| author | date | generator | title |
|---|---|---|---|
Haowei Du, [email protected] |
2023-04-11 |
pandoc |
HMZDupFinder example run |
Haowei Du, [email protected]
# require devtools
install.packages("devtools")
devtools::install_github("cluhaowie/HMZDupFinder")
docker run -it --rm -v /local/output/:/output tgambin/hmzdupfinder-image:v1 /bin/bash# Prepare the normalized read depth
``` {.r}
library(HMZDupFinder)
library(IRanges)
library(WES.1KG.WUGSC)
library(BSgenome.Hsapiens.UCSC.hg19)
library(dplyr)
library(data.table)
ref.genome <- BSgenome.Hsapiens.UCSC.hg19
dirPath1 <- system.file("extdata", package = "HMZDupFinder")
dirPath2 <- system.file("extdata", package = "WES.1KG.WUGSC")
outputDir <- "./output/"
bamFiles <- list.files(dirPath2, pattern = '*.bam$',full.names = T)
sampname <- as.character(unlist(read.table(file.path(dirPath2, "sampname"))))
bedFile <- file.path(dirPath1, "chr22_400_to_500.bed")
## prepare bed file
bedOrdered <- prepareBed(bedFile,ref.genome = ref.genome)
## calculate TPM
calcTPMsFromBAMs(bedOrdered,bamFiles,sampname,outputDir,mc.cores = 1)
## generate TPM data frame
tpmFile <- list.files(outputDir,pattern = "*tpm.bed$",full.names = T)
tpmDtOrdered <- prepareTPMData(tpmFile,mc.cores = 2)
## compare the TPM profile to identify reference st
perMat <- cor(tpmDtOrdered,method = "pearson")
## write out the Z-TPM and log2 ratio
CalcuZtpm(perMat,tpmDtOrdered,bedOrdered,outputDir,mc.cores = 4)## call homozygous duplication with fine-tune cut-off
hmzdup.seg.mean <- c(0.85,1.15)
ztpm.mean.cutoff <- 1.5 ## with the optimized ztpm cutoff(>4.0), there are no call in the toy data set, here set 1.5 to get the low confidence call
## get all hmz dup call
ztpmFile <- list.files(outputDir,pattern = "*ztpm.ratio.bed",full.names = T)
tmplist <- lapply(sampname,function(i){
file <- paste0(outputDir,i,".ztpm.ratio.bed")
print(i)
df <- fread(file,stringsAsFactors = F)
seg <- tryCatch({
SegNormRDES(df,i,seg.method="slm")
},error=function(e){
return(i)
})
if(!is.data.frame(seg)){
return(seg)
}else{
hmzdup <- seg%>%
mutate(cnv=ifelse(dplyr::between(seg.mean,hmzdup.seg.mean[1], hmzdup.seg.mean[2])&ztpm.mean>ztpm.mean.cutoff,"hmzdup","else"))%>%
filter(cnv=="hmzdup")
return(hmzdup)
}
})
## [1] "NA06994"
## [1] "segment with SLM"
## [1] "NA10847"
## [1] "segment with SLM"
## [1] "NA11840"
## [1] "segment with SLM"
## [1] "NA12249"
## [1] "segment with SLM"
## [1] "NA12716"
## [1] "segment with SLM"
## [1] "NA12750"
## [1] "segment with SLM"
## [1] "NA12751"
## [1] "segment with SLM"
## [1] "NA12760"
## [1] "segment with SLM"
## [1] "NA12761"
## [1] "segment with SLM"
## [1] "NA12763"
## [1] "segment with SLM"
## [1] "NA18966"
## [1] "segment with SLM"
## [1] "NA18967"
## [1] "segment with SLM"
## [1] "NA18968"
## [1] "segment with SLM"
## [1] "NA18969"
## [1] "segment with SLM"
## [1] "NA18970"
## [1] "segment with SLM"
## [1] "NA18971"
## [1] "segment with SLM"
## [1] "NA18972"
## [1] "segment with SLM"
## [1] "NA18973"
## [1] "segment with SLM"
## [1] "NA18974"
## [1] "segment with SLM"
## [1] "NA18975"
## [1] "segment with SLM"
## [1] "NA18976"
## [1] "segment with SLM"
## [1] "NA18981"
## [1] "segment with SLM"
## [1] "NA18987"
## [1] "segment with SLM"
## [1] "NA18990"
## [1] "segment with SLM"
## [1] "NA18991"
## [1] "segment with SLM"
## [1] "NA19098"
## [1] "segment with SLM"
## [1] "NA19119"
## [1] "segment with SLM"
## [1] "NA19131"
## [1] "segment with SLM"
## [1] "NA19137"
## [1] "segment with SLM"
## [1] "NA19138"
## [1] "segment with SLM"
## [1] "NA19141"
## [1] "segment with SLM"
## [1] "NA19143"
## [1] "segment with SLM"
## [1] "NA19144"
## [1] "segment with SLM"
## [1] "NA19152"
## [1] "segment with SLM"
## [1] "NA19153"
## [1] "segment with SLM"
## [1] "NA19159"
## [1] "segment with SLM"
## [1] "NA19160"
## [1] "segment with SLM"
## [1] "NA19171"
## [1] "segment with SLM"
## [1] "NA19200"
## [1] "segment with SLM"
## [1] "NA19201"
## [1] "segment with SLM"
## [1] "NA19204"
## [1] "segment with SLM"
## [1] "NA19206"
## [1] "segment with SLM"
## [1] "NA19207"
## [1] "segment with SLM"
## [1] "NA19209"
## [1] "segment with SLM"
## [1] "NA19210"
## [1] "segment with SLM"
## [1] "NA19223"
## [1] "segment with SLM"
dupcall <- rbindlist(tmplist)
dupcall
## ID chrom loc.start loc.end num.mark seg.mean ztpm.mean cnv
## 1: NA10847 chr22 21797070 21799161 1 1.0369728 2.383095 hmzdup
## 2: NA12249 chr22 21921983 21947100 1 0.9437214 1.598907 hmzdup
## 3: NA18969 chr22 21356071 21369444 1 1.0549529 1.890923 hmzdup
## 4: NA18971 chr22 21987420 21988261 1 1.0836136 1.781615 hmzdup
## 5: NA18974 chr22 21987420 21988261 1 0.9754562 1.537753 hmzdup
## 6: NA19119 chr22 21356071 21369444 1 0.9044307 1.529398 hmzdup
## 7: NA19144 chr22 21797070 21799161 1 0.9033155 1.971388 hmzdup
library(ggplot2)
library(dplyr)
library(stringr)
scale_rd <- scale_y_continuous(name="Log2 Ratio",
limits=c(-2.5, 2),
breaks = c(-2,
round(log2(1/2),2),
round(log2(2/2),2),
round(log2(3/2),2),
round(log2(4/2),2),
round(log2(5/2),2),
round(log2(6/2),2)
))
wg_ratio2plot <- function(df,id){
temp <- df %>%
group_by(seqnames) %>%
summarise(max_end = max(end)) %>%
mutate(across("seqnames", str_replace, "chr", "")) %>%
arrange(as.numeric(seqnames)) %>%
mutate(loc_add = lag(cumsum(as.numeric(max_end)), default = 0)) %>%
mutate(seqnames = paste0("chr", seqnames))%>%
mutate(loc_end=loc_add+max_end)
para.label <- paste0(id)
df <- df%>%mutate(log2ratio=log2(ratio+0.001))%>%
mutate(log2ratio=ifelse(log2ratio < -2.5,-2,log2ratio))
df <- df %>%
inner_join(temp, by = "seqnames") %>%
mutate(end_cum = loc_add + end)
df <- df %>%
mutate(start_cum = loc_add+start)
axis_set <- df %>%
group_by(seqnames) %>%
summarize(center = mean(end_cum)) %>%
arrange((seqnames))
label_seg_gain <- df %>%
filter(dplyr::between(log2ratio,log2(3/2)-0.2,log2(3/2)+0.5))
label_seg_loss <- df %>%
filter(dplyr::between(log2ratio,log2(1/2)-0.5,log2(1/2)+0.5))
wg <-
ggplot()+
geom_point(data=df,aes(x = start_cum, y = log2ratio))+
geom_point(data = label_seg_gain,aes(x = start_cum, y = log2ratio), color = "red")+
geom_point(data = label_seg_loss,aes(x = start_cum, y = log2ratio), color = "green")+
#geom_rect(data = temp[seq(1,24,2),],aes(xmin=loc_add,xmax=loc_end,ymin=-2,ymax=1.58),alpha = 0.3)+
#geom_point(data = label_seg_gain, shape = 8, color = "red")+
#geom_point(data = label_seg_gain.sig, shape = 8, color = "blue")+
#geom_point(data = label_seg_loss, shape= 8, color = "green")+
theme_minimal() +
theme(
legend.position = "none",
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
axis.text.x = element_text(angle = 60, size = 10, vjust = 0.8)
)+
scale_rd+
#scale_size_continuous(range = c(0.5,3))+
labs(x = NULL)+
scale_colour_manual(values = c("orange", "purple"))+
scale_x_continuous(label = axis_set$seqnames, breaks = axis_set$center)+
coord_cartesian(expand = F)+
ggtitle(para.label)
return(wg)
}
df <- fread("output/NA10847.ztpm.ratio.bed",stringsAsFactors = F)
plt <- wg_ratio2plot(df,"NA10847")
ggsave("./output/NA10847.pdf",plt, width = 6,height = 4)
