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conumee_wrappers.r
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conumee_wrappers.r
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.cumsum0 <- function(x, left = TRUE, right = FALSE, n = NULL) {
xx <- c(0, cumsum(as.numeric(x)))
if (!left)
xx <- xx[-1]
if (!right)
xx <- head(xx, -1)
names(xx) <- n
xx
}
get_gains <- function(gains, pos, sample_ids, h){
gains_new <- gains
for (i in 1:length(pos)){
if (i==1){
start <- 0
}
else {
start <- pos[i-1]
}
if (i<44){
end <- pos[i]
chrs_1 <- h[(h>=start)&(h<=end)]
}
else{
chrs_1 <- h[h>=start]
}
v_sum <- gains[names(chrs_1)] #sum(gains[names(chrs_1)])
v_len <- 1 #length(gains[names(chrs_1)])
v_prop <- v_sum/(v_len*length(sample_ids))
gains_new[names(chrs_1)] <- v_prop
}
return(gains_new)
}
get_gains_regions <- function(gains_regions, sample_ids){
gains_regions_new <- gains_regions/length(sample_ids)
return(gains_regions_new)
}
composite_cnv_dist_gr <- function(high_sample_ids, ctrl_ids, anno, minfi.data, savename,
ylim, cols, chr, chrX, chrY, centromere, main){
########### Gains
id_n = 1
# message(id_n)
colors = c("green", "skyblue")
pdf(savename, width=20)
for (ids in list(high_sample_ids)){
for (sample in ids){
message(paste("processing sample", sample))
x <- CNV.fit(minfi.data[sample], minfi.data[ctrls], anno)
x <- CNV.bin(x)
x <- CNV.detail(x)
x <- CNV.segment(x)
bin.ratio <- x@bin$ratio - x@bin$shift # Adjust for shift, different per sample
detail.ratio <- x@detail$ratio - x@bin$shift
bin.ratio[bin.ratio < ylim[1]] <- ylim[1] # Clip values
bin.ratio[bin.ratio > ylim[2]] <- ylim[2]
if (sample==ids[1]){
if (sample==high_sample_ids[1]){
message("Starting")
if (is.null(main))
main <- x@name
if (chr[1] == "all") {
chr <- x@anno@genome$chr
} else {
chr <- intersect(chr, x@anno@genome$chr)
}
plot(NA, xlim = c(0, sum(as.numeric(x@anno@genome[chr, "size"])) -
0), ylim = ylim, xaxs = "i", xaxt = "n", yaxt = "n", xlab = NA,
ylab = NA, main = main)
chr.cumsum0 <- .cumsum0(x@anno@genome[chr, "size"], n = chr)
if (!chrX & is.element("chrX", names(chr.cumsum0)))
chr.cumsum0["chrX"] <- NA
if (!chrY & is.element("chrY", names(chr.cumsum0)))
chr.cumsum0["chrY"] <- NA
abline(v = .cumsum0(x@anno@genome[chr, "size"], right = TRUE),
col = "grey")
if (centromere) {
abline(v = .cumsum0(x@anno@genome[chr, "size"]) + x@anno@genome[chr,
"pq"], col = "grey", lty = 2)
}
axis(1, at = .cumsum0(x@anno@genome[chr, "size"]) + x@anno@genome[chr,
"size"]/2, labels = x@anno@genome[chr, "chr"], las = 2)
if (all(ylim == c(-1.25, 1.25))) {
axis(2, at = round(seq(-1.2, 1.2, 0.2), 1), las = 2)
} else {
axis(2, las = 2)
}
}
gains <- bin.ratio
gains[gains>=0.4] = 1
gains[gains<0.4] = 0
gains_regions <- detail.ratio
gains_regions[gains_regions>=0.4] = 1
gains_regions[gains_regions<0.4] = 0
}
else {
# gains
gains_tmp <- bin.ratio
gains_tmp[gains_tmp>=0.4] = 1
gains_tmp[gains_tmp<0.4] = 0
gains_regions_tmp <- detail.ratio
gains_regions_tmp[gains_regions_tmp>=0.4] = 1
gains_regions_tmp[gains_regions_tmp<0.4] = 0
gains <- gains + gains_tmp
gains_regions <- gains_regions + gains_regions_tmp
}
}
# chr
h = chr.cumsum0[as.vector(seqnames(x@anno@bins))] + values(x@anno@bins)$midpoint
names(h) <- names(gains)
pq_pos = .cumsum0(x@anno@genome[chr, "size"]) + x@anno@genome[chr,
"pq"]
chr_pos <- .cumsum0(x@anno@genome[x@anno@genome$chr, "size"])
pos = c(rbind(chr_pos, pq_pos))
gains_new <- get_gains(gains, pos, ids, h)
lines(chr.cumsum0[as.vector(seqnames(x@anno@bins))] + values(x@anno@bins)$midpoint,
gains_new, type = "h", pch = 16, cex = 0.4, col=colors[id_n])
# gene regions
gains_regions_new <- get_gains_regions(gains_regions, ids)
# detail.ratio.above <- (gains_regions_new > 0 & gains_regions_new < 0.85) |
# gains_regions_new < -0.85
lines(start(x@anno@detail) + (end(x@anno@detail) - start(x@anno@detail)) /2
+ chr.cumsum0[as.vector(seqnames(x@anno@detail))],
gains_regions_new, type = "p", pch = 21, col="black", bg=colors[id_n])
#bg=alpha(colors[id_n], 0.5))
if (id_n==1){ # Label only once
labels <- values(x@anno@detail)$name
labels <- gsub("\\/", "-", labels)
print(labels)
text(start(x@anno@detail) + (end(x@anno@detail) - start(x@anno@detail)) /2
+ chr.cumsum0[as.vector(seqnames(x@anno@detail))], gains_regions_new, # ifelse(detail.ratio.above, detail.ratio, NA),
labels = labels, adj = c(0,
0.5), srt = 90, col = "darkblue")
# text(start(x@anno@detail) + (end(x@anno@detail) - start(x@anno@detail)) /2
# + chr.cumsum0[as.vector(seqnames(x@anno@detail))], gains_regions_new, # ifelse(detail.ratio.above, NA, detail.ratio),
# labels = labels, adj = c(1, 0.5), srt = 90, col = "darkblue")
}
id_n <- 2
}
########### Losses
id_n = 1
colors = c("red", "blue")
for (ids in list(high_sample_ids)){
for (sample in ids){
message(paste("processing sample", sample))
x <- CNV.fit(minfi.data[sample], minfi.data[ctrls], anno)
x <- CNV.bin(x)
x <- CNV.detail(x)
x <- CNV.segment(x)
bin.ratio <- x@bin$ratio - x@bin$shift # Adjust for shift, different per sample
detail.ratio <- x@detail$ratio - x@bin$shift
bin.ratio[bin.ratio < ylim[1]] <- ylim[1] # Clip values
bin.ratio[bin.ratio > ylim[2]] <- ylim[2]
if (sample==ids[1]){
gains <- bin.ratio
gains[gains>-0.4] = 0
gains[gains<=-0.4] = 1
gains_regions <- detail.ratio
gains_regions[gains_regions>-0.4] = 0
gains_regions[gains_regions<=-0.4] = 1
}
else {
# gains
gains_tmp <- bin.ratio
gains_tmp[gains_tmp>-0.4] = 0
gains_tmp[gains_tmp<=-0.4] = 1
gains_regions_tmp <- detail.ratio
gains_regions_tmp[gains_regions_tmp>-0.4] = 0
gains_regions_tmp[gains_regions_tmp<=-0.4] = 1
gains <- gains + gains_tmp
gains_regions <- gains_regions + gains_regions_tmp
}
}
# chr
h = chr.cumsum0[as.vector(seqnames(x@anno@bins))] + values(x@anno@bins)$midpoint
names(h) <- names(gains)
pq_pos = .cumsum0(x@anno@genome[chr, "size"]) + x@anno@genome[chr,
"pq"]
chr_pos <- .cumsum0(x@anno@genome[x@anno@genome$chr, "size"])
pos = c(rbind(chr_pos, pq_pos))
gains_new <- get_gains(gains, pos, ids, h)
lines(chr.cumsum0[as.vector(seqnames(x@anno@bins))] + values(x@anno@bins)$midpoint,
-gains_new, type = "h", pch = 16, cex = 0.4, col=colors[id_n])
# gene regions
gains_regions_new <- get_gains_regions(gains_regions, ids)
# detail.ratio.above <- (gains_regions_new > 0 & gains_regions_new < 0.85) |
# gains_regions_new < -0.85
lines(start(x@anno@detail) + (end(x@anno@detail) - start(x@anno@detail)) /2
+ chr.cumsum0[as.vector(seqnames(x@anno@detail))],
-gains_regions_new, type = "p", pch = 21, col="black", bg=colors[id_n])
# bg=alpha(colors[id_n], 0.5))
id_n <- 2
}
legend("topright", inset=c(-0.2,0), bg="transparent", legend=c("Low neuronal (gains)",
"High neuronal (gains)",
"Low neuronal (losses)",
"High neuronal (losses)"),
fill=c("skyblue", "green", "blue", "red"), title="Group")
dev.off()
}
composite_cnv_dist_gr_1 <- function(high_sample_ids, low_sample_ids, cell_culture, h_cortex, neun, ctrl_ids,
anno, minfi.data, savename, ylim, cols, chr, chrX, chrY, centromere, main){
########### Gains
id_n = 1
colors = c("green", "skyblue", "gray", "pink", "red")
pdf(savename, width=20)
for (ids in list(high_sample_ids, low_sample_ids, cell_culture, h_cortex, neun)){
for (sample in ids){
message(paste("processing sample", sample))
x <- CNV.fit(minfi.data[sample], minfi.data[ctrls], anno)
x <- CNV.bin(x)
x <- CNV.detail(x)
x <- CNV.segment(x)
bin.ratio <- x@bin$ratio - x@bin$shift # Adjust for shift, different per sample
detail.ratio <- x@detail$ratio - x@bin$shift
bin.ratio[bin.ratio < ylim[1]] <- ylim[1] # Clip values
bin.ratio[bin.ratio > ylim[2]] <- ylim[2]
if (sample==ids[1]){
if (sample==high_sample_ids[1]){
message("Starting")
if (is.null(main))
main <- x@name
if (chr[1] == "all") {
chr <- x@anno@genome$chr
} else {
chr <- intersect(chr, x@anno@genome$chr)
}
plot(NA, xlim = c(0, sum(as.numeric(x@anno@genome[chr, "size"])) -
0), ylim = ylim, xaxs = "i", xaxt = "n", yaxt = "n", xlab = NA,
ylab = NA, main = main)
chr.cumsum0 <- .cumsum0(x@anno@genome[chr, "size"], n = chr)
if (!chrX & is.element("chrX", names(chr.cumsum0)))
chr.cumsum0["chrX"] <- NA
if (!chrY & is.element("chrY", names(chr.cumsum0)))
chr.cumsum0["chrY"] <- NA
abline(v = .cumsum0(x@anno@genome[chr, "size"], right = TRUE),
col = "grey")
if (centromere) {
abline(v = .cumsum0(x@anno@genome[chr, "size"]) + x@anno@genome[chr,
"pq"], col = "grey", lty = 2)
}
axis(1, at = .cumsum0(x@anno@genome[chr, "size"]) + x@anno@genome[chr,
"size"]/2, labels = x@anno@genome[chr, "chr"], las = 2)
if (all(ylim == c(-1.25, 1.25))) {
axis(2, at = round(seq(-1.2, 1.2, 0.2), 1), las = 2)
} else {
axis(2, las = 2)
}
}
gains <- bin.ratio
gains[gains>=0.4] = 1
gains[gains<0.4] = 0
gains_regions <- detail.ratio
gains_regions[gains_regions>=0.4] = 1
gains_regions[gains_regions<0.4] = 0
}
else {
# gains
gains_tmp <- bin.ratio
gains_tmp[gains_tmp>=0.4] = 1
gains_tmp[gains_tmp<0.4] = 0
gains_regions_tmp <- detail.ratio
gains_regions_tmp[gains_regions_tmp>=0.4] = 1
gains_regions_tmp[gains_regions_tmp<0.4] = 0
gains <- gains + gains_tmp
gains_regions <- gains_regions + gains_regions_tmp
}
}
# chr
h = chr.cumsum0[as.vector(seqnames(x@anno@bins))] + values(x@anno@bins)$midpoint
names(h) <- names(gains)
pq_pos = .cumsum0(x@anno@genome[chr, "size"]) + x@anno@genome[chr,
"pq"]
chr_pos <- .cumsum0(x@anno@genome[x@anno@genome$chr, "size"])
pos = c(rbind(chr_pos, pq_pos))
gains_new <- get_gains(gains, pos, ids, h)
lines(chr.cumsum0[as.vector(seqnames(x@anno@bins))] + values(x@anno@bins)$midpoint,
gains_new, type = "h", pch = 16, cex = 0.4, col=colors[id_n])
# gene regions
gains_regions_new <- get_gains_regions(gains_regions, ids)
# detail.ratio.above <- (gains_regions_new > 0 & gains_regions_new < 0.85) |
# gains_regions_new < -0.85
lines(start(x@anno@detail) + (end(x@anno@detail) - start(x@anno@detail)) /2
+ chr.cumsum0[as.vector(seqnames(x@anno@detail))],
gains_regions_new, type = "p", pch = 21, col="black", bg=colors[id_n])
#bg=alpha(colors[id_n], 0.5))
if (id_n==1){ # Label only once
labels <- values(x@anno@detail)$name
labels <- gsub("\\/", "-", labels)
print(labels)
text(start(x@anno@detail) + (end(x@anno@detail) - start(x@anno@detail)) /2
+ chr.cumsum0[as.vector(seqnames(x@anno@detail))], gains_regions_new, # ifelse(detail.ratio.above, detail.ratio, NA),
labels = labels, adj = c(0,
0.5), srt = 90, col = "darkblue")
# text(start(x@anno@detail) + (end(x@anno@detail) - start(x@anno@detail)) /2
# + chr.cumsum0[as.vector(seqnames(x@anno@detail))], gains_regions_new, # ifelse(detail.ratio.above, NA, detail.ratio),
# labels = labels, adj = c(1, 0.5), srt = 90, col = "darkblue")
}
id_n <- id_n + 1
}
########### Losses
id_n = 1
for (ids in list(high_sample_ids, low_sample_ids, cell_culture, h_cortex, neun)){
for (sample in ids){
message(paste("processing sample", sample))
x <- CNV.fit(minfi.data[sample], minfi.data[ctrls], anno)
x <- CNV.bin(x)
x <- CNV.detail(x)
x <- CNV.segment(x)
bin.ratio <- x@bin$ratio - x@bin$shift # Adjust for shift, different per sample
detail.ratio <- x@detail$ratio - x@bin$shift
bin.ratio[bin.ratio < ylim[1]] <- ylim[1] # Clip values
bin.ratio[bin.ratio > ylim[2]] <- ylim[2]
if (sample==ids[1]){
gains <- bin.ratio
gains[gains>-0.4] = 0
gains[gains<=-0.4] = 1
gains_regions <- detail.ratio
gains_regions[gains_regions>-0.4] = 0
gains_regions[gains_regions<=-0.4] = 1
}
else {
# gains
gains_tmp <- bin.ratio
gains_tmp[gains_tmp>-0.4] = 0
gains_tmp[gains_tmp<=-0.4] = 1
gains_regions_tmp <- detail.ratio
gains_regions_tmp[gains_regions_tmp>-0.4] = 0
gains_regions_tmp[gains_regions_tmp<=-0.4] = 1
gains <- gains + gains_tmp
gains_regions <- gains_regions + gains_regions_tmp
}
}
# chr
h = chr.cumsum0[as.vector(seqnames(x@anno@bins))] + values(x@anno@bins)$midpoint
names(h) <- names(gains)
pq_pos = .cumsum0(x@anno@genome[chr, "size"]) + x@anno@genome[chr,
"pq"]
chr_pos <- .cumsum0(x@anno@genome[x@anno@genome$chr, "size"])
pos = c(rbind(chr_pos, pq_pos))
gains_new <- get_gains(gains, pos, ids, h)
lines(chr.cumsum0[as.vector(seqnames(x@anno@bins))] + values(x@anno@bins)$midpoint,
-gains_new, type = "h", pch = 16, cex = 0.4, col=colors[id_n])
# gene regions
gains_regions_new <- get_gains_regions(gains_regions, ids)
# detail.ratio.above <- (gains_regions_new > 0 & gains_regions_new < 0.85) |
# gains_regions_new < -0.85
lines(start(x@anno@detail) + (end(x@anno@detail) - start(x@anno@detail)) /2
+ chr.cumsum0[as.vector(seqnames(x@anno@detail))],
-gains_regions_new, type = "p", pch = 21, col="black", bg=colors[id_n])
# bg=alpha(colors[id_n], 0.5))
id_n <- id_n + 1
}
legend("topright", inset=c(0), bg="transparent", legend=c("Low neuronal",
"High neuronal",
"cell_culture",
"healthy cortex",
"NeuN+"),
fill=colors, title="Group")
dev.off()
}