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Avoid densification #139

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5 changes: 3 additions & 2 deletions DESCRIPTION
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Expand Up @@ -16,8 +16,9 @@ Imports:
RColorBrewer,
mixtools,
cluster,
MCMCpack
RoxygenNote: 7.1.2
MCMCpack,
Matrix
RoxygenNote: 7.3.3
Suggests:
knitr,
rmarkdown
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2 changes: 2 additions & 0 deletions NAMESPACE
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@@ -1,4 +1,6 @@
# Generated by roxygen2: do not edit by hand
importFrom(Matrix,colSums)
importFrom(Matrix,rowSums)
export(copykat)
export(CNA.MCMC)
export(annotateGenes.hg20)
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4 changes: 2 additions & 2 deletions R/annotateGenes.hg20.R
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Expand Up @@ -18,7 +18,7 @@ annotateGenes.hg20 <- function(mat, ID.type="S"){
anno <- full.anno[which(as.vector(full.anno$ensembl_gene_id) %in% shar),]
anno <- anno[!duplicated(anno$hgnc_symbol),]
anno <- anno[order(match(anno$ensembl_gene_id, rownames(mat))),]
data <- cbind(anno, mat)
list(anno = anno, counts = mat)

}else if(substring(ID.type,1,1) %in% c("S", "s")) {

Expand All @@ -27,7 +27,7 @@ annotateGenes.hg20 <- function(mat, ID.type="S"){
anno <- full.anno[which(as.vector(full.anno$hgnc_symbol) %in% shar),]
anno <- anno[!duplicated(anno$hgnc_symbol),]
anno <- anno[order(match(anno$hgnc_symbol, rownames(mat))),]
data <- cbind(anno, mat)
list(anno = anno, counts = mat)
}
}

4 changes: 2 additions & 2 deletions R/annotateGenes.mm10.R
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Expand Up @@ -20,7 +20,7 @@ annotateGenes.mm10 <- function(mat, ID.type="S", full.anno=full.anno.mm10){
anno <- full.anno[which(as.vector(full.anno$ensembl_gene_id) %in% shar),]
anno <- anno[!duplicated(anno$mgi_symbol),]
anno <- anno[order(match(anno$ensembl_gene_id, rownames(mat))),]
data <- cbind(anno, mat)
list(anno = anno, counts = mat)

}else if(substring(ID.type,1,1) %in% c("S", "s")) {

Expand All @@ -32,7 +32,7 @@ annotateGenes.mm10 <- function(mat, ID.type="S", full.anno=full.anno.mm10){
anno <- full.anno[which(as.vector(full.anno$mgi_symbol) %in% shar),]
anno <- anno[!duplicated(anno$mgi_symbol),]
anno <- anno[order(match(anno$mgi_symbol, rownames(mat))),]
data <- cbind(anno, mat)
list(anno = anno, counts = mat)
}
}

138 changes: 73 additions & 65 deletions R/copykat.R
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Expand Up @@ -23,6 +23,7 @@

#'
#' test.pred <- test.ck$prediction
#' @importFrom Matrix colSums rowSums
#' @export
###

Expand All @@ -38,15 +39,15 @@ start_time <- Sys.time()
print("step1: read and filter data ...")
print(paste(nrow(rawmat), " genes, ", ncol(rawmat), " cells in raw data", sep=""))

genes.raw <- apply(rawmat, 2, function(x)(sum(x>0)))
genes.raw <- colSums(rawmat != 0)

if(sum(genes.raw> min.gene.per.cell)==0) stop("none cells have more than min.gene.per.cell")
if(sum(genes.raw<min.gene.per.cell)>1){
rawmat <- rawmat[, -which(genes.raw< min.gene.per.cell)]
print(paste("filtered out ", sum(genes.raw<=min.gene.per.cell), " cells with less than min.gene.per.cell; remaining ", ncol(rawmat), " cells", sep=""))
}
##
der<- apply(rawmat,1,function(x)(sum(x>0)))/ncol(rawmat)
der <- rowSums(rawmat != 0)/ncol(rawmat)

if(sum(der>LOW.DR)>=1){
rawmat <- rawmat[which(der > LOW.DR), ]; print(paste(nrow(rawmat)," genes past LOW.DR filtering", sep=""))
Expand All @@ -61,66 +62,69 @@ start_time <- Sys.time()

print("step 2: annotations gene coordinates ...")
if(genome=="hg20"){
anno.mat <- annotateGenes.hg20(mat = rawmat, ID.type = id.type) #SYMBOL or ENSEMBLE
anno.list <- annotateGenes.hg20(mat = rawmat, ID.type = id.type) #SYMBOL or ENSEMBLE
} else if(genome=="mm10"){
anno.mat <- annotateGenes.mm10(mat = rawmat, ID.type = id.type) #SYMBOL or ENSEMBLE
anno.list <- annotateGenes.mm10(mat = rawmat, ID.type = id.type) #SYMBOL or ENSEMBLE
dim(rawmat)
}
anno.mat <- anno.mat[order(as.numeric(anno.mat$abspos), decreasing = FALSE),]
ord <- order(as.numeric(anno.list$anno$abspos), decreasing = FALSE)
anno.list$anno <- anno.list$anno[ord, ]
anno.list$counts <- anno.list$counts[ord, ]

# print(paste(nrow(anno.mat)," genes annotated", sep=""))
# print(paste(nrow(anno.list$anno)," genes annotated", sep=""))

### module 3 removing genes that are involved in cell cycling

if(genome=="hg20"){
HLAs <- anno.mat$hgnc_symbol[grep("^HLA-", anno.mat$hgnc_symbol)]
toRev <- which(anno.mat$hgnc_symbol %in% c(as.vector(cyclegenes[[1]]), HLAs))
HLAs <- anno.list$anno$hgnc_symbol[grep("^HLA-", anno.list$anno$hgnc_symbol)]
toRev <- which(anno.list$anno$hgnc_symbol %in% c(as.vector(cyclegenes[[1]]), HLAs))
if(length(toRev)>0){
anno.mat <- anno.mat[-toRev, ]
anno.list$anno <- anno.list$anno[-toRev, ]
anno.list$counts <- anno.list$counts[-toRev, ]
}
}
# print(paste(nrow(anno.mat)," genes after rm cell cycle genes", sep=""))
# print(paste(nrow(anno.list$anno)," genes after rm cell cycle genes", sep=""))
### secondary filtering
ToRemov2 <- NULL
for(i in 8:ncol(anno.mat)){
cell <- cbind(anno.mat$chromosome_name, anno.mat[,i])
cell <- cell[cell[,2]!=0,]
if(length(as.numeric(cell))< 5){
rm <- colnames(anno.mat)[i]
ToRemov2 <- c(ToRemov2, rm)
} else if(length(rle(cell[,1])$length)<length(unique((anno.mat$chromosome_name)))|min(rle(cell[,1])$length)< ngene.chr){
rm <- colnames(anno.mat)[i]
ToRemov2 <- c(ToRemov2, rm)
chrom_vec <- anno.list$anno$chromosome_name
ToRemov2 <- character(0)
for(j in seq_len(ncol(anno.list$counts))) {
nz_idx <- which(anno.list$counts[, j] != 0)
if(length(nz_idx) < 5) {
ToRemov2 <- c(ToRemov2, colnames(anno.list$counts)[j])
} else {
chr_nz <- chrom_vec[nz_idx]
rl <- rle(chr_nz)
if(length(rl$lengths) < length(unique(chrom_vec)) || min(rl$lengths) < ngene.chr)
ToRemov2 <- c(ToRemov2, colnames(anno.list$counts)[j])
}
i<- i+1
}

if(length(ToRemov2)==(ncol(anno.mat)-7)) stop("all cells are filtered")
if(length(ToRemov2)==ncol(anno.list$counts)) stop("all cells are filtered")
if(length(ToRemov2)>0){
anno.mat <-anno.mat[, -which(colnames(anno.mat) %in% ToRemov2)]
anno.list$counts <- anno.list$counts[, !(colnames(anno.list$counts) %in% ToRemov2), drop=FALSE]
}

# print(paste("filtered out ", length(ToRemov2), " cells with less than ",ngene.chr, " genes per chr", sep=""))
rawmat3 <- data.matrix(anno.mat[, 8:ncol(anno.mat)])
norm.mat<- log(sqrt(rawmat3)+sqrt(rawmat3+1))
norm.mat<- apply(norm.mat,2,function(x)(x <- x-mean(x)))
colnames(norm.mat) <- colnames(rawmat3)

#print(paste("A total of ", ncol(norm.mat), " cells, ", nrow(norm.mat), " genes after preprocessing", sep=""))
## compute DR2 from sparse counts before densification
DR2 <- rowSums(anno.list$counts != 0) / ncol(anno.list$counts)

##smooth data
##smooth data — normalize per-column to avoid building a full dense rawmat3/norm.mat
print("step 3: smoothing data with dlm ...")
dlm.sm <- function(c){
dlm.sm <- function(j){
col_raw <- as.numeric(anno.list$counts[, j])
col_norm <- log(sqrt(col_raw) + sqrt(col_raw + 1))
col_norm <- col_norm - mean(col_norm)
model <- dlm::dlmModPoly(order=1, dV=0.16, dW=0.001)
x <- dlm::dlmSmooth(norm.mat[, c], model)$s
x<- x[2:length(x)]
x <- x-mean(x)
x <- dlm::dlmSmooth(col_norm, model)$s
x <- x[2:length(x)]
x - mean(x)
}

test.mc <-parallel::mclapply(1:ncol(norm.mat), dlm.sm, mc.cores = n.cores)
norm.mat.smooth <- matrix(unlist(test.mc), ncol = ncol(norm.mat), byrow = FALSE)
test.mc <- parallel::mclapply(seq_len(ncol(anno.list$counts)), dlm.sm, mc.cores = n.cores)
norm.mat.smooth <- matrix(unlist(test.mc), ncol = ncol(anno.list$counts), byrow = FALSE)

colnames(norm.mat.smooth) <- colnames(norm.mat)
colnames(norm.mat.smooth) <- colnames(anno.list$counts)

print("step 4: measuring baselines ...")
if (cell.line=="yes"){
Expand Down Expand Up @@ -182,25 +186,29 @@ start_time <- Sys.time()
}

###use a smaller set of genes to perform segmentation
DR2 <- apply(rawmat3,1,function(x)(sum(x>0)))/ncol(rawmat3)
## DR2 was already computed from sparse counts before dlm smoothing
##relative expression using pred.normal cells
norm.mat.relat <- norm.mat.relat[which(DR2>=UP.DR),]

###filter cells
anno.mat2 <- anno.mat[which(DR2>=UP.DR), ]

ToRemov3 <- NULL
for(i in 8:ncol(anno.mat2)){
cell <- cbind(anno.mat2$chromosome_name, anno.mat2[,i])
cell <- cell[cell[,2]!=0,]
if(length(as.numeric(cell))< 5){
rm <- colnames(anno.mat2)[i]
ToRemov3 <- c(ToRemov3, rm)
} else if(length(rle(cell[,1])$length)<length(unique((anno.mat$chromosome_name)))|min(rle(cell[,1])$length)< ngene.chr){
rm <- colnames(anno.mat2)[i]
ToRemov3 <- c(ToRemov3, rm)
keep_rows2 <- which(DR2 >= UP.DR)
anno.mat2 <- list(
anno = anno.list$anno[keep_rows2, ],
counts = anno.list$counts[keep_rows2, , drop=FALSE]
)

chrom_vec2 <- anno.mat2$anno$chromosome_name
ToRemov3 <- character(0)
for(j in seq_len(ncol(anno.mat2$counts))) {
nz_idx <- which(anno.mat2$counts[, j] != 0)
if(length(nz_idx) < 5) {
ToRemov3 <- c(ToRemov3, colnames(anno.mat2$counts)[j])
} else {
chr_nz <- chrom_vec2[nz_idx]
rl <- rle(chr_nz)
if(length(rl$lengths) < length(unique(chrom_vec)) || min(rl$lengths) < ngene.chr)
ToRemov3 <- c(ToRemov3, colnames(anno.mat2$counts)[j])
}
i<- i+1
}

if(length(ToRemov3)==ncol(norm.mat.relat)) stop ("all cells are filtered")
Expand Down Expand Up @@ -232,7 +240,7 @@ start_time <- Sys.time()

colnames(results$logCNA) <- colnames(norm.mat.relat)
results.com <- apply(results$logCNA,2, function(x)(x <- x-mean(x)))
RNA.copycat <- cbind(anno.mat2[, 1:7], results.com)
RNA.copycat <- cbind(anno.mat2$anno, results.com)

write.table(RNA.copycat, paste(sample.name, "CNA_raw_results_gene_by_cell.txt", sep=""), sep="\t", row.names = FALSE, quote = F)

Expand Down Expand Up @@ -289,8 +297,8 @@ start_time <- Sys.time()
### add a step to plot out gene by cell matrix
if(plot.genes=="TRUE"){

rownames(results.com) <- anno.mat2$hgnc_symbol
chrg <- as.numeric(anno.mat2$chrom) %% 2+1
rownames(results.com) <- anno.mat2$anno$hgnc_symbol
chrg <- as.numeric(anno.mat2$anno$chrom) %% 2+1
rbPal1g <- colorRampPalette(c('black','grey'))
CHRg <- rbPal1(2)[as.numeric(chrg)]
chr1g <- cbind(CHRg,CHRg)
Expand Down Expand Up @@ -318,8 +326,8 @@ start_time <- Sys.time()
### add a step to plot out gene by cell matrix
if(plot.genes=="TRUE"){

rownames(results.com) <- anno.mat2$hgnc_symbol
chrg <- as.numeric(anno.mat2$chrom) %% 2+1
rownames(results.com) <- anno.mat2$anno$hgnc_symbol
chrg <- as.numeric(anno.mat2$anno$chrom) %% 2+1
rbPal1g <- colorRampPalette(c('black','grey'))
CHRg <- rbPal1(2)[as.numeric(chrg)]
chr1g <- cbind(CHRg,CHRg)
Expand Down Expand Up @@ -477,8 +485,8 @@ start_time <- Sys.time()
### add a step to plot out gene by cell matrix
if(plot.genes=="TRUE"){
dim(results.com)
rownames(results.com) <- anno.mat2$hgnc_symbol
chrg <- as.numeric(anno.mat2$chrom) %% 2+1
rownames(results.com) <- anno.mat2$anno$hgnc_symbol
chrg <- as.numeric(anno.mat2$anno$chrom) %% 2+1
rbPal1g <- colorRampPalette(c('black','grey'))
CHRg <- rbPal1(2)[as.numeric(chrg)]
chr1g <- cbind(CHRg,CHRg)
Expand Down Expand Up @@ -511,8 +519,8 @@ start_time <- Sys.time()
### add a step to plot out gene by cell matrix
if(plot.genes=="TRUE"){
dim(results.com)
rownames(results.com) <- anno.mat2$hgnc_symbol
chrg <- as.numeric(anno.mat2$chrom) %% 2+1
rownames(results.com) <- anno.mat2$anno$hgnc_symbol
chrg <- as.numeric(anno.mat2$anno$chrom) %% 2+1
rbPal1g <- colorRampPalette(c('black','grey'))
CHRg <- rbPal1(2)[as.numeric(chrg)]
chr1g <- cbind(CHRg,CHRg)
Expand Down Expand Up @@ -678,15 +686,15 @@ start_time <- Sys.time()
write.table(res, paste(sample.name, "prediction.txt",sep=""), sep="\t", row.names = FALSE, quote = FALSE)

####save copycat CNA
write.table(cbind(anno.mat2[, 1:7], mat.adj), paste(sample.name, "CNA_results.txt", sep=""), sep="\t", row.names = FALSE, quote = F)
write.table(cbind(anno.mat2$anno, mat.adj), paste(sample.name, "CNA_results.txt", sep=""), sep="\t", row.names = FALSE, quote = F)

####%%%%%%%%%%%%%%%%%next heatmaps, subpopulations and tSNE overlay
print("step 10: ploting heatmap ...")
my_palette <- colorRampPalette(rev(RColorBrewer::brewer.pal(n = 3, name = "RdBu")))(n = 999)

rownames(mat.adj) <- anno.mat2$mgi_symbol
chrg <- as.numeric(anno.mat2$chromosome_name) %% 2+1
rle(as.numeric(anno.mat2$chromosome_name))
rownames(mat.adj) <- anno.mat2$anno$mgi_symbol
chrg <- as.numeric(anno.mat2$anno$chromosome_name) %% 2+1
rle(as.numeric(anno.mat2$anno$chromosome_name))
rbPal1g <- colorRampPalette(c('black','grey'))
CHRg <- rbPal1g(2)[as.numeric(chrg)]
chr1g <- cbind(CHRg,CHRg)
Expand Down Expand Up @@ -734,7 +742,7 @@ start_time <- Sys.time()
if(output.seg=="TRUE"){
print("generating seg files for IGV viewer")

thisRatio <- cbind(anno.mat2[, c(2,3,1)], mat.adj)
thisRatio <- cbind(anno.mat2$anno[, c(2,3,1)], mat.adj)
Short <- NULL
chr <- rle(thisRatio$chromosome_name)[[2]]

Expand Down Expand Up @@ -778,7 +786,7 @@ start_time <- Sys.time()
end_time<- Sys.time()
print(end_time -start_time)

reslts <- list(res, cbind(anno.mat2[, 1:7], mat.adj), hcc)
reslts <- list(res, cbind(anno.mat2$anno, mat.adj), hcc)
names(reslts) <- c("prediction", "CNAmat","hclustering")
return(reslts)

Expand Down