rm(list = ls())
library(dplyr)
library(ccube)
library(doParallel)
library(ggplot2)
library(tidyr)
library(gridExtra)
registerDoParallel(cores=3)
set.seed(12345)
numSv <- 500
numOfClusterPool = 1:7
numOfRepeat = 1
baseDepth = 50
ccfCN <- c(0.7, 0.3)
ccfSet <- c(1, 0.3, 0.7) # true ccf pool
ccfTrue <- sample(ccfSet, numSv, c(0.5,0.2,0.3), replace = T)
purity <- 0.8
cnPoolMaj <- c(1,2,3,4)
cnPoolMin <- c(0,1,2)
cnPoolMajFractions <- c(0.25, 0.25, 0.25,0.25)
cnPoolMinFractions <- c(1/3, 1/3, 1/3)
subclonal_cn <- sample(c(T, F), numSv, c(1,5), replace = T)
cnProfile = GenerateSubClonalCNProfile(cnPoolMaj, cnPoolMin,
cnPoolMajFractions, cnPoolMinFractions,
numSv, subclonal_cn, ccfCN)
mydata <- data.frame(mutation_id = paste0("ss", seq_len(numSv)) ,
ccf_true = ccfTrue,
minor_cn_sub1 = cnProfile[,1],
major_cn_sub1 = cnProfile[,2],
total_cn_sub1 = cnProfile[,3],
frac_cn_sub1 = cnProfile[,4],
minor_cn_sub2 = cnProfile[,5],
major_cn_sub2 = cnProfile[,6],
total_cn_sub2 = cnProfile[,7],
frac_cn_sub2 = cnProfile[,8],
stringsAsFactors = F)
mydata$purity <- purity
mydata$normal_cn <- 2
mydata$subclonal_cn <- subclonal_cn
mydata <- mutate(rowwise(mydata),
true_mult_sub1 = sample(c(1,if (major_cn_sub1 ==1) { 1 } else {major_cn_sub1}), 1),
true_mult_sub2 = if ( major_cn_sub2 == -100 ) {
-100
} else {
sample(c(1,if (major_cn_sub2 ==1) { 1 } else {major_cn_sub2}), 1)
},
true_mult = frac_cn_sub1 * true_mult_sub1 + frac_cn_sub2 * true_mult_sub2,
total_cn = frac_cn_sub1 * total_cn_sub1 + frac_cn_sub2 * total_cn_sub2,
true_vaf = cp2ap(ccf_true, purity, normal_cn,
total_cn,
total_cn,
true_mult),
total_counts = rpois(1, total_cn/2 * baseDepth),
var_counts = rbinom(1, total_counts, true_vaf),
ref_counts = total_counts - var_counts)
ccubeRes <- RunCcubePipeline(ssm = mydata, numOfClusterPool = numOfClusterPool, numOfRepeat = numOfRepeat,
runAnalysisSnap = T, runQC = T, maxiter = 100)
fn1 = "~/Desktop/snv_subclonal_0.3_0.7_snap.pdf"
MakeCcubeStdPlot(res = ccubeRes$res, ssm = ccubeRes$ssm, printPlot = T, fn = fn1)
mydataCon <- mutate(rowwise(mydata),
major_cn_sub1 = frac_cn_sub1 * major_cn_sub1 + frac_cn_sub2 * major_cn_sub2,
minor_cn_sub1 = frac_cn_sub1 * minor_cn_sub1 + frac_cn_sub2 * minor_cn_sub2,
major_cn_sub2 = -100,
minor_cn_sub2 = -100,
frac_cn_sub1 = 1,
frac_cn_sub2 = 1-frac_cn_sub1,
total_cn_sub1 = major_cn_sub1 + minor_cn_sub1,
total_cn_sub2 = -100,
total_cn = frac_cn_sub1*total_cn_sub1 + frac_cn_sub2 * total_cn_sub2,
subclonal_cn = frac_cn_sub1 < 1)
ccubeRes1 <- RunCcubePipeline(ssm = mydataCon, numOfClusterPool = numOfClusterPool, numOfRepeat = numOfRepeat,
runAnalysisSnap = T, runQC = T, maxiter = 100)
fn1 = "~/Desktop/snv_subclonal_0.3_0.7_consensus_snap.pdf"
MakeCcubeStdPlot(res = ccubeRes1$res, ssm = ccubeRes1$ssm, printPlot = T, fn = fn1)
mydata = ccubeRes$ssm
mydataCon = ccubeRes1$ssm
label = ccubeRes$res$label
myColors=gg_color_hue(10)
fn = "~/Desktop/consensus_ccf_comparsions_subclonal_30_70.pdf"
pdf(fn, width=8, height=4)
par(mfrow=c(1,2))
plot(mydata$ccube_ccf,
mydataCon$ccube_ccf,
col = myColors[label],
xlim = c(0, max( c(mydataCon$ccube_ccf,
mydata$ccube_ccf) ) ),
ylim = c(0, max( c(mydataCon$ccube_ccf,
mydata$ccube_ccf) ) ),
xlab = "consensus CN ccf", ylab = "battenberg CN ccf", main = "SNV model")
points( seq(0, max( c(mydataCon$ccube_ccf,
mydata$ccube_ccf) ), length.out = 100 ),
seq(0, max( c(mydataCon$ccube_ccf,
mydata$ccube_ccf) ), length.out = 100 ),
type = "l" )
mydata$ccube_cluster_ccf_mult = mydata$ccube_ccf_mean*mydata$ccube_mult
mydataCon$ccube_cluster_ccf_mult = mydataCon$ccube_ccf_mean*mydataCon$ccube_mult
plot(mydataCon$ccube_cluster_ccf_mult,
mydata$ccube_cluster_ccf_mult,
col = myColors[label],
xlim = c(0, max( c(mydataCon$ccube_cluster_ccf_mult,
mydata$ccube_cluster_ccf_mult) ) ),
ylim = c(0, max( c(mydataCon$ccube_cluster_ccf_mult,
mydata$ccube_cluster_ccf_mult) ) ),
xlab = "consensus CN: cluster mean * \n multiplicity", ylab = "battenberg CN: cluster mean * multiplicity",
main = "SNV model")
points( seq(0, max( c(mydataCon$ccube_cluster_ccf_mult,
mydata$ccube_cluster_ccf_mult) ), length.out = 100 ),
seq(0, max( c(mydataCon$ccube_cluster_ccf_mult,
mydata$ccube_cluster_ccf_mult) ), length.out = 100 ),
type = "l" )
dev.off()
# mydata$error_mult = mydata$ccube_mult - mydata$true_mult
# selectedData <- mydata[, c("mutation_id","ccube_ccf_mean", "true_mult", "total_cn", "error_mult")]
# fn = "~/Desktop/snv_mults.pdf"
# pdf(fn, width=8, height=4)
#
# selectedData1 = gather(selectedData, key, value, -mutation_id, -total_cn, -true_mult, -ccube_ccf_mean)
# tt1 = filter( selectedData1, key %in% c("error_mult") )
# g1 = ggplot(tt1, aes(y = value, x = as.factor(true_mult), fill = as.factor(ccube_ccf_mean))) + geom_boxplot() +
# xlab("true_mult") + ylab("error") + theme(legend.position="none")
# print(g1)
# dev.off()
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