, colorkey = NULL)
p2 <- epiNEM::HeatmapOP(tmp, bordercol = rgb(0,0,0,0), col = "RdBu", clusterx = tmp2, colorkey = NULL)
print(p1, position=c(0, .5, 1, 1), more=TRUE)
print(p2, position=c(0, 0, 1, .5))
sum(tmp == 1 & tmp2 == 1)/sum(tmp == 1)
dev.off()
pdf("Fig8.pdf", width = 10, height = 10)
plot(uresf, edgelwd = 2)
dev.off()
pdf("Fig9.pdf", width = 10, height = 5)
tmp <- uresf$Gamma
colnames(tmp) <- NULL
epiNEM::HeatmapOP(tmp, bordercol = rgb(0,0,0,0), col = "RdBu", colorkey = NULL)
dev.off()
pdf("Fig10.pdf", width = 10, height = 10)
phitmp <- mytc(uresf$res$adj)
tmp <- t(phitmp)%*%uresf$Gamma
colnames(tmp) <- NULL
rownames(tmp) <- rownames(uresf$Gamma)
tmp2 <- tmp
colnames(tmp) <- NULL
tmp <- Gamma
colnames(tmp) <- NULL
p1 <- epiNEM::HeatmapOP(tmp2, bordercol = rgb(0,0,0,0), col = "RdBu", clusterx = tmp2, colorkey = NULL)
p2 <- epiNEM::HeatmapOP(tmp, bordercol = rgb(0,0,0,0), col = "RdBu", clusterx = tmp2, colorkey = NULL)
print(p1, position=c(0, .5, 1, 1), more=TRUE)
print(p2, position=c(0, 0, 1, .5))
sum(tmp == 1 & tmp2 == 1)/sum(tmp == 1)
dev.off()
## new figure:
P <- t(mytc(uresf$res$adj))%*%uresf$Gamma
rownames(P) <- rownames(uresf$Gamma)
PM <- P
PM[which(PM > 1/6 & Gamma == 1)] <- P[which(PM > 1/6 & Gamma == 1)] + 1
PM[which(PM <= 1/6 & Gamma == 1)] <- P[which(PM <= 1/6 & Gamma == 1)] - 1
epiNEM::HeatmapOP(PM, bordercol = rgb(0,0,0,0), col = "RdYlBu", breaks = seq(-1,2,length.out=5), clusterx = tmp2)
## other plots:
pdf("temp.pdf", width = 10, height = 10)
plotDnf(c("M1=M4", "M2=M4", "M3=M5", "M1=M5"), edgelwd = 2)
dev.off()
M <- matrix(0, 5, 10)
rownames(M) <- paste0("M", 1:5)
colnames(M) <- paste0("S", 1:10)
M[1, 1:4] <- 1
M[2, c(2,7:9)] <- 1
M[3, 5:6] <- 1
M[3, 10] <- 1
phi <- matrix(0, 5, 5)
diag(phi) <- 1
phi[1, 1:5] <- 1
phi[2, 3] <- phi[4, 5] <- 1
## M <- t(phi)%*%M; M[M > 1] <- 1
rownames(M) <- paste0("M", 1:5)
pdf("temp.pdf", width = 8, height = 4)
epiNEM::HeatmapOP(M, Colv = 0, Rowv = 0, col = "RdBu", colorkey = NULL)
dev.off()
## check for mutation type (das führt zu nichts)
colnames(Mtype) <- unlist(lapply(strsplit(colnames(Mtype), "-"), function(x) {
y <- x[1:3]
y <- paste(c(y, "01"), collapse = "-")
return(y)
}))
checkgene <- "CBFB"
van <- intersect(colnames(Gamma)[which(PM[checkgene, ] < 0)], colnames(Mtype))
A <- sum(unlist(lapply(strsplit(Mtype[checkgene, van], " "), function(x) if ("Silent" %in% x) { return(1) } else { return(0) })))
B <- length(van) - A
van <- intersect(colnames(Gamma)[which(PM[checkgene, ] > 0)], colnames(Mtype))
C <- sum(unlist(lapply(strsplit(Mtype[checkgene, van], " "), function(x) if ("Silent" %in% x) { return(1) } else { return(0) })))
D <- length(van) - C
table(unlist(lapply(strsplit(Mtype[checkgene, van], " "), function(x) return(names(table(x))[which.max(table(x))]))))
## table(unlist(strsplit(as.vector(Mtype), " ")))
## pdf("Fig10.pdf", width = 10, height = 10)
## p1 <- epiNEM::HeatmapOP(tmp4, bordercol = rgb(0,0,0,0), col = "RdBu", clusterx = tmp2, colorkey = NULL)
## p2 <- epiNEM::HeatmapOP(tmp3, bordercol = rgb(0,0,0,0), col = "RdBu", clusterx = tmp2, colorkey = NULL)
## p3 <- epiNEM::HeatmapOP(tmp2, bordercol = rgb(0,0,0,0), col = "RdBu", clusterx = tmp2, colorkey = NULL)
## p4 <- epiNEM::HeatmapOP(tmp, bordercol = rgb(0,0,0,0), col = "RdBu", clusterx = tmp2, colorkey = NULL)
## print(p1, position=c(0, .5, .5, 1), more=TRUE)
## print(p2, position=c(0, 0, .5, .5), more=TRUE)
## print(p3, position=c(.5, .5, 1, 1), more=TRUE)
## print(p4, position=c(.5, 0, 1, .5))
## sum(tmp == 1 & tmp2 == 1)/sum(tmp == 1)
## dev.off()
source("~/Documents/testing/R/nempi.r")
source("~/Documents/testing/R/nempi_low.r")
bsres <- unembs(D2, Gamma = Gamma, complete = 1, full = TRUE, converged = converged, combi = combi, bsruns = 10, bssize = 0.5)
pdf("temp.pdf", width = 10, height = 5)
epiNEM::HeatmapOP(bsres$Gamma, bordercol = rgb(0,0,0,0), col = "RdBu", colorkey = NULL)
dev.off()
pdf("temp.pdf", width = 20, height = 10)
par(mfrow=c(1,2))
tmp <- bsres$phi
tmp[which(tmp > 0)] <- 1
diag(tmp) <- 0
freqtop <- as.vector(t(bsres$phi)[which(lower.tri(bsres$phi) == TRUE)])/10
freqtop <- freqtop[which(freqtop != 0)]
tmptop <- tmp
tmptop[lower.tri(tmptop)] <- 0
tmptop <- adj2dnf(tmptop)
tmptop <- tmptop[grep("=", tmptop)]
plotDnf(tmptop, edgelwd = 2, edgelab = freqtop, freq = freqtop)
freqbot <- as.vector(t(bsres$phi)[which(upper.tri(bsres$phi) == TRUE)])/10
freqbot <- freqbot[which(freqbot != 0)]
tmpbot <- tmp
tmpbot[upper.tri(tmpbot)] <- 0
tmpbot <- adj2dnf(tmpbot)
tmpbot <- tmpbot[grep("=", tmpbot)]
plotDnf(tmpbot, edgelwd = 2, edgelab = freqbot, freq = freqbot)
dev.off()
##
source("testing/vignettes/TCGA_cluster.r")
pcares <- prcomp(tmp)
library(naturalsort)
library(nem)
library(cluster)
library(Rcpp)
source("~/Documents/mnem/R/mnems.r")
source("~/Documents/mnem/R/mnems_low.r")
sourceCpp("~/Documents/mnem/src/mm.cpp")
res <- mnem(tmp, starts = 10, search = "greedy", type = "cluster3", complete = 1, multi = 1)
tmp2 <- tmp
Rprof("temp.txt", line.profiling=TRUE)
res <- mnem(tmp2, starts = 10, search = "greedy", type = "cluster3", complete = 1, multi = 1, k = 2)
Rprof(NULL)
summaryRprof("temp.txt", lines = "show")$sampling.time
head(summaryRprof("temp.txt", lines = "show")$by.self)
## resk <- mnemk(tmp2, starts = 10, search = "estimate", type = "cluster3", complete = 1, multi = 1)
cluster <- apply(getAffinity(res$probs, mw = res$mw, complete = TRUE), 2, which.max)
par(mfrow=c(1,2))
plot(pcares$rotation[, 1:2], col = cluster)
names(cluster) <- gsub("-01$|-03$", "", colnames(M))
cluster <- cluster[which(names(cluster) %in% clinical$submitter_id)]
cluster <- cluster[order(names(cluster))]
clinical <- rbind(clinical, clinical[which(clinical[, 1] %in% names(cluster)[which(duplicated(names(cluster)))]), ])
clinical <- clinical[order(clinical[, 1]), ]
print(all(clinical[, 1] == names(cluster)))
fit <- survfit(Surv(days_to_death, vital_status) ~ cluster, clinical)
plot(fit, col = 1:length(table(cluster)), lty = 1:length(table(cluster)))
legend(max(clinical$days_to_death, na.rm = TRUE), 1, 1:length(table(cluster)), lty = 1:length(table(cluster)), col = 1:length(table(cluster)), xjust = 1, yjust = 1)
fit <- coxph(Surv(days_to_death, vital_status) ~ cluster + age_at_diagnosis, clinical)
print(fit)
fit <- coxph(Surv(days_to_death, vital_status) ~ cluster + age_at_diagnosis + tumor_stage, clinical)
print(fit)
fit <- coxph(Surv(days_to_death, vital_status) ~ cluster + tumor_stage, clinical)
print(fit)
fit <- coxph(Surv(days_to_death, vital_status) ~ cluster, clinical)
print(fit)
kres <- clustNEM(tmp, nem = 0, k = length(res$comp), nstart = 10)
## kres <- clustNEM(tmp, nem = 0, nstart = 10)
plot(pcares$rotation[, 1:2], col = kres$cluster)
kcluster <- kres$cluster
names(kcluster) <- gsub("-01$|-03$", "", colnames(M))
kcluster <- kcluster[which(names(kcluster) %in% clinical$submitter_id)]
kcluster <- kcluster[order(names(kcluster))]
fit <- survfit(Surv(days_to_death, vital_status) ~ kcluster, clinical)
plot(fit, col = 1:length(table(cluster)), lty = 1:length(table(cluster)))
legend(max(clinical$days_to_death, na.rm = TRUE), 1, 1:length(table(cluster)), lty = 1:length(table(cluster)), col = 1:length(table(cluster)), xjust = 1, yjust = 1)
fit <- coxph(Surv(days_to_death, vital_status) ~ kcluster + age_at_diagnosis, clinical)
print(fit)
fit <- coxph(Surv(days_to_death, vital_status) ~ kcluster, clinical)
print(fit)
cbg-ethz/nempi documentation built on Nov. 5, 2024, 11:27 p.m.
R Package Documentation
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library(snowfall)
library(TCGAbiolinks)
if (is.null(types)) {
types <- TCGAbiolinks:::getGDCprojects()$project_id
## special "FM-AD"
dont <- paste0(unique(gsub("-.*", "", types)), "-")
dont <- dont[-grep("TCGA", dont)]
samplenr <- matrix(NA, length(types), 2)
rownames(samplenr) <- types
donr <- TRUE
snrcount <- 0
} else {
dont <- "AGCT"
donr <- FALSE
}
sizemat <- matrix(0, 1, 2)
colnames(sizemat) <- c("Tumor", "Normal")
rownames(sizemat) <- ""
path <- "mutclust/"
for (type in types) {
if (donr) {
snrcount <- snrcount + 1
}
if (length(grep(paste(dont, collapse = "|"), type)) > 0) { next() }
print(type)
if (file.exists(paste0(path, type, "_final.rda")) & !newmut & !newllr & !newsave) {
load(paste0(path, type, "_final.rda"))
} else {
summ <- TCGAbiolinks:::getProjectSummary(type)
library(SummarizedExperiment)
## get methylation:
if (file.exists(paste0(path, type, "_meth.rda"))) {
load(paste0(path, type, "_meth.rda"))
} else {
data <- GDCquery(project = paste(type, sep = ""),
sample.type = "Primary Tumor",
data.category = "DNA methylation",
data.type = "Methylation beta value",
legacy = TRUE
)
GDCdownload(data)
data <- GDCprepare(data, summarizedExperiment = 0)
## map methy sites to genes:
library(methyAnalysis)
if (is.data.frame(data)) {
meth2 <- as.matrix(data[, -(1:3)])
rownames(meth2) <- gsub(";.*", "", data[, 1])
} else {
meth <- data@rowRanges
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
probe2gene <- annotateDMRInfo(meth, 'TxDb.Hsapiens.UCSC.hg19.knownGene')
meth2 <- assay(data)
rownames(meth2) <- probe2gene$sigDMRInfo@elementMetadata@listData$GeneSymbol
}
meth <- meth2
meth <- meth[which(apply(meth, 1, function(x) return(any(is.na(x)))) == FALSE), ]
methm <- meth[which(duplicated(rownames(meth)) == FALSE), ]
count <- 0
for (i in which(duplicated(rownames(meth)) == FALSE)) {
count <- count + 1
methm[count, ] <- apply(meth[which(rownames(meth) %in% rownames(methm)[i]), , drop = FALSE], 2, median)
}
meth <- methm
meth <- meth[order(rownames(meth)), order(colnames(meth))]
colnames(meth) <- gsub("A$", "", lapply(strsplit(colnames(meth), "-"), function(x) { y <- x[1:4]; y <- paste(y, collapse = "-"); return(y) }))
methm <- meth[, which(duplicated(colnames(meth)) == FALSE)]
for (i in which(duplicated(colnames(meth)) == TRUE)) {
j <- which(colnames(methm) == colnames(meth)[i])
methm[, j] <- apply(meth[, which(colnames(meth) %in% colnames(methm)[i]), drop = FALSE], 2, median)
}
meth <- methm
meth <- meth[order(rownames(meth)), order(colnames(meth))]
save(meth, file = paste0(path, type, "_meth.rda"))
}
print("meth done")
## get copy number variation:
if (file.exists(paste0(path, type, "_cnv.rda"))) {
load(paste0(path, type, "_cnv.rda"))
} else {
data <- getGistic(gsub("TCGA-", "", type), type = "thresholded")
## data <- GDCquery(project = paste(type, sep = ""),
## sample.type = "Primary solid Tumor",
## data.category = "Copy Number Variation",
## data.type = "Copy Number Segment",
## )
## GDCdownload(data)
## data <- GDCprepare(data)
cnv <- data[, -(1:3)]
cnv <- apply(cnv, c(1,2), as.numeric)
rownames(cnv) <- data[, 1]
colnames(cnv) <- gsub("A$", "", lapply(strsplit(colnames(cnv), "-"), function(x) { y <- x[1:4]; y <- paste(y, collapse = "-"); return(y) }))
cnv <- cnv[order(rownames(cnv)), order(colnames(cnv))]
save(cnv, file = paste0(path, type, "_cnv.rda"))
}
print("cnv done")
## get expression
if (file.exists(paste0(path, type, "_query.rda"))) {
load(paste0(path, type, "_query.rda"))
} else {
if (length(grep("-AML$|-LAML$", type)) > 0) {
sampletype <- c("Primary Blood Derived Cancer - Peripheral Blood", "Solid Tissue Normal")
} else {
sampletype <- c("Primary solid Tumor", "Solid Tissue Normal")
}
data <- GDCquery(project = paste(type, sep = ""),
sample.type = sampletype,
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "HTSeq - Counts"
)
GDCdownload(data)
if (is.null(data)) {
print("is null")
next()
}
data <- GDCprepare(data)
save(data, file = paste0(path, type, "_query.rda"))
}
## process expression data:
D <- assay(data)
class <- data@colData@listData$definition
print("gene expression done")
## get mutation
if (file.exists(paste0(path, type, "_mut.rda"))) {
load(paste0(path, type, "_mut.rda"))
} else {
type2 <- gsub(paste(paste0(unique(gsub("-.*", "", types)), "-"), collapse = "|"), "", type)
library(data.table)
GDCquery_Maf(tumor = type2, save.csv = TRUE, pipeline = "varscan2") # mutation file
GDCquery_Maf(tumor = type2, save.csv = TRUE, pipeline = "muse") # mutation file
GDCquery_Maf(tumor = type2, save.csv = TRUE, pipeline = "somaticsniper") # mutation file
GDCquery_Maf(tumor = type2, save.csv = TRUE, pipeline = "mutect2") # mutation file
mut <- list()
count <- 1
type3 <- gsub("-", "\\.", type)
for (i in list.files("GDCdata")) {
if (length(grep(type3, i)) > 0 & length(grep("csv", i)) > 0) {
mut[[count]] <- fread(paste0("GDCdata/", i))
count <- count + 1
}
}
save(mut, file = paste0(path, type, "_mut.rda"))
}
## clinical
if (file.exists(paste0(path, type, "_clin.rda"))) {
load(paste0(path, type, "_clin.rda"))
} else {
clinical <- GDCquery_clinic(project = type, type = "clinical")
save(clinical, file = paste0(path, type, "_clin.rda"))
}
## process mutation data: (https://cancer.sanger.ac.uk/cosmic/help/mutation/overview)
if (file.exists(paste0(path, type, "_mut0.rda")) & !newmut) {
load(paste0(path, type, "_mut0.rda"))
} else {
n <- nmut # try something to get all patients with at least one mutation
library(snowfall)
countSamples <- function(i, mut.org, genes, mut.mat, coln) {
i <- which(rownames(mut.mat) %in% genes[i])
tmp <- mut.org[which(mut.org$Hugo_Symbol %in% rownames(mut.mat)[i]), coln]
tmp2 <- mut.mat[i, ]
tmp2[which(colnames(mut.mat) %in% tmp)] <- 1
return(tmp2)
}
typeSamples <- function(i, mut.org, genes, type.mat, coln, coln2) {
i <- which(rownames(type.mat) %in% genes[i])
tmp <- mut.org[which(mut.org$Hugo_Symbol %in% rownames(type.mat)[i]), coln]
tmp3 <- mut.org[which(mut.org$Hugo_Symbol %in% rownames(type.mat)[i]), coln2]
tmp2 <- type.mat[i, ]
tmp2[which(colnames(type.mat) %in% tmp)] <- tmp3
return(tmp2)
}
biggl <- list()
for (i in length(mut)) {
mutation <- mut[[i]]
biggl[[i]] <- mutation$Hugo_Symbol
}
freq <- sort(table(unlist(biggl)), decreasing = TRUE)
if (n == 0) {
allsub <- names(freq)
} else {
allsub <- names(freq)[1:n]
}
M <- Mtype <- list()
for (i in 1:length(mut)) {
mutation <- mut[[i]]
mut.mat <- matrix(0, length(allsub), length(unique(mutation$Tumor_Sample_Barcode)))
type.mat <- matrix("", length(allsub), length(unique(mutation$Tumor_Sample_Barcode)))
colnames(type.mat) <- colnames(mut.mat) <- sort(unique(mutation$Tumor_Sample_Barcode))
rownames(type.mat) <- rownames(mut.mat) <- allsub
coln <- which(colnames(mutation) %in% "Tumor_Sample_Barcode")
coln2 <- which(colnames(mutation) %in% "Variant_Classification")
mut.org <- mutation[which(mutation$Hugo_Symbol %in% allsub), ]
sfInit(parallel = T, cpus = 4)
sfExport("mut.mat", "coln")
tmp <- sfLapply(as.list(1:length(allsub)), countSamples, mut.org, allsub, mut.mat, coln)
sfStop()
tmp <- do.call("rbind", tmp)
rownames(tmp) <- allsub
colnames(tmp) <- colnames(mut.mat)
M[[i]] <- tmp
sfInit(parallel = T, cpus = 4)
sfExport("type.mat", "coln2")
tmp <- sfLapply(as.list(1:length(allsub)), typeSamples, mut.org, allsub, type.mat, coln, coln2)
sfStop()
tmp <- do.call("rbind", tmp)
rownames(tmp) <- allsub
colnames(tmp) <- colnames(mut.mat)
Mtype[[i]] <- tmp
}
samples <- intersect(intersect(intersect(colnames(M[[1]]), colnames(M[[2]])), colnames(M[[3]])), colnames(M[[4]]))
M0 <- M[[1]][, which(colnames(M[[1]]) %in% samples)]
Mtype0 <- Mtype[[1]][, which(colnames(Mtype[[1]]) %in% samples)]
for (i in 2:length(M)) {
M0 <- M0 + M[[i]][, which(colnames(M[[i]]) %in% samples)]
Mtype0 <- matrix(paste(Mtype0, Mtype[[i]][, which(colnames(Mtype[[i]]) %in% samples)]), nrow(Mtype0))
}
rownames(Mtype0) <- rownames(M0)
colnames(Mtype0) <- colnames(M0)
save(M0, Mtype0, file = paste0(path, type, "_mut0.rda"))
}
## process expression data:
D <- assay(data)
class <- data@colData@listData$definition
M <- M0
Mtype <- Mtype0
colnames(M) <- lapply(colnames(M), function(x) {
y <- unlist(strsplit(x, "-"))
y <- paste(y[1:4], collapse = "-")
y <- unlist(strsplit(y, ""))
y <- paste(y[1:(length(y)-1)], collapse = "")
return(y)
})
colnames(D) <- lapply(colnames(D), function(x) {
y <- unlist(strsplit(x, "-"))
y <- paste(y[1:4], collapse = "-")
y <- unlist(strsplit(y, ""))
y <- paste(y[1:(length(y)-1)], collapse = "")
return(y)
})
colnames(M) <- gsub("A$", "", lapply(strsplit(colnames(M), "-"), function(x) { y <- x[1:4]; y <- paste(y, collapse = "-"); return(y) }))
M <- M[order(rownames(M)), order(colnames(M))]
Mtype <- Mtype[order(rownames(Mtype)), order(colnames(Mtype))]
print("mutation done")
## log odds:
if (file.exists(paste0(path, type, "_llr.rda")) & !newllr) {
load(paste0(path, type, "_llr.rda"))
} else {
library(edgeR)
if (sum(class %in% "Solid Tissue Normal") < 10) {
distrParNC <- function(i, data) {
data[i, ] <- data[i, ] - median(data[, i])
llrcol <- numeric(ncol(data))
div0 <- quantile(data[i, ], 0.25)
div1 <- quantile(data[i, ], 0.75)
sigma <- sd(data[i, ])
for (j in 1:ncol(data)) {
if (data[i, j] <= 0) {
llrcol[j] <- log2(div0/data[i, j])
} else {
llrcol[j] <- log2(data[i, j]/div1)
}
}
return(llrcol)
}
highcounts <- which(apply(D, 1, median) >= 10)
DC <- D[highcounts, ]
genenames <- rownames(D)[highcounts, ]
nf <- calcNormFactors(DC)
DC <- t(t(DC)/nf) # DC <- DC2
## this is very adventurous:
## DC <- t(scale(t(DC)))
## DC <- abs(DC)
## pc <- 1-min(DC)
## tmp <- log2(DC+pc)
## hist(tmp)
sfInit(parallel = T, cpus = 4)
sfExport("DC")
tmp <- do.call("rbind", sfLapply(1:nrow(DC),
distrParNC, DC))
colnames(tmp) <- colnames(DC)
rownames(tmp) <- genenames
sfStop()
DF <- DC
} else {
library(ks)
mykcde <- function(x, k) {
a <- which.min(abs(k$eval.points - x))
b <- k$estimate[a]
b <- min(b, 1-b)
return(b)
}
distrParKs <- function(i, data, C) {
llrcol <- numeric(ncol(data))
ddistr <- list()
dogenes <- unique(colnames(data))[which(!(unique(colnames(data)) %in% ""))]
for (j in dogenes) {
D <- which(colnames(data) %in% j)
ddistr[[j]] <- kcde(data[i, D])
}
cdistr <- kcde(data[i, C])
for (j in which(!(colnames(data) %in% ""))) {
gene <- colnames(data)[j]
llrcol[j] <- log2(mykcde(data[i, j], ddistr[[gene]])/mykcde(data[i,j], cdistr))
}
llrcol <- llrcol[-C]
return(llrcol)
}
DN <- D[, which(class %in% "Solid Tissue Normal")]
DT <- D[, which(class %in% "Primary solid Tumor")]
DF <- cbind(DT, DN)
C <- (ncol(DT)+1):ncol(DF)
highcounts <- which(apply(DF, 1, median) >= 10)
DF <- DF[highcounts, ]
genenames <- rownames(DF)
colnames(DF)[1:ncol(DT)] <- "P" # not knock-down specific!
colnames(DF)[grep(paste(unique(gsub("-.*", "", types)), collapse = "|"), colnames(DF))] <- ""
nf <- calcNormFactors(DF)
DF <- t(t(DF)/nf)
sfInit(parallel = T, cpus = 4)
sfExport("DF", "C", "mykcde")
sfLibrary(ks)
tmp <- do.call("rbind", sfLapply(1:nrow(DF),
distrParKs, DF, C))
sfStop()
colnames(tmp) <- colnames(DT)
}
save(tmp, DF, file = paste0(path, type, "_llr.rda"))
}
rownames(tmp) <- rownames(DF)
par(mfrow=c(1,3))
hist(tmp)
tmp[which(is.na(tmp) | is.infinite(tmp))] <- 0
hist(tmp)
D <- tmp
print("expression done")
## sd.glob <- sd(tmp)
## tmp <- tmp[-which(apply(tmp, 1, sd) < sd.glob), ]
## hist(tmp)
## prep clinical data:
clinical[which(clinical$vital_status%in% "dead"), which(colnames(clinical) %in% "vital_status")] <- 1
clinical[which(clinical$vital_status%in% "alive"), which(colnames(clinical) %in% "vital_status")] <- 0
count <- 0
for (stage in sort(unique(clinical$tumor_stage))) {
clinical[which(clinical$tumor_stage%in% stage), which(colnames(clinical) %in% "tumor_stage")] <- count
count <- count + 1
}
clinical$tumor_stage <- as.numeric(clinical$tumor_stage)
clinical[which(is.na(clinical$days_to_death)), which(colnames(clinical) %in% "days_to_death")] <- clinical[which(is.na(clinical$days_to_death)), which(colnames(clinical) %in% "days_to_last_follow_up")]
clinical$vital_status<- as.numeric(clinical$vital_status)
print("clinical done")
save(clinical, D, M, Mtype, DF, class, meth, cnv, file = paste0(path, type, "_final.rda"))
}
print(table(class))
sizemat <- rbind(sizemat, table(class))
rownames(sizemat)[nrow(sizemat)] <- type
if (donr) {
samplenr[snrcount, 1] <- sum(class %in% "Primary solid Tumor")
samplenr[snrcount, 2] <- sum(class %in% "Solid Tissue Normal")
}
}
stop("done")
samplenr2 <- samplenr[-which(is.na(samplenr[, 1]) == TRUE), ]
barplot(t(samplenr2[order(apply(samplenr2, 1, sum)), ]), horiz = 1, space = 1, las = 2)
newllr <- 1; newmut <- 1; nmut <- 1; newsave <- 1; types <- c("TCGA-SKCM","TCGA-UVM"); source("~/Documents/testing/general/TCGA.r")
nonesolid <- c("TCGA-LAML")
solidnonormal <- c()
## analysis:
type <- "TCGA-BRCA"
path <- "mutclust/"
load(paste0(path, type, "_final.rda"))
M[which(M < 3)] <- 0
M[which(M > 0)] <- 1
M[grep("Silent", Mtype)] <- 0
M <- M[order(rownames(M)), order(colnames(M))]
cnv[which(cnv == 0)] <- 0
cnv[which(cnv != 0)] <- 1
cnv <- cnv[order(rownames(cnv)), order(colnames(cnv))]
meth[which(meth > 0.5)] <- 1
meth[which(meth <= 0.5)] <- 0
meth <- meth[order(rownames(meth)), order(colnames(meth))]
meth[is.na(meth)] <- 0
P <- matrix(0, length(unique(c(rownames(M), rownames(cnv), rownames(meth)))), length(unique(c(colnames(M), colnames(cnv), colnames(meth)))))
rownames(P) <- sort(unique(c(rownames(M), rownames(cnv), rownames(meth))))
colnames(P) <- sort(unique(c(colnames(M), colnames(cnv), colnames(meth))))
colnames(P) <- gsub("A$", "", lapply(strsplit(colnames(P), "-"), function(x) { y <- x[1:4]; y <- paste(y, collapse = "-"); return(y) }))
P <- P[, which(duplicated(colnames(P)) == FALSE)]
P[which(rownames(P) %in% rownames(M)), which(colnames(P) %in% colnames(M))] <- M[which(rownames(M) %in% rownames(P)), which(colnames(M) %in% colnames(P))]
Pmut <- P
P <- Pmut*0
P[which(rownames(P) %in% rownames(meth)), which(colnames(P) %in% colnames(meth))] <- P[which(rownames(P) %in% rownames(meth)), which(colnames(P) %in% colnames(meth))] + meth[which(rownames(meth) %in% rownames(P)), which(colnames(meth) %in% colnames(P))]
Pmeth <- P
P <- Pmeth*0
P[which(rownames(P) %in% rownames(cnv)), which(colnames(P) %in% colnames(cnv))] <- P[which(rownames(P) %in% rownames(cnv)), which(colnames(P) %in% colnames(cnv))] + cnv[which(rownames(cnv) %in% rownames(P)), which(colnames(cnv) %in% colnames(P))]
Pcnv <- P
P <- Pmut+Pmeth+Pcnv
P2 <- P # full abberations including cnv and meth
P <- Pmut
## Bailey et al Cell 2018
goi <- c("MAP2K4", "GATA3", "GPS2", "TBX3", "PTPRD", "NCOR1", "CBFB", "CDKN1B") # BRCA
P <- P[which(rownames(P) %in% goi), ]
P[which(P > 1)] <- 1
P <- apply(P, 2, function(x) return(x/sum(x)))
P[is.na(P)] <- 0
## data imputation:
library(naturalsort)
library(nem)
library(cluster)
library(Rcpp)
library(Rgraphviz)
library(mnem)
source("~/Documents/mnem/R/mnems.r")
source("~/Documents/mnem/R/mnems_low.r")
sourceCpp("~/Documents/mnem/src/mm.cpp")
source("~/Documents/nempi/R/nempi_main.r")
source("~/Documents/nempi/R/nempi_low.r")
Rho <- cbind(P, matrix(0, nrow(P), sum(!(colnames(D) %in% colnames(P)))))
colnames(Rho) <- c(colnames(P), colnames(D)[which(!(colnames(D) %in% colnames(P)))])
Rho <- Rho[, colnames(D)]
if (sum(apply(Rho, 1, sum) == 0) > 0) {
Rho <- Rho[-which(apply(Rho, 1, sum) == 0), ]
}
Rho[is.na(Rho)] <- 0
sum(apply(Rho, 2, sum) == 0)/ncol(Rho) # unlabelled
pdf("temp.pdf", width = 12, height = 6)
tmp <- Rho
colnames(tmp) <- NULL
epiNEM::HeatmapOP(tmp, col = "RdBu", Rowv = 0, bordercol = "transparent")
dev.off()
inc <- sort(apply(Rho, 1, sum))
D2 <- D[which(apply(D, 1, median) != 0), ]
D3 <- D2[, which(duplicated(colnames(D2)) == FALSE)]
Rho <- Rho[, which(duplicated(colnames(D2)) == FALSE)]
for (i in which(duplicated(colnames(D2)) == TRUE)) {
j <- which(colnames(D3) %in% colnames(D2)[i])
D3[, j] <- apply(D2[, which(colnames(D2) %in% colnames(D2)[i]), drop = FALSE], 1, median)
}
D2 <- D3
colnames(D2) <- c(rownames(Rho), sample(rownames(Rho), ncol(D2)-nrow(Rho), replace = TRUE))
converged <- 10
start <- Sys.time()
nempires <- nempi(D2, Gamma = Rho, full = TRUE, converged = converged)
end <- Sys.time()
print(end - start)
ures <- nempires
sum(ures$lls[2:length(ures$lls)] - ures$lls[1:(length(ures$lls)-1)] < 0)
pdf("temp.pdf", width = 12, height = 6)
epiNEM::HeatmapOP(ures$Gamma, bordercol = rgb(0,0,0,0), col = "RdBu")
#plot(ures, edgewidth = 30)
dev.off()
pdf("temp.pdf", width = 9, height = 6)
par(mfrow=c(2,3))
plotConvergence(ures, type = "b", col = "blue")
dev.off()
source("~/Documents/nempi/R/nempi_main.r")
D4 <- D2
colnames(D4) <- apply(Rho, 2, function(x) {
Sgenes <- paste(sort(rownames(Rho)[which(x > 0)]), collapse = "_")
return(Sgenes)
})
## run on hpc cluster:
path <- ""
type <- "TCGA-BRCA"
if (do == 1) {
library(e1071)
load(paste0(path, type, "_nempi.rda"))
D4 <- D2
colnames(D4) <- apply(Rho, 2, function(x) {
Sgenes <- paste(sort(rownames(Rho)[which(x > 0)]), collapse = "_")
return(Sgenes)
})
svmres <- classpi(D4, full = TRUE, method = "svm")
save(svmres, file = paste0("temp_", do, "_", as.numeric(Sys.time()), ".rda"))
}
if (do == 2) {
library(nnet)
load(paste0(path, type, "_nempi.rda"))
D4 <- D2
colnames(D4) <- apply(Rho, 2, function(x) {
Sgenes <- paste(sort(rownames(Rho)[which(x > 0)]), collapse = "_")
return(Sgenes)
})
nnres <- classpi(D4, full = TRUE, method = "nnet", MaxNWts = 50000, size = 5) # takes forever
save(nnres, file = paste0("temp_", do, "_", as.numeric(Sys.time()), ".rda"))
}
if (do == 3) {
library(CALIBERrfimpute)
load(paste0(path, type, "_nempi.rda"))
D4 <- D2
colnames(D4) <- apply(Rho, 2, function(x) {
Sgenes <- paste(sort(rownames(Rho)[which(x > 0)]), collapse = "_")
return(Sgenes)
})
mfdata <- cbind(as.data.frame(t(D4)), colnames(D4))
mfdata[which(mfdata == "", arr.ind = TRUE)] <- NA
micedata <- mfdata
colnames(micedata) <- paste0(LETTERS[1:ncol(micedata)], 1:ncol(micedata))
miceres <- mice(micedata, method = c(rep('rfcont', ncol(micedata)-1), 'rfcat'), m = 2, maxit = 2)
save(miceres, file = paste0("temp_", do, "_", as.numeric(Sys.time()), ".rda"))
}
if (do == 4) {
library(e1071)
load(paste0(path, type, "_nempi.rda"))
D4 <- D2
colnames(D4) <- apply(Rho, 2, function(x) {
Sgenes <- paste(sort(rownames(Rho)[which(x > 0)]), collapse = "_")
return(Sgenes)
})
rfres <- classpi(D4, full = TRUE, method = "randomForest")
save(rfres, file = paste0("temp_", do, "_", as.numeric(Sys.time()), ".rda"))
}
if (do == 5) {
library(e1071)
load(paste0(path, type, "_nempi.rda"))
D4 <- D2
colnames(D4) <- apply(Rho, 2, function(x) {
Sgenes <- paste(sort(rownames(Rho)[which(x > 0)]), collapse = "_")
return(Sgenes)
})
mfdata <- cbind(as.data.frame(t(D4)), colnames(D4))
mfdata[which(mfdata == "", arr.ind = TRUE)] <- NA
library(missForest)
mfimp <- missForest(mfdata)
D4 <- D2
colnames(D4) <- mfimp$ximp[, ncol(mfimp$ximp)]
tmp <- mynem(D4, multi = TRUE)
Gamma <- getGamma(D4)
ures <- list()
ures$Gamma <- apply(Gamma, 2, function(x) return(x/sum(x)))
ures$res <- list()
ures$res$adj <- tmp$adj
ures$null <- TRUE
ures$combi <- 1
mfres <- ures
save(mfres, file = paste0("temp_", do, "_", as.numeric(Sys.time()), ".rda"))
}
## knn
library(class)
train <- t(D4[, which(colnames(D4) != "")])
test <- t(D4[, which(colnames(D4) == "")])
knn0 <- 0
if (knn0) {
train <- rbind(train, NULL = rep(0, ncol(train)))
cl <- c(colnames(D4)[which(colnames(D4) != "")], "NULL")
} else {
cl <- colnames(D4)[which(colnames(D4) != "")]
}
knnres <- knn(train, test, cl, prob = TRUE)
D3 <- D4
colnames(D3)[which(colnames(D3) %in% "")] <- as.character(knnres)
tmp <- mynem(D3, multi = TRUE)
Gamma <- getGamma(D3)
ures <- list()
ures$Gamma <- Gamma # apply(Gamma, 2, function(x) return(x/sum(x)))
ures$res <- list()
ures$res$adj <- tmp$adj
ures$null <- TRUE
ures$combi <- 1
knnres <- ures
## save(nempires, knnres, rfres, mfres, svmres, nnres, Rho, D2, Pmut, Pmeth, Pcnv, file = paste0(path, type, "_nempi.rda"))
path <- "mutclust/"; type <- "TCGA-BRCA"
load(paste0(path, type, "_nempi.rda"))
## ## load("~/Mount/Euler/temp_1_1573814974.40659.rda") # old
## load("~/Mount/Euler/temp_1_1574076694.65703.rda")
## ## load("~/Mount/Euler/temp_4_1573819581.9749.rda") # old
## load("~/Mount/Euler/temp_4_1574080528.67352.rda")
## ## load("~/Mount/Euler/temp_2_1573821112.2412.rda") # old
## load("~/Mount/Euler/temp_2_1574084503.12431.rda")
## load("~/Mount/Euler/temp_5_1574081415.91547.rda")
## ures <- rfres
## ures <- mfres
## ures <- svmres
## ures <- nnres
## ures <- knnres
ures <- nempires
## check against methylation and cnvs:
pdf("nempi_gamma.pdf", width = 12, height = 6)
tmp <- ures$Gamma
colnames(tmp) <- NULL
epiNEM::HeatmapOP(tmp, bordercol = rgb(0,0,0,0), col = "RdBu", colorkey = NULL)
dev.off()
pdf("nempi_phi.pdf", width = 6, height = 6)
Pgenes <- sort(unique(colnames(D2)))
adjtmp <- ures$res$adj
colnames(adjtmp) <- rownames(adjtmp) <- Pgenes
plotDnf(adjtmp, edgelwd = 2)
dev.off()
## cnv/meth enrichment:
methods <- list("NEM$\\pi$" = nempires, knn = knnres, mf = mfres, nn = nnres, rf = rfres, svm = svmres)
mutinc <- 1
Lall <- Lcnv <- Lmeth <- Lmut <- list()
for (i in 1:length(methods)) {
if (i != 8) {
print(names(methods)[i])
ures <- methods[[i]]
newGamma <- ures$Gamma
} else {
print("random")
newGamma <- newGamma*0
newGamma[sample(1:length(newGamma), floor(0.45*length(newGamma)))] <- 1 # well that is included into the test...
}
hist(newGamma)
if (i == 1) {
rntmp <- rownames(newGamma); newGamma <- t(mytc(ures$res$adj))%*%newGamma; rownames(newGamma) <- rntmp
}
P <- Pmut+Pmeth+Pcnv
if (!mutinc) { # include mutations or not (0)
P[which(Pmut == 1)] <- 0
}
P <- P[which(rownames(P) %in% rownames(Rho)), which(colnames(P) %in% colnames(Rho))]
P <- P[order(rownames(P)), order(colnames(P))]
P[which(P > 1)] <- 1
Ptmp <- cbind(P, matrix(0, nrow(P), sum(!(colnames(Rho) %in% colnames(P)))))
colnames(Ptmp) <- c(colnames(P), colnames(Rho)[which(!(colnames(Rho) %in% colnames(P)))])
P <- Ptmp[, colnames(Rho)]
## fisher:
if (i %in% c(4,5)) {
cut <- 0.07
} else if (i == 6) {
cut <- 0.1
} else {
cut <- 1/8
}
newGamma[which(newGamma > cut)] <- 1
newGamma[which(newGamma <= cut)] <- 0
pmeth <- newGamma
pmeth[which(newGamma == 1 & P == 1)] <- 2
pmeth[which(newGamma == 0 & P == 1)] <- -2
if (!mutinc) { # include mutations or not (0)
pmeth[which(Rho > 0)] <- 0
}
colnames(pmeth) <- NULL
##pdf(paste0("FigS_", names(methods)[i], ".pdf"), height = 6, width = 12)
setEPS()
postscript(paste0("FigS_", names(methods)[i], ".eps"), height = 6, width = 12)
print(epiNEM::HeatmapOP(pmeth, bordercol = rgb(0,0,0,0), col = "RdBu", colorkey = NULL))
dev.off()
print("cnv")
P <- Pcnv
P <- P[which(rownames(P) %in% rownames(Rho)), which(colnames(P) %in% colnames(Rho))]
P <- P[order(rownames(P)), order(colnames(P))]
P[which(P > 1)] <- 1
Ptmp <- cbind(P, matrix(0, nrow(P), sum(!(colnames(Rho) %in% colnames(P)))))
colnames(Ptmp) <- c(colnames(P), colnames(Rho)[which(!(colnames(Rho) %in% colnames(P)))])
P <- Ptmp[, colnames(Rho)]
F <- matrix(c(sum(pmeth >= 1 & P == 1), sum(pmeth >= 1 & P == 0), sum(pmeth == -2 & P == 1), sum(pmeth == 0 & P == 0)), 2)
print(1 - phyper(F[1,1]-1, sum(F[, 1]), sum(F[, 2]), sum(F[1, ])))
Lcnv[[i]] <- F
print("meth")
P <- Pmeth
P <- P[which(rownames(P) %in% rownames(Rho)), which(colnames(P) %in% colnames(Rho))]
P <- P[order(rownames(P)), order(colnames(P))]
P[which(P > 1)] <- 1
Ptmp <- cbind(P, matrix(0, nrow(P), sum(!(colnames(Rho) %in% colnames(P)))))
colnames(Ptmp) <- c(colnames(P), colnames(Rho)[which(!(colnames(Rho) %in% colnames(P)))])
P <- Ptmp[, colnames(Rho)]
F <- matrix(c(sum(pmeth >= 1 & P == 1), sum(pmeth >= 1 & P == 0), sum(pmeth == -2 & P == 1), sum(pmeth == 0 & P == 0)), 2)
print(1 - phyper(F[1,1]-1, sum(F[, 1]), sum(F[, 2]), sum(F[1, ])))
Lmeth[[i]] <- F
print("mut")
P <- Pmut
P <- P[which(rownames(P) %in% rownames(Rho)), which(colnames(P) %in% colnames(Rho))]
P <- P[order(rownames(P)), order(colnames(P))]
P[which(P > 1)] <- 1
Ptmp <- cbind(P, matrix(0, nrow(P), sum(!(colnames(Rho) %in% colnames(P)))))
colnames(Ptmp) <- c(colnames(P), colnames(Rho)[which(!(colnames(Rho) %in% colnames(P)))])
P <- Ptmp[, colnames(Rho)]
F <- matrix(c(sum(pmeth >= 1 & P == 1), sum(pmeth >= 1 & P == 0), sum(pmeth == -2 & P == 1), sum(pmeth == 0 & P == 0)), 2)
print(1 - phyper(F[1,1]-1, sum(F[, 1]), sum(F[, 2]), sum(F[1, ])))
Lmut[[i]] <- F
Fmat <- matrix(c(sum(pmeth == 2), sum(pmeth == 1), sum(pmeth == -2), sum(pmeth == 0)), 2)
Lall[[i]] <- Fmat
## print(fisher.test(Fmat, alternative = "greater"))
print("p-value")
print(1 - phyper(Fmat[1,1]-1, sum(Fmat[, 1]), sum(Fmat[, 2]), sum(Fmat[1, ])))
}
## create tables:
for (i in 1:length(methods)) {
cat(paste0(names(methods)[i], " & ", Lcnv[[i]][1,1], " & ", Lcnv[[i]][2,1], " & ", Lcnv[[i]][2,2], " & ", Lcnv[[i]][1,2], "\\\\\n"))
}
for (i in 1:length(methods)) {
cat(paste0(names(methods)[i], " & ", Lmeth[[i]][1,1], " & ", Lmeth[[i]][2,1], " & ", Lmeth[[i]][2,2], " & ", Lmeth[[i]][1,2], "\\\\\n"))
}
for (i in 1:length(methods)) {
cat(paste0(names(methods)[i], " & ", Lmut[[i]][1,1], " & ", Lmut[[i]][2,1], " & ", Lmut[[i]][2,2], " & ", Lmut[[i]][1,2], "\\\\\n"))
}
for (i in 1:length(methods)) {
if (names(methods)[i] == "nn") { next() }
Fmat <- Lall[[i]]
ptmp <- 1 - phyper(Fmat[1,1]-1, sum(Fmat[, 1]), sum(Fmat[, 2]), sum(Fmat[1, ]))
if (ptmp == 0) {
ptmp <- "$< 2.2\\times10^{-16}$"
} else {
ptmp <- paste0("$", signif(ptmp), "$")
}
cat(paste0(names(methods)[i], " & ", Lall[[i]][1,1], " & ", Lall[[i]][2,1], " & ", Lall[[i]][2,2], " & ", Lall[[i]][1,2], " & ", ptmp, "\\\\\n"))
}
## check correlation
## P4 <- apply(P3, 2, function(x) return(x/sum(x)))
## P4[is.na(P4)] <- 0
## cor(as.vector(newGamma), as.vector(P3))
##
cormat <- matrix(0, nrow(pmeth), 2)
fishres <- numeric(nrow(pmeth))
names(fishres) <- rownames(pmeth)
for (i in 1:nrow(pmeth)) {
Fmat <- matrix(c(sum(pmeth[i, ] == 2), sum(pmeth[i, ] == 1), sum(pmeth[i, ] == -2), sum(pmeth[i, ] == 0)), 2)
fishres[i] <- fisher.test(Fmat, alternative = "g")$p.value
cormat[i, ] <- c(sum(Fmat[1, ]), sum(Fmat[, 1]))
}
## GATA3 & PTPRD:
Fmat <- matrix(c(sum(pmeth[3, ] %in% c(2,-2) & pmeth[7, ] %in% c(2,-2)),
sum(pmeth[3, ] %in% c(1,0) & pmeth[7, ] %in% c(-2,2)),
sum(pmeth[3, ] %in% c(-2,2) & pmeth[7, ] %in% c(1,0)),
sum(pmeth[3, ] %in% c(1,0) & pmeth[7, ] %in% c(1,0))), 2)
1 - phyper(Fmat[1,1]-1, sum(Fmat[, 1]), sum(Fmat[, 2]), sum(Fmat[1, ]))
## pca:
pca <- princomp(D2)
col <- apply(newGamma, 2, sum)
col <- col/max(col)
K <- kmeans(t(newGamma), nrow(newGamma))
plot(pca$loadings[, 1:2], col = K$cluster)#rgb(col,0,0,1))
## tsne:
sne <- tsne(t(newGamma))
plot(sne, col = K$cluster)
## R profiling:
Rprof("temp.txt", line.profiling=TRUE)
ures <- nempi(D2[1:20, ], Gamma = Gamma, complete = 1, full = TRUE, converged = converged, combi = combi)
Rprof(NULL)
summaryRprof("temp.txt", lines = "show")$sampling.time
head(summaryRprof("temp.txt", lines = "show")$by.self, 10)
##
type <- "TCGA-BRCA"
load(paste0(path, type, "_nempi.rda"))
pdf("Fig5.pdf", width = 10, height = 10)
plot(uresn, edgelwd = 2)
dev.off()
pdf("Fig6.pdf", width = 10, height = 5)
tmp <- uresn$Gamma
colnames(tmp) <- NULL
epiNEM::HeatmapOP(tmp, bordercol = rgb(0,0,0,0), col = "RdBu", colorkey = NULL)
dev.off()
pdf("Fig7.pdf", width = 10, height = 10)
phitmp <- mytc(uresn$res$adj)
tmp <- t(phitmp)%*%uresn$Gamma
colnames(tmp) <- NULL
rownames(tmp) <- rownames(uresn$Gamma)
tmp2 <- tmp
colnames(tmp) <- NULL
tmp <- Gamma
colnames(tmp) <- NULL
tmp3 <- tmp
tmp4 <- tmp2
p1 <- epiNEM::HeatmapOP(tmp2, bordercol = rgb(0,0,0,0), col = "RdBu", clusterx = tmp2, colorkey = NULL)
p2 <- epiNEM::HeatmapOP(tmp, bordercol = rgb(0,0,0,0), col = "RdBu", clusterx = tmp2, colorkey = NULL)
print(p1, position=c(0, .5, 1, 1), more=TRUE)
print(p2, position=c(0, 0, 1, .5))
sum(tmp == 1 & tmp2 == 1)/sum(tmp == 1)
dev.off()
pdf("Fig8.pdf", width = 10, height = 10)
plot(uresf, edgelwd = 2)
dev.off()
pdf("Fig9.pdf", width = 10, height = 5)
tmp <- uresf$Gamma
colnames(tmp) <- NULL
epiNEM::HeatmapOP(tmp, bordercol = rgb(0,0,0,0), col = "RdBu", colorkey = NULL)
dev.off()
pdf("Fig10.pdf", width = 10, height = 10)
phitmp <- mytc(uresf$res$adj)
tmp <- t(phitmp)%*%uresf$Gamma
colnames(tmp) <- NULL
rownames(tmp) <- rownames(uresf$Gamma)
tmp2 <- tmp
colnames(tmp) <- NULL
tmp <- Gamma
colnames(tmp) <- NULL
p1 <- epiNEM::HeatmapOP(tmp2, bordercol = rgb(0,0,0,0), col = "RdBu", clusterx = tmp2, colorkey = NULL)
p2 <- epiNEM::HeatmapOP(tmp, bordercol = rgb(0,0,0,0), col = "RdBu", clusterx = tmp2, colorkey = NULL)
print(p1, position=c(0, .5, 1, 1), more=TRUE)
print(p2, position=c(0, 0, 1, .5))
sum(tmp == 1 & tmp2 == 1)/sum(tmp == 1)
dev.off()
## new figure:
P <- t(mytc(uresf$res$adj))%*%uresf$Gamma
rownames(P) <- rownames(uresf$Gamma)
PM <- P
PM[which(PM > 1/6 & Gamma == 1)] <- P[which(PM > 1/6 & Gamma == 1)] + 1
PM[which(PM <= 1/6 & Gamma == 1)] <- P[which(PM <= 1/6 & Gamma == 1)] - 1
epiNEM::HeatmapOP(PM, bordercol = rgb(0,0,0,0), col = "RdYlBu", breaks = seq(-1,2,length.out=5), clusterx = tmp2)
## other plots:
pdf("temp.pdf", width = 10, height = 10)
plotDnf(c("M1=M4", "M2=M4", "M3=M5", "M1=M5"), edgelwd = 2)
dev.off()
M <- matrix(0, 5, 10)
rownames(M) <- paste0("M", 1:5)
colnames(M) <- paste0("S", 1:10)
M[1, 1:4] <- 1
M[2, c(2,7:9)] <- 1
M[3, 5:6] <- 1
M[3, 10] <- 1
phi <- matrix(0, 5, 5)
diag(phi) <- 1
phi[1, 1:5] <- 1
phi[2, 3] <- phi[4, 5] <- 1
## M <- t(phi)%*%M; M[M > 1] <- 1
rownames(M) <- paste0("M", 1:5)
pdf("temp.pdf", width = 8, height = 4)
epiNEM::HeatmapOP(M, Colv = 0, Rowv = 0, col = "RdBu", colorkey = NULL)
dev.off()
## check for mutation type (das führt zu nichts)
colnames(Mtype) <- unlist(lapply(strsplit(colnames(Mtype), "-"), function(x) {
y <- x[1:3]
y <- paste(c(y, "01"), collapse = "-")
return(y)
}))
checkgene <- "CBFB"
van <- intersect(colnames(Gamma)[which(PM[checkgene, ] < 0)], colnames(Mtype))
A <- sum(unlist(lapply(strsplit(Mtype[checkgene, van], " "), function(x) if ("Silent" %in% x) { return(1) } else { return(0) })))
B <- length(van) - A
van <- intersect(colnames(Gamma)[which(PM[checkgene, ] > 0)], colnames(Mtype))
C <- sum(unlist(lapply(strsplit(Mtype[checkgene, van], " "), function(x) if ("Silent" %in% x) { return(1) } else { return(0) })))
D <- length(van) - C
table(unlist(lapply(strsplit(Mtype[checkgene, van], " "), function(x) return(names(table(x))[which.max(table(x))]))))
## table(unlist(strsplit(as.vector(Mtype), " ")))
## pdf("Fig10.pdf", width = 10, height = 10)
## p1 <- epiNEM::HeatmapOP(tmp4, bordercol = rgb(0,0,0,0), col = "RdBu", clusterx = tmp2, colorkey = NULL)
## p2 <- epiNEM::HeatmapOP(tmp3, bordercol = rgb(0,0,0,0), col = "RdBu", clusterx = tmp2, colorkey = NULL)
## p3 <- epiNEM::HeatmapOP(tmp2, bordercol = rgb(0,0,0,0), col = "RdBu", clusterx = tmp2, colorkey = NULL)
## p4 <- epiNEM::HeatmapOP(tmp, bordercol = rgb(0,0,0,0), col = "RdBu", clusterx = tmp2, colorkey = NULL)
## print(p1, position=c(0, .5, .5, 1), more=TRUE)
## print(p2, position=c(0, 0, .5, .5), more=TRUE)
## print(p3, position=c(.5, .5, 1, 1), more=TRUE)
## print(p4, position=c(.5, 0, 1, .5))
## sum(tmp == 1 & tmp2 == 1)/sum(tmp == 1)
## dev.off()
source("~/Documents/testing/R/nempi.r")
source("~/Documents/testing/R/nempi_low.r")
bsres <- unembs(D2, Gamma = Gamma, complete = 1, full = TRUE, converged = converged, combi = combi, bsruns = 10, bssize = 0.5)
pdf("temp.pdf", width = 10, height = 5)
epiNEM::HeatmapOP(bsres$Gamma, bordercol = rgb(0,0,0,0), col = "RdBu", colorkey = NULL)
dev.off()
pdf("temp.pdf", width = 20, height = 10)
par(mfrow=c(1,2))
tmp <- bsres$phi
tmp[which(tmp > 0)] <- 1
diag(tmp) <- 0
freqtop <- as.vector(t(bsres$phi)[which(lower.tri(bsres$phi) == TRUE)])/10
freqtop <- freqtop[which(freqtop != 0)]
tmptop <- tmp
tmptop[lower.tri(tmptop)] <- 0
tmptop <- adj2dnf(tmptop)
tmptop <- tmptop[grep("=", tmptop)]
plotDnf(tmptop, edgelwd = 2, edgelab = freqtop, freq = freqtop)
freqbot <- as.vector(t(bsres$phi)[which(upper.tri(bsres$phi) == TRUE)])/10
freqbot <- freqbot[which(freqbot != 0)]
tmpbot <- tmp
tmpbot[upper.tri(tmpbot)] <- 0
tmpbot <- adj2dnf(tmpbot)
tmpbot <- tmpbot[grep("=", tmpbot)]
plotDnf(tmpbot, edgelwd = 2, edgelab = freqbot, freq = freqbot)
dev.off()
##
source("testing/vignettes/TCGA_cluster.r")
pcares <- prcomp(tmp)
library(naturalsort)
library(nem)
library(cluster)
library(Rcpp)
source("~/Documents/mnem/R/mnems.r")
source("~/Documents/mnem/R/mnems_low.r")
sourceCpp("~/Documents/mnem/src/mm.cpp")
res <- mnem(tmp, starts = 10, search = "greedy", type = "cluster3", complete = 1, multi = 1)
tmp2 <- tmp
Rprof("temp.txt", line.profiling=TRUE)
res <- mnem(tmp2, starts = 10, search = "greedy", type = "cluster3", complete = 1, multi = 1, k = 2)
Rprof(NULL)
summaryRprof("temp.txt", lines = "show")$sampling.time
head(summaryRprof("temp.txt", lines = "show")$by.self)
## resk <- mnemk(tmp2, starts = 10, search = "estimate", type = "cluster3", complete = 1, multi = 1)
cluster <- apply(getAffinity(res$probs, mw = res$mw, complete = TRUE), 2, which.max)
par(mfrow=c(1,2))
plot(pcares$rotation[, 1:2], col = cluster)
names(cluster) <- gsub("-01$|-03$", "", colnames(M))
cluster <- cluster[which(names(cluster) %in% clinical$submitter_id)]
cluster <- cluster[order(names(cluster))]
clinical <- rbind(clinical, clinical[which(clinical[, 1] %in% names(cluster)[which(duplicated(names(cluster)))]), ])
clinical <- clinical[order(clinical[, 1]), ]
print(all(clinical[, 1] == names(cluster)))
fit <- survfit(Surv(days_to_death, vital_status) ~ cluster, clinical)
plot(fit, col = 1:length(table(cluster)), lty = 1:length(table(cluster)))
legend(max(clinical$days_to_death, na.rm = TRUE), 1, 1:length(table(cluster)), lty = 1:length(table(cluster)), col = 1:length(table(cluster)), xjust = 1, yjust = 1)
fit <- coxph(Surv(days_to_death, vital_status) ~ cluster + age_at_diagnosis, clinical)
print(fit)
fit <- coxph(Surv(days_to_death, vital_status) ~ cluster + age_at_diagnosis + tumor_stage, clinical)
print(fit)
fit <- coxph(Surv(days_to_death, vital_status) ~ cluster + tumor_stage, clinical)
print(fit)
fit <- coxph(Surv(days_to_death, vital_status) ~ cluster, clinical)
print(fit)
kres <- clustNEM(tmp, nem = 0, k = length(res$comp), nstart = 10)
## kres <- clustNEM(tmp, nem = 0, nstart = 10)
plot(pcares$rotation[, 1:2], col = kres$cluster)
kcluster <- kres$cluster
names(kcluster) <- gsub("-01$|-03$", "", colnames(M))
kcluster <- kcluster[which(names(kcluster) %in% clinical$submitter_id)]
kcluster <- kcluster[order(names(kcluster))]
fit <- survfit(Surv(days_to_death, vital_status) ~ kcluster, clinical)
plot(fit, col = 1:length(table(cluster)), lty = 1:length(table(cluster)))
legend(max(clinical$days_to_death, na.rm = TRUE), 1, 1:length(table(cluster)), lty = 1:length(table(cluster)), col = 1:length(table(cluster)), xjust = 1, yjust = 1)
fit <- coxph(Surv(days_to_death, vital_status) ~ kcluster + age_at_diagnosis, clinical)
print(fit)
fit <- coxph(Surv(days_to_death, vital_status) ~ kcluster, clinical)
print(fit)
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