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FigS_mutationLoad.Rmd
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FigS_mutationLoad.Rmd
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---
title: "Deleterious mutation accumulation"
author: "Aoyue Bi, Daxing Xu"
date: "`r Sys.Date()`"
output:
html_document: # html_notebook, html_document
toc: true
toc_float:
toc_collapsed: true
toc_depth: 3
number_sections: true
theme: simplex # default, united, cerulean, journal, flatly, darkly, readable, spacelab, united, cosmo, lumen, paper, sandstone, simplex, and yeti
highlight: haddock # default, tango, pygments, kate, monochrome, espresso, zenburn, haddock, and breezedark
# bibliography: bibliography.json
---
# Fig3S
```{r setup, echo =T, message = T, warning = F}
library(tidyverse)
# library(RColorBrewer)
# library(ggpubr)
# library(egg)
## eval 代码要不要运行
## echo 代码要不要输出
## include 图要不要输出
## warning 警告要不要输出
## message 默认Bin等信息要不要输出
```
#### ===1=== Dataset one 1
## dftotal
```{r}
dftotal <- read_tsv("data/005_SNPAnnoDB/geneSNPAnno.txt.gz") %>%
mutate(Sub=str_sub(Transcript, 9, 9))
# filter(!is.na(Ancestral),Ancestral==Ref|Ancestral==Alt) %>%
# mutate(IfRefisAnc = if_else(Ref == Ancestral,"Anc","Der")) %>%
# mutate(Sub=str_sub(Transcript, 9, 9)) %>%
```
## VMap2.1 - 1:1:1 Triads
```{r}
triadsGeneSet <- read_tsv("data/021_homoeologsGene111/triadGenes1.1_V3.txt") %>%
filter(synteny=="syntenic") %>%
select(TriadID,A,B,D) %>%
pivot_longer(cols = c(A,B,D),names_to = "Sub",values_to = "Gene") %>%
pull(Gene)
## dftotal_triads 数据库
dftotal_triads <- dftotal %>%
mutate(Gene=str_split_fixed(Transcript,"\\.",2)[,1]) %>%
filter(Gene %in% triadsGeneSet) %>%
select(-Sub,-Gene)
# write_tsv(dftotal_triads,"/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/004_annoDB/006_geneSNPAnnotation_merge/000_triads/001_geneSNPAnno_triads.txt.gz")
```
## Hexaploid/Pseudohexaploid - whole-genome/1:1:1 Triads annotation
```{r eval=FALSE, include=FALSE}
### 将上述结果重新读入，加到数据库
dfAAF <- read_tsv("data/015_AAF/002_aaf_byPloidy/002_AAF_byPloidy.txt.gz") %>%
mutate(ID=str_c(Chr,"-",Pos))
### ************************* hexaploid ***********************
df06 <- dfAAF %>%
filter(AAF_AABBDD>0,AAF_AABBDD<1) %>%
pull(ID)
### dftotal_hexaploid 数据库
dftotal_hexaploid <- dftotal %>%
filter(ID %in% df06)
# write_tsv(dftotal_hexaploid,"data/005_SNPAnnoDB/000_hexaploid_pseudohexaploid/001_geneSNPAnno_hexaploid.txt.gz")
### dftotal_hexaploid_triads 数据库
triadsGeneSet <- read_tsv("data/021_homoeologsGene111/triadGenes1.1_V3.txt") %>%
filter(synteny=="syntenic") %>%
select(TriadID,A,B,D) %>%
pivot_longer(cols = c(A,B,D),names_to = "Sub",values_to = "Gene") %>%
pull(Gene)
dftotal_hexaploid_triads <- dftotal %>%
mutate(Gene=str_split_fixed(Transcript,"\\.",2)[,1]) %>%
filter(Gene %in% triadsGeneSet) %>%
filter(ID %in% df06) %>%
select(-Gene)
# write_tsv(dftotal_hexaploid_triads,"/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/004_annoDB/006_geneSNPAnnotation_merge/000_triads/002_geneSNPAnno_hexaploid_triads.txt.gz")
### ************************* pseudohexaploid ***********************
df42 <- dfAAF %>%
filter(AAF_AABB_DD>0,AAF_AABB_DD<1) %>%
pull(ID)
### dftotal_hexaploid 数据库
dftotal_pseudohexaploid <- dftotal %>%
filter(ID %in% df42)
# write_tsv(dftotal_pseudohexaploid,"/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/004_annoDB/006_geneSNPAnnotation_merge/000_hexaploid_pseudohexaploid/002_geneSNPAnno_pseudohexaploid.txt.gz")
### dftotal_hexaploid_triads 数据库
triadsGeneSet <- read_tsv("data/021_homoeologsGene111/triadGenes1.1_V3.txt") %>%
filter(synteny=="syntenic") %>%
select(TriadID,A,B,D) %>%
pivot_longer(cols = c(A,B,D),names_to = "Sub",values_to = "Gene") %>%
pull(Gene)
dftotal_pseudohexaploid_triads <- dftotal %>%
mutate(Gene=str_split_fixed(Transcript,"\\.",2)[,1]) %>%
filter(Gene %in% triadsGeneSet) %>%
filter(ID %in% df42) %>%
select(-Gene)
# write_tsv(dftotal_pseudohexaploid_triads,"/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/004_annoDB/006_geneSNPAnnotation_merge/000_triads/003_geneSNPAnno_pseudohexaploid_triads.txt.gz")
```
#### ===2=== Dataset two 2
## del Count and del Ratio
### java Calculate the deleterious load
```{r}
### DeleteriousXPCLRS1000 java类
```
### data operate1 - bySub summary
- 数据初处理，主要做2个工作：
①过滤Sub:过滤四倍体中的D亚基因组数据和二倍体中的AB亚基因组数据；
②相加Sub:将染色体1-42以亚基因组为单位进行合并相加，得到每个taxa每个亚基因组的del count。
输入文件夹和输出文件夹的名字保持不变
```{r eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}
# ### 导入种质数据库,并选取特定的列
dftaxaDB <- read.delim("data/001_TaxaDB/WheatVMap2_GermplasmInfo.txt") %>% select(Taxa=VcfID,GenomeType)
#
### 导入染色体-亚基因组数据库，并选取特定的列
dfchrDB <- read.delim("data/001_TaxaDB/chrConvertionRule_byAoyue.txt") %>% select(Chr = ChrID,Subgenome)
sumDerivedCountPerTaxon <- function(file){
df <- read_tsv(file,show_col_types = F) %>%
left_join(.,dftaxaDB,by="Taxa") %>%
left_join(.,dfchrDB,by="Chr") %>%
# filter(Chr==3) %>%
mutate(MergedGenomeTypeSub = paste(GenomeType,"_",Subgenome,sep = "")) %>%
filter(!MergedGenomeTypeSub %in% c("AABB_D","DD_A","DD_B") ) %>%
select(-MergedGenomeTypeSub) %>%
###************ group_by ***********需要根据实际修改!!!!!!!
group_by(Taxa,Subgenome,VariantsGroup,IfSelected,Group_refobj) %>%
# group_by(Taxa,Subgenome,VariantsGroup) %>%
##求每个个体，每个亚基因组，每种类型的count之和
summarise(TotalDerivedSNPCount = sum(TotalDerivedSNPCount),
HomoDerivedSNPCount = sum(HomoDerivedSNPCount),
HeterDerivedSNPCount = sum(HeterDerivedSNPCount),
SiteCountWithMinDepth = sum(SiteCountWithMinDepth))
df
}
# inputDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/005_allIndivi")
# df <- dir(inputDir, full.names = T, pattern = "") %>%
# map(sumDerivedCountPerTaxon) %>%
# bind_rows()
# write_tsv(df,path = "~/Documents/delCount.txt.gz")
### 一
inputDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/006_indivi_triads")
outDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/005_merge")
df <- dir(inputDir, full.names = T, pattern = "") %>%
map(sumDerivedCountPerTaxon) %>%
bind_rows()
# write_tsv(df,file.path(outDir,"delCount_triads.txt.gz"))
### 二
inputDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/007_indivi_hexaploid")
outDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/005_merge")
df <- dir(inputDir, full.names = T, pattern = "") %>%
map(sumDerivedCountPerTaxon) %>%
bind_rows()
# write_tsv(df,file.path(outDir,"delCount_hexaploid.txt.gz"))
### 三
inputDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/008_indivi_hexaploid_triads")
outDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/005_merge")
df <- dir(inputDir, full.names = T, pattern = "") %>%
map(sumDerivedCountPerTaxon) %>%
bind_rows()
# write_tsv(df,file.path(outDir,"delCount_hexaploid_triads.txt.gz"))
### 四
inputDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/009_indivi_pseudohexaploid")
outDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/005_merge")
df <- dir(inputDir, full.names = T, pattern = "") %>%
map(sumDerivedCountPerTaxon) %>%
bind_rows()
# write_tsv(df,file.path(outDir,"delCount_pseudohexaploid.txt.gz"))
### 五
inputDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/010_indivi_pseudohexaploid_triads")
outDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/005_merge")
df <- dir(inputDir, full.names = T, pattern = "") %>%
map(sumDerivedCountPerTaxon) %>%
bind_rows()
# write_tsv(df,file.path(outDir,"delCount_pseudohexaploid_triads.txt.gz"))
### 六
inputDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/005_allIndivi")
outDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/005_merge")
df <- dir(inputDir, full.names = T, pattern = "") %>%
map(sumDerivedCountPerTaxon) %>%
bind_rows()
# write_tsv(df,file.path(outDir,"delCount.txt.gz"))
```
### data operate2 - 长表宽表转换，并标准化数据
- 合并数据，成为一个数据框，进行操作
levels(factor(df$VariantsGroup))
[1] "001_synonymous" "002_nonsynonymous" "003_nonsynGERPandDerivedSIFT" "004_nonsynDerivedSIFT"
[5] "005_GERP" "006_SIFT_StopGain" "007_VEP" "008_snpEff"
[9] "009_VEP_stopGained" "010_GERP16way_1" "011_GERP16way_1.2_max" "012_GERP16way_1.2_2.5"
[13] "013_GERP16way_2.5_max" "014_GERP16way_2.14_max" "015_LIST_S2" "016_PhyloP1.5_RefNotMask"
[17] "017_PhyloP1.5_RefMask" "018_GERP16way_2.14andSIFT" "019_Alt_PolyPhen2" "020_Derived_PolyPhen2"
[21] "021_GERP16way_2.14andPolyPhen2" "022_GERP16way_1.78" "023_GERP16way_1.78andPolyPhen2" "024_GERP16way_1andPolyPhen2"
[25] "025_GERP16way_3.1" "026_Derived_PolyPhen2_probably"
[27] "027_GERP16way_1andDerived_PolyPhen2_probably" "028_GERP16way_1.78andDerived_PolyPhen2_probably"
[29] "029_GERP16way_2.14andDerived_PolyPhen2_probably" "030_GERP16way_3.1andDerived_PolyPhen2_probably"
"031_GERP16way_1.5andDerived_PolyPhen2_0.5" "032_GERP16way_1.5"
[33] "033_Derived_PolyPhen2_0.5"
>
```{r}
### 跑一次，将数据存储起来，不用再跑。
### wide table -> long table 将 DelCountGroup (total, homo, heter)变成长表,后续可以filter;
### long table -> wide table 将 VariantsGroup 变成宽表，为标准化求ratio做准备;
### wide table -> long table 将 Stand_LoadGroup 变成长表， 为画图做准备。
fun_getDelRatio <- function(filenamefun){
dfdelRatio <- read_tsv(filenamefun,show_col_types = F) %>%
### wide -> long
pivot_longer(.,cols =c("TotalDerivedSNPCount","HomoDerivedSNPCount","HeterDerivedSNPCount","SiteCountWithMinDepth"),names_to ="DelCountGroup" ,values_to = "DelCount") %>%
### long -> wide
pivot_wider(.,names_from = "VariantsGroup",values_from = "DelCount") %>%
mutate(Ratio_002_nonsynonymous = `002_nonsynonymous`/`001_synonymous`,
# Ratio_003_nonsynGERPandDerivedSIFT = `003_nonsynGERPandDerivedSIFT`/`001_synonymous`,
Ratio_004_nonsynDerivedSIFT = `004_nonsynDerivedSIFT`/`001_synonymous`,
# Ratio_005_GERP = `005_GERP`/`001_synonymous`,
# Ratio_006_SIFT_StopGain = `006_SIFT_StopGain`/`001_synonymous`,
Ratio_007_VEP = `007_VEP`/`001_synonymous`,
Ratio_008_snpEff = `008_snpEff`/`001_synonymous`,
# Ratio_009_VEP_stopGained = `009_VEP_stopGained`/`001_synonymous`,
Ratio_010_GERP16way_1 = `010_GERP16way_1`/`001_synonymous`,
# Ratio_011_GERP16way_1.2_max = `011_GERP16way_1.2_max`/`001_synonymous`,
# Ratio_012_GERP16way_1.2_2.5 = `012_GERP16way_1.2_2.5`/`001_synonymous`,
# Ratio_013_GERP16way_2.5_max=
# `013_GERP16way_2.5_max`/`001_synonymous`,
Ratio_014_GERP16way_2.14_max = `014_GERP16way_2.14_max`/`001_synonymous`,
Ratio_015_LIST_S2=`015_LIST_S2`/`001_synonymous`,
# Ratio_016_PhyloP1.5_RefNotMask=`016_PhyloP1.5_RefNotMask`/`001_synonymous`,
Ratio_017_PhyloP1.5_RefMask=`017_PhyloP1.5_RefMask`/`001_synonymous`,
# Ratio_018_GERP16way_2.14andSIFT=`018_GERP16way_2.14andSIFT`/`001_synonymous`,
# Ratio_019_Alt_PolyPhen2 = `019_Alt_PolyPhen2`/`001_synonymous`,
# Ratio_020_Derived_PolyPhen2=`020_Derived_PolyPhen2`/`001_synonymous`,
# Ratio_021_GERP16way_2.14andPolyPhen2=`021_GERP16way_2.14andPolyPhen2`/`001_synonymous`,
# Ratio_022_GERP16way_1.78=`022_GERP16way_1.78`/`001_synonymous`,
# Ratio_023_GERP16way_1.78andPolyPhen2=`023_GERP16way_1.78andPolyPhen2`/`001_synonymous`,
# Ratio_024_GERP16way_1andPolyPhen2 = `024_GERP16way_1andPolyPhen2`/`001_synonymous`,
Ratio_025_GERP16way_3.1=`025_GERP16way_3.1`/`001_synonymous`,
# Ratio_026_Derived_PolyPhen2_probably = `026_Derived_PolyPhen2_probably`/`001_synonymous`,
# Ratio_027_GERP16way_1andDerived_PolyPhen2_probably=`027_GERP16way_1andDerived_PolyPhen2_probably`/`001_synonymous`,
# Ratio_028_GERP16way_1.78andDerived_PolyPhen2_probably=`028_GERP16way_1.78andDerived_PolyPhen2_probably`/`001_synonymous`,
# Ratio_029_GERP16way_2.14andDerived_PolyPhen2_probably=`029_GERP16way_2.14andDerived_PolyPhen2_probably`/`001_synonymous`,
# Ratio_030_GERP16way_3.1andDerived_PolyPhen2_probably=`030_GERP16way_3.1andDerived_PolyPhen2_probably`/`001_synonymous`,
Ratio_031_GERP16way_1.5andDerived_PolyPhen2_0.5=`031_GERP16way_1.5andDerived_PolyPhen2_0.5`/`001_synonymous`,
Ratio_032_GERP16way_1.5=`032_GERP16way_1.5`/`001_synonymous`,
Ratio_033_Derived_PolyPhen2_0.5=`033_Derived_PolyPhen2_0.5`/`001_synonymous`) %>%
### wide -> long
pivot_longer(.,cols =c("Ratio_002_nonsynonymous":"Ratio_033_Derived_PolyPhen2_0.5"),names_to ="Stand_LoadGroup" ,values_to = "Stand_delCount") %>%
### 去除不必要的列，方便画图和管理数据
select(- ("001_synonymous":"033_Derived_PolyPhen2_0.5")) %>%
write_tsv(file.path("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/005_merge",str_replace_all(basename(filenamefun),"delCount","delRatio")))
return(dfdelRatio)
}
### 函数调用
inputDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/005_merge")
# df <- dir(inputDir,pattern = "delCount",full.names = T) %>%
# map(fun_getDelRatio)
```
#### -------- AB sub D sub
### data operate1 - bySub summary
- 数据初处理，主要做2个工作：
①过滤Sub:过滤四倍体中的D亚基因组数据和二倍体中的AB亚基因组数据；
②相加Sub:将染色体1-42以亚基因组为单位进行合并相加，得到每个taxa每个亚基因组的del count。
输入文件夹和输出文件夹的名字保持不变
```{r eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}
# ### 导入种质数据库,并选取特定的列
dftaxaDB <- read.delim("/Users/Aoyue/project/wheatVMap2_1000/001_germplasm/021_WheatVMap2_GermplasmInfo.txt") %>% select(Taxa=VcfID,GenomeType)
#
### 导入染色体-亚基因组数据库，并选取特定的列
dfchrDB <- read.delim("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/000_chrDB/chrConvertionRule_byAoyue.txt") %>% select(Chr = ChrID,Subgenome)
sumDerivedCountPerTaxon <- function(file){
df <- read_tsv(file,show_col_types = F) %>%
left_join(.,dftaxaDB,by="Taxa") %>%
left_join(.,dfchrDB,by="Chr") %>%
# filter(Chr==3) %>%
mutate(MergedGenomeTypeSub = paste(GenomeType,"_",Subgenome,sep = "")) %>%
filter(!MergedGenomeTypeSub %in% c("AABB_D","DD_A","DD_B") ) %>%
select(-MergedGenomeTypeSub) %>%
mutate(MergedSub=ifelse(Subgenome=="D","D","AB")) %>%
mutate(Subgenome=MergedSub) %>%
select(-MergedSub) %>%
###************ group_by ***********需要根据实际修改!!!!!!!
group_by(Taxa,Subgenome,VariantsGroup,IfSelected,Group_refobj) %>%
# group_by(Taxa,Subgenome,VariantsGroup) %>%
##求每个个体，每个亚基因组，每种类型的count之和
summarise(TotalDerivedSNPCount = sum(TotalDerivedSNPCount),
HomoDerivedSNPCount = sum(HomoDerivedSNPCount),
HeterDerivedSNPCount = sum(HeterDerivedSNPCount),
SiteCountWithMinDepth = sum(SiteCountWithMinDepth))
df
}
# inputDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/005_allIndivi")
# df <- dir(inputDir, full.names = T, pattern = "") %>%
# map(sumDerivedCountPerTaxon) %>%
# bind_rows()
# write_tsv(df,path = "~/Documents/delCount.txt.gz")
### 一
inputDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/006_indivi_triads")
outDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/011_merge_byABsubDsub")
df <- dir(inputDir, full.names = T, pattern = "") %>%
map(sumDerivedCountPerTaxon) %>%
bind_rows()
# write_tsv(df,file.path(outDir,"delCount_triads.txt.gz"))
### 二
inputDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/007_indivi_hexaploid")
outDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/011_merge_byABsubDsub")
df <- dir(inputDir, full.names = T, pattern = "") %>%
map(sumDerivedCountPerTaxon) %>%
bind_rows()
# write_tsv(df,file.path(outDir,"delCount_hexaploid.txt.gz"))
### 三
inputDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/008_indivi_hexaploid_triads")
outDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/011_merge_byABsubDsub")
df <- dir(inputDir, full.names = T, pattern = "") %>%
map(sumDerivedCountPerTaxon) %>%
bind_rows()
# write_tsv(df,file.path(outDir,"delCount_hexaploid_triads.txt.gz"))
### 四
inputDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/009_indivi_pseudohexaploid")
outDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/011_merge_byABsubDsub")
df <- dir(inputDir, full.names = T, pattern = "") %>%
map(sumDerivedCountPerTaxon) %>%
bind_rows()
# write_tsv(df,file.path(outDir,"delCount_pseudohexaploid.txt.gz"))
### 五
inputDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/010_indivi_pseudohexaploid_triads")
outDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/011_merge_byABsubDsub")
df <- dir(inputDir, full.names = T, pattern = "") %>%
map(sumDerivedCountPerTaxon) %>%
bind_rows()
# write_tsv(df,file.path(outDir,"delCount_pseudohexaploid_triads.txt.gz"))
### 六
inputDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/005_allIndivi")
outDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/011_merge_byABsubDsub")
df <- dir(inputDir, full.names = T, pattern = "") %>%
map(sumDerivedCountPerTaxon) %>%
bind_rows()
# write_tsv(df,file.path(outDir,"delCount.txt.gz"))
```
### data operate2 - 长表宽表转换，并标准化数据
- 合并数据，成为一个数据框，进行操作
levels(factor(df$VariantsGroup))
[1] "001_synonymous" "002_nonsynonymous" "003_nonsynGERPandDerivedSIFT" "004_nonsynDerivedSIFT"
[5] "005_GERP" "006_SIFT_StopGain" "007_VEP" "008_snpEff"
[9] "009_VEP_stopGained" "010_GERP16way_1" "011_GERP16way_1.2_max" "012_GERP16way_1.2_2.5"
[13] "013_GERP16way_2.5_max" "014_GERP16way_2.14_max" "015_LIST_S2" "016_PhyloP1.5_RefNotMask"
[17] "017_PhyloP1.5_RefMask" "018_GERP16way_2.14andSIFT" "019_Alt_PolyPhen2" "020_Derived_PolyPhen2"
[21] "021_GERP16way_2.14andPolyPhen2" "022_GERP16way_1.78" "023_GERP16way_1.78andPolyPhen2" "024_GERP16way_1andPolyPhen2"
[25] "025_GERP16way_3.1" "026_Derived_PolyPhen2_probably"
[27] "027_GERP16way_1andDerived_PolyPhen2_probably" "028_GERP16way_1.78andDerived_PolyPhen2_probably"
[29] "029_GERP16way_2.14andDerived_PolyPhen2_probably" "030_GERP16way_3.1andDerived_PolyPhen2_probably"
"031_GERP16way_1.5andDerived_PolyPhen2_0.5" "032_GERP16way_1.5"
[33] "033_Derived_PolyPhen2_0.5"
>
```{r eval=FALSE, include=FALSE}
### 跑一次，将数据存储起来，不用再跑。
### wide table -> long table 将 DelCountGroup (total, homo, heter)变成长表,后续可以filter;
### long table -> wide table 将 VariantsGroup 变成宽表，为标准化求ratio做准备;
### wide table -> long table 将 Stand_LoadGroup 变成长表， 为画图做准备。
fun_getDelRatio <- function(filenamefun){
dfdelRatio <- read_tsv(filenamefun,show_col_types = F) %>%
### wide -> long
pivot_longer(.,cols =c("TotalDerivedSNPCount","HomoDerivedSNPCount","HeterDerivedSNPCount","SiteCountWithMinDepth"),names_to ="DelCountGroup" ,values_to = "DelCount") %>%
### long -> wide
pivot_wider(.,names_from = "VariantsGroup",values_from = "DelCount") %>%
mutate(Ratio_002_nonsynonymous = `002_nonsynonymous`/`001_synonymous`,
# Ratio_003_nonsynGERPandDerivedSIFT = `003_nonsynGERPandDerivedSIFT`/`001_synonymous`,
Ratio_004_nonsynDerivedSIFT = `004_nonsynDerivedSIFT`/`001_synonymous`,
# Ratio_005_GERP = `005_GERP`/`001_synonymous`,
# Ratio_006_SIFT_StopGain = `006_SIFT_StopGain`/`001_synonymous`,
Ratio_007_VEP = `007_VEP`/`001_synonymous`,
Ratio_008_snpEff = `008_snpEff`/`001_synonymous`,
# Ratio_009_VEP_stopGained = `009_VEP_stopGained`/`001_synonymous`,
Ratio_010_GERP16way_1 = `010_GERP16way_1`/`001_synonymous`,
# Ratio_011_GERP16way_1.2_max = `011_GERP16way_1.2_max`/`001_synonymous`,
# Ratio_012_GERP16way_1.2_2.5 = `012_GERP16way_1.2_2.5`/`001_synonymous`,
# Ratio_013_GERP16way_2.5_max=
# `013_GERP16way_2.5_max`/`001_synonymous`,
Ratio_014_GERP16way_2.14_max = `014_GERP16way_2.14_max`/`001_synonymous`,
Ratio_015_LIST_S2=`015_LIST_S2`/`001_synonymous`,
# Ratio_016_PhyloP1.5_RefNotMask=`016_PhyloP1.5_RefNotMask`/`001_synonymous`,
Ratio_017_PhyloP1.5_RefMask=`017_PhyloP1.5_RefMask`/`001_synonymous`,
# Ratio_018_GERP16way_2.14andSIFT=`018_GERP16way_2.14andSIFT`/`001_synonymous`,
# Ratio_019_Alt_PolyPhen2 = `019_Alt_PolyPhen2`/`001_synonymous`,
# Ratio_020_Derived_PolyPhen2=`020_Derived_PolyPhen2`/`001_synonymous`,
# Ratio_021_GERP16way_2.14andPolyPhen2=`021_GERP16way_2.14andPolyPhen2`/`001_synonymous`,
# Ratio_022_GERP16way_1.78=`022_GERP16way_1.78`/`001_synonymous`,
# Ratio_023_GERP16way_1.78andPolyPhen2=`023_GERP16way_1.78andPolyPhen2`/`001_synonymous`,
# Ratio_024_GERP16way_1andPolyPhen2 = `024_GERP16way_1andPolyPhen2`/`001_synonymous`,
Ratio_025_GERP16way_3.1=`025_GERP16way_3.1`/`001_synonymous`,
# Ratio_026_Derived_PolyPhen2_probably = `026_Derived_PolyPhen2_probably`/`001_synonymous`,
# Ratio_027_GERP16way_1andDerived_PolyPhen2_probably=`027_GERP16way_1andDerived_PolyPhen2_probably`/`001_synonymous`,
# Ratio_028_GERP16way_1.78andDerived_PolyPhen2_probably=`028_GERP16way_1.78andDerived_PolyPhen2_probably`/`001_synonymous`,
# Ratio_029_GERP16way_2.14andDerived_PolyPhen2_probably=`029_GERP16way_2.14andDerived_PolyPhen2_probably`/`001_synonymous`,
# Ratio_030_GERP16way_3.1andDerived_PolyPhen2_probably=`030_GERP16way_3.1andDerived_PolyPhen2_probably`/`001_synonymous`,
Ratio_031_GERP16way_1.5andDerived_PolyPhen2_0.5=`031_GERP16way_1.5andDerived_PolyPhen2_0.5`/`001_synonymous`,
Ratio_032_GERP16way_1.5=`032_GERP16way_1.5`/`001_synonymous`,
Ratio_033_Derived_PolyPhen2_0.5=`033_Derived_PolyPhen2_0.5`/`001_synonymous`) %>%
### wide -> long
pivot_longer(.,cols =c("Ratio_002_nonsynonymous":"Ratio_033_Derived_PolyPhen2_0.5"),names_to ="Stand_LoadGroup" ,values_to = "Stand_delCount") %>%
### 去除不必要的列，方便画图和管理数据
select(- ("001_synonymous":"033_Derived_PolyPhen2_0.5")) %>%
write_tsv(file.path("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/011_merge_byABsubDsub",str_replace_all(basename(filenamefun),"delCount","delRatio")))
return(dfdelRatio)
}
### 函数调用
inputDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/005_delCount/011_merge_byABsubDsub")
# df <- dir(inputDir,pattern = "delCount",full.names = T) %>%
# map(fun_getDelRatio)
```
#### =================
## Del load ratio 技巧画图测试
```{r}
### 先处理del文件
dfdel <- read_tsv("data/004_delCount/delRatio.txt.gz")
dftaxaDB <- read_tsv("data/001_TaxaDB/WheatVMap2_GermplasmInfo.txt") %>%
select(Taxa=VcfID,GenomeType,GroupbyContinent)
factorA <- c("AT","WE","DE","FTT","LR_EU","CL","LR_EA")
factorB <- c("Ratio_002_nonsynonymous","Ratio_031_GERP16way_1.5andDerived_PolyPhen2_0.5","Ratio_007_VEP")
labelsB <- c("Nonsynonymous","Deleterious","High impact")
factorC <- c("TotalDerivedSNPCount","HomoDerivedSNPCount")
labelsC <- c("Additive","Recessive")
df <- dfdel %>% left_join(.,dftaxaDB,by="Taxa") %>%
# filter(DelCountGroup == "TotalDerivedSNPCount") %>%
select(-c("IfSelected","Group_refobj")) %>%
filter(GroupbyContinent %in% factorA) %>%
filter(Stand_LoadGroup %in% factorB) %>%
filter(DelCountGroup %in% factorC) %>%
droplevels() %>%
mutate(GroupbyContinent= factor(GroupbyContinent, levels = factorA)) %>%
mutate(Stand_LoadGroup=factor(Stand_LoadGroup,levels = factorB, labels = labelsB)) %>%
mutate(DelCountGroup=factor(DelCountGroup,levels = factorC,labels = labelsC))
colB <- c("#87cef9","#ffd702","#7f5701","#016699", "#fc6e6e","#9900ff","gray80")
p <- df %>%
filter(DelCountGroup=="Additive") %>%
filter(Stand_LoadGroup=="Deleterious") %>%
ggplot(aes(x=Subgenome,y=Stand_delCount,fill=GroupbyContinent))+
# ylab("Mutation burden")+xlab("")+
ylab("Mutation load")+xlab("")+
# ylab("Derived del SNPs/syn SNPs per accession")+xlab("")+
### volin
# geom_violin(position = position_dodge(0.8),alpha=0.8,width=2) +
# geom_boxplot(position = position_dodge(0.8), width=0.01, color="white", alpha=0.2,outlier.color = NA) +
# # geom_point(position = position_jitterdodge(jitter.width = 0.5, dodge.width = 0.8),aes(color=GroupbyContinent),alpha=0.3)+
# stat_summary(fun=mean, geom="point", color="red",position = position_dodge(0.8),size=0.5)+
### boxplot
# geom_boxplot(position = position_dodge(0.8),outlier.color = NA, alpha = 0.8) +
# geom_point(position = position_jitterdodge(jitter.width = 0.5, dodge.width = 0.8),aes(color=GroupbyContinent),alpha=0.3)+
# stat_summary(fun=mean, geom="point", color="red",position = position_dodge(0.8),size=0.5)+
### boxplot2
geom_boxplot(position = position_dodge(0.8),outlier.color = 'white',alpha=0.5,outlier.size = 0.5)+
geom_point(position = position_jitterdodge(0.2),alpha = 0.3,aes(color=GroupbyContinent),size=0.03)+
stat_summary(fun=mean, geom="point", color="red",position = position_dodge(0.8),size=0.5)+
geom_vline(xintercept = c(1.5,2.5),color = "gray", linetype="dashed")+
### pointrange
# stat_summary(aes(color=GroupbyContinent),fun.data=mean_sdl,position = position_dodge(0.8),geom="pointrange",size=0.3)+
### other para
scale_color_manual(values = colB,name='')+
scale_fill_manual(values = colB,name='')+
theme_classic()+
theme(panel.grid = element_blank(),
panel.background = element_blank(),
axis.line = element_line(size=0.3, colour = "black"),
legend.position = 'none',
text = element_text(size = 12))+
theme(strip.background = element_blank())
p
ggsave(p,filename = "~/Documents/test.pdf",height = 3,width = 5.3)
```
#### ======正文
## Del load ratio
```{r}
choiceA <- "Ratio_031_GERP16way_1.5andDerived_PolyPhen2_0.5"
### 先处理del文件
dfdel <- read_tsv("data/004_delCount/delRatio.txt.gz")
dftaxaDB <- read_tsv("data/001_TaxaDB/WheatVMap2_GermplasmInfo.txt") %>%
select(Taxa=VcfID,GenomeType,GroupbyContinent)
factorA <- c("AT","WE","DE","FTT","LR_EU","CL","LR_EA")
factorB <- c("Ratio_002_nonsynonymous","Ratio_031_GERP16way_1.5andDerived_PolyPhen2_0.5","Ratio_007_VEP")
labelsB <- c("Nonsynonymous","Deleterious","High impact")
df <- dfdel %>% left_join(.,dftaxaDB,by="Taxa") %>%
filter(DelCountGroup == "TotalDerivedSNPCount") %>%
select(-c("IfSelected","Group_refobj","DelCountGroup")) %>%
filter(GroupbyContinent %in% factorA) %>%
filter(Stand_LoadGroup %in% factorB) %>%
droplevels() %>%
mutate(GroupbyContinent= factor(GroupbyContinent, levels = factorA)) %>%
mutate(Stand_LoadGroup=factor(Stand_LoadGroup,levels = factorB, labels = labelsB))
colB <- c("#87cef9","#ffd702","#7f5701","#016699", "#fc6e6e","#9900ff","gray80")
```
### plot
```{r}
p <- df %>%
ggplot(aes(x=Subgenome,y=Stand_delCount,fill=GroupbyContinent))+
ylab("Mutation burden")+xlab("")+
### volin
geom_violin(position = position_dodge(0.8),alpha=0.8,width=2) +
geom_boxplot(position = position_dodge(0.8), width=0.01, color="white", alpha=0.2,outlier.color = NA) +
# geom_point(position = position_jitterdodge(jitter.width = 0.5, dodge.width = 0.8),aes(color=GroupbyContinent),alpha=0.3)+
stat_summary(fun=mean, geom="point", color="red",position = position_dodge(0.8),size=0.5)+
### boxplot
# geom_boxplot(position = position_dodge(0.8),outlier.color = NA, alpha = 0.8) +
# geom_point(position = position_jitterdodge(jitter.width = 0.5, dodge.width = 0.8),aes(color=GroupbyContinent),alpha=0.3)+
# stat_summary(fun=mean, geom="point", color="red",position = position_dodge(0.8),size=0.5)+
### pointrange
# stat_summary(aes(color=GroupbyContinent),fun.data=mean_sdl,position = position_dodge(0.8),geom="pointrange",size=0.3)+
scale_color_manual(values = colB,name='')+
scale_fill_manual(values = colB,name='')+
facet_grid(Stand_LoadGroup~.,scales = "free")+
# facet_wrap(Stand_LoadGroup~.,scales = "free", ncol = 1)+
# ggtitle("Del total count under all genes in vmap 2.1-2021") +
theme(strip.background = element_blank())+
theme(
panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=0.4)
,legend.position = 'none'
,text = element_text(size = 9))
p
# ggsave(p,filename = "~/Documents/test.pdf",height = 3,width = 4)
ggsave(p,filename = "figs/Fig3/misc/whole_load.pdf",height = 3,width = 4)
```
## Del count
```{r}
### 先处理del文件
dfdel <- read_tsv("data/004_delCount/delCount.txt.gz")
dftaxaDB <- read_tsv("data/001_TaxaDB/WheatVMap2_GermplasmInfo.txt") %>%
select(Taxa=VcfID,GenomeType,GroupbyContinent)
factorA <- c("AT","WE","DE","FTT","LR_EU","CL","LR_EA")
factorB <- c("002_nonsynonymous","031_GERP16way_1.5andDerived_PolyPhen2_0.5","007_VEP")
labelsB <- c("Nonsynonymous","Deleterious","High impact")
df <- dfdel %>% left_join(.,dftaxaDB,by="Taxa") %>%
select(-c("IfSelected","Group_refobj","SiteCountWithMinDepth")) %>%
filter(GroupbyContinent %in% factorA) %>%
filter(VariantsGroup %in% factorB) %>%
droplevels() %>%
mutate(GroupbyContinent= factor(GroupbyContinent, levels = factorA)) %>%
mutate(VariantsGroup=factor(VariantsGroup,levels = factorB, labels = labelsB))
colB <- c("#87cef9","#ffd702","#7f5701","#016699", "#fc6e6e","#9900ff","gray70")
```
### plot
```{r}
p <- df %>%
ggplot(aes(x=Subgenome,y=TotalDerivedSNPCount,fill=GroupbyContinent))+
labs(y="No. derived SNPs",x="")+
### volin
# geom_violin(position = position_dodge(0.8),alpha=0.8,width=2) +
# geom_boxplot(position = position_dodge(0.8), width=0.01, color="white", alpha=0.2,outlier.color = NA) +
# # geom_point(position = position_jitterdodge(jitter.width = 0.5, dodge.width = 0.8),aes(color=GroupbyContinent),alpha=0.3)+
# stat_summary(fun=mean, geom="point", color="red",position = position_dodge(0.8),size=0.5)+
### boxplot
# geom_boxplot(position = position_dodge(0.8),outlier.color = NA, alpha = 0.8) +
# geom_point(position = position_jitterdodge(jitter.width = 0.5, dodge.width = 0.8),aes(color=GroupbyContinent),alpha=0.3)+
# stat_summary(fun=mean, geom="point", color="red",position = position_dodge(0.8),size=0.5)+
### pointrange
stat_summary(aes(color=GroupbyContinent),fun.data=mean_sdl,
fun.args = list(mult = 1),
position = position_dodge(0.8),geom="pointrange")+
stat_summary(fun=mean, geom="point", color="white",position = position_dodge(0.8))+
scale_color_manual(values = colB,name='')+
scale_fill_manual(values = colB,name='')+
facet_grid(VariantsGroup~.,scales = "free")+
# facet_wrap(Stand_LoadGroup~.,scales = "free", ncol = 1)+
# ggtitle("Del total count under all genes in vmap 2.1-2021") +
theme(strip.background = element_blank())+
theme(
panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=0.4)
,legend.position = 'none'
,text = element_text(size = 9))
p
ggsave(p,filename = "~/Documents/test.pdf",height = 3,width = 4)
# ggsave(p,filename = "figs/Fig3/misc/whole_load_count.pdf",height = 3,width = 4)
```
## 文件名字
FigS_delCount
FigS_delRatio
FigS_DelVsNonsynProportion
FigS_delCount_triads
FigS_delRatio_triads
FigS_triads_DelVsNonsynProportion
FigS_delCount_hexaploid
FigS_delRatio_hexaploid
FigS_hexaploid_DelVsNonsynProportion
FigS_delCount_hexaploid_triads
FigS_delRatio_hexaploid_triads
FigS_hexaploid_triads_DelVsNonsynProportion
#### ====== 附件 1
## Whole genome / Triads
## Del count
```{r}
plot_getDelCount <- function(filenamefun){
dfdel <- read_tsv(filenamefun)
dftaxaDB <- read_tsv("data/001_TaxaDB/WheatVMap2_GermplasmInfo.txt") %>%
select(Taxa=VcfID,GenomeType,GroupbyContinent)
factorA <- c("AT","WE","DE","FTT","LR_EU","CL","LR_EA")
factorB <- c("001_synonymous","002_nonsynonymous","031_GERP16way_1.5andDerived_PolyPhen2_0.5","007_VEP")
labelsB <- c("Synonymous","Nonsynonymous","Deleterious","High impact")
factorC <- c("TotalDerivedSNPCount","HomoDerivedSNPCount")
labelsC <- c("Additive","Recessive")
df <- dfdel %>%
select(-c("IfSelected","Group_refobj","SiteCountWithMinDepth","HeterDerivedSNPCount")) %>%
pivot_longer(cols = c(TotalDerivedSNPCount,HomoDerivedSNPCount,),names_to = "DelCountGroup",values_to = "Count") %>%
left_join(.,dftaxaDB,by="Taxa") %>%
filter(GroupbyContinent %in% factorA) %>%
filter(VariantsGroup %in% factorB) %>%
droplevels() %>%
mutate(GroupbyContinent= factor(GroupbyContinent, levels = factorA)) %>%
mutate(VariantsGroup=factor(VariantsGroup,levels = factorB, labels = labelsB)) %>%
mutate(DelCountGroup=factor(DelCountGroup,levels = factorC,labels = labelsC))
colB <- c("#87cef9","#ffd702","#7f5701","#016699", "#fc6e6e","#9900ff","gray70")
p <- df %>%
# filter(DelCountGroup=="Additive") %>%
ggplot(aes(x=Subgenome,y=Count,fill=GroupbyContinent))+
labs(y="No. derived SNPs",x="")+
### pointrange
stat_summary(aes(color=GroupbyContinent),fun.data=mean_sdl,
fun.args = list(mult = 1),
position = position_dodge(0.8),geom="pointrange")+
stat_summary(fun=mean, geom="point", color="white",position = position_dodge(0.8))+
geom_vline(xintercept = c(1.5,2.5),color = "gray", linetype="dashed")+
### other para
scale_color_manual(values = colB,name='')+
scale_fill_manual(values = colB,name='')+
facet_grid(VariantsGroup~DelCountGroup,scales = "free")+
# facet_wrap(Stand_LoadGroup~.,scales = "free", ncol = 1)+
theme(strip.background = element_blank())+
theme(
panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=0.4),
# legend.position = 'none',
### 添加 legend
legend.key = element_blank(),
legend.position = "bottom",
text = element_text(size = 9))+
guides(color=guide_legend(ncol=7))
p
outDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/RProject/R4VMap2FigsS1062/figs/FigS_delCount/misc")
outDir <- c("~/Documents/")
ggsave(p,filename = file.path(outDir,str_c(str_split_fixed(basename(filenamefun),".txt",2)[,1],".pdf")),height = 4.45,width = 7.2)
library("ggpubr")
ggp_legend <- get_legend(p)
ggsave(as_ggplot(ggp_legend),filename = "~/Documents/legend.pdf",height = 1,width = 5)
return(p)
}
p <- plot_getDelCount("data/004_delCount/delCount.txt.gz")
p
p <- plot_getDelCount("data/004_delCount/delCount_triads.txt.gz")
p
```
## Del count from 0 - max
```{r}
plot_getDelCount <- function(filenamefun){
dfdel <- read_tsv(filenamefun)
dftaxaDB <- read_tsv("data/001_TaxaDB/WheatVMap2_GermplasmInfo.txt") %>%
select(Taxa=VcfID,GenomeType,GroupbyContinent)
factorA <- c("AT","WE","DE","FTT","LR_EU","CL","LR_EA")
factorB <- c("001_synonymous","002_nonsynonymous","031_GERP16way_1.5andDerived_PolyPhen2_0.5","007_VEP")
labelsB <- c("Synonymous","Nonsynonymous","Deleterious","High impact")
factorC <- c("TotalDerivedSNPCount","HomoDerivedSNPCount")
labelsC <- c("Additive","Recessive")
df <- dfdel %>%
select(-c("IfSelected","Group_refobj","SiteCountWithMinDepth","HeterDerivedSNPCount")) %>%
pivot_longer(cols = c(TotalDerivedSNPCount,HomoDerivedSNPCount,),names_to = "DelCountGroup",values_to = "Count") %>%
left_join(.,dftaxaDB,by="Taxa") %>%
filter(GroupbyContinent %in% factorA) %>%
filter(VariantsGroup %in% factorB) %>%
droplevels() %>%
mutate(GroupbyContinent= factor(GroupbyContinent, levels = factorA)) %>%
mutate(VariantsGroup=factor(VariantsGroup,levels = factorB, labels = labelsB)) %>%
mutate(DelCountGroup=factor(DelCountGroup,levels = factorC,labels = labelsC))
colB <- c("#87cef9","#ffd702","#7f5701","#016699", "#fc6e6e","#9900ff","gray70")
p <- df %>%
# filter(DelCountGroup=="Additive") %>%
ggplot(aes(x=Subgenome,y=Count,fill=GroupbyContinent))+
labs(y="No. derived SNPs",x="")+
### pointrange
stat_summary(aes(color=GroupbyContinent),fun.data=mean_sdl,
fun.args = list(mult = 1),
position = position_dodge(0.8),geom="pointrange")+
stat_summary(fun=mean, geom="point", color="white",position = position_dodge(0.8))+
geom_vline(xintercept = c(1.5,2.5),color = "gray", linetype="dashed")+
### other para
scale_color_manual(values = colB,name='')+
scale_fill_manual(values = colB,name='')+
facet_grid(VariantsGroup~DelCountGroup,scales = "free")+
# facet_wrap(Stand_LoadGroup~.,scales = "free", ncol = 1)+
expand_limits(y = 0)+
theme(strip.background = element_blank())+
theme(
panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=0.4),
# legend.position = 'none',
### 添加 legend
legend.key = element_blank(),
legend.position = "bottom",
text = element_text(size = 12))+
guides(color=guide_legend(ncol=7))
p
outDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/RProject/R4VMap2FigsS1062/figs/FigS_delCount/misc")
outDir <- c("~/Documents/")
ggsave(p,filename = file.path(outDir,str_c(str_split_fixed(basename(filenamefun),".txt",2)[,1],".pdf")),height = 4.45,width = 7.2)
library("ggpubr")
ggp_legend <- get_legend(p)
ggsave(as_ggplot(ggp_legend),filename = "~/Documents/legend.pdf",height = 1,width = 5)
return(p)
}
p <- plot_getDelCount("data/004_delCount/delCount.txt.gz")
p
p <- plot_getDelCount("data/004_delCount/delCount_triads.txt.gz")
p
```
## Del load ratio
```{r}
plot_getDelRatio <- function(filenamefun){
### 先处理del文件
dfdel <- read_tsv(filenamefun)
dftaxaDB <- read_tsv("data/001_TaxaDB/WheatVMap2_GermplasmInfo.txt") %>%
select(Taxa=VcfID,GenomeType,GroupbyContinent)
factorA <- c("AT","WE","DE","FTT","LR_EU","CL","LR_EA")
factorB <- c("Ratio_002_nonsynonymous","Ratio_031_GERP16way_1.5andDerived_PolyPhen2_0.5","Ratio_007_VEP")
labelsB <- c("Nonsynonymous","Deleterious","High impact")
factorC <- c("TotalDerivedSNPCount","HomoDerivedSNPCount")
labelsC <- c("Additive","Recessive")
df <- dfdel %>% left_join(.,dftaxaDB,by="Taxa") %>%
# filter(DelCountGroup == "TotalDerivedSNPCount") %>%
select(-c("IfSelected","Group_refobj")) %>%
filter(GroupbyContinent %in% factorA) %>%
filter(Stand_LoadGroup %in% factorB) %>%
filter(DelCountGroup %in% factorC) %>%
droplevels() %>%
mutate(GroupbyContinent= factor(GroupbyContinent, levels = factorA)) %>%
mutate(Stand_LoadGroup=factor(Stand_LoadGroup,levels = factorB, labels = labelsB)) %>%
mutate(DelCountGroup=factor(DelCountGroup,levels = factorC,labels = labelsC))
colB <- c("#87cef9","#ffd702","#7f5701","#016699", "#fc6e6e","#9900ff","gray80")
p <- df %>%
# filter(DelCountGroup=="Additive") %>%
# filter(Stand_LoadGroup=="Deleterious") %>%
ggplot(aes(x=Subgenome,y=Stand_delCount,fill=GroupbyContinent))+
# ylab("Mutation burden")+xlab("")+
ylab("Mutation load")+xlab("")+
# ylab("Derived del SNPs/syn SNPs per accession")+xlab("")+
### volin
# geom_violin(position = position_dodge(0.8),alpha=0.8,width=2) +
# geom_boxplot(position = position_dodge(0.8), width=0.01, color="white", alpha=0.2,outlier.color = NA) +
# # geom_point(position = position_jitterdodge(jitter.width = 0.5, dodge.width = 0.8),aes(color=GroupbyContinent),alpha=0.3)+
# stat_summary(fun=mean, geom="point", color="red",position = position_dodge(0.8),size=0.5)+
### boxplot
# geom_boxplot(position = position_dodge(0.8),outlier.color = NA, alpha = 0.8) +
# geom_point(position = position_jitterdodge(jitter.width = 0.5, dodge.width = 0.8),aes(color=GroupbyContinent),alpha=0.3)+
# stat_summary(fun=mean, geom="point", color="red",position = position_dodge(0.8),size=0.5)+
### boxplot2
geom_boxplot(position = position_dodge(0.8),outlier.color = 'white',alpha=0.5,outlier.size = 0.5)+
geom_point(position = position_jitterdodge(0.2),alpha = 0.3,aes(color=GroupbyContinent),size=0.03)+
stat_summary(fun=mean, geom="point", color="red",position = position_dodge(0.8),size=0.5)+
geom_vline(xintercept = c(1.5,2.5),color = "gray", linetype="dashed")+
### pointrange
# stat_summary(aes(color=GroupbyContinent),fun.data=mean_sdl,position = position_dodge(0.8),geom="pointrange",size=0.3)+
### other para
scale_color_manual(values = colB,name='')+
scale_fill_manual(values = colB,name='')+
# facet_grid(Stand_LoadGroup~DelCountGroup,scales = "free")+
facet_wrap(Stand_LoadGroup~DelCountGroup,scales = "free", ncol = 2,
strip.position = c("right"))+
# ggtitle("Del total count under all genes in vmap 2.1-2021") +
theme_classic()+
theme(panel.grid = element_blank(),
panel.background = element_blank(),
axis.line = element_line(size=0.3, colour = "black"),
# legend.position = 'none',
### 添加 legend
legend.key = element_blank(),
legend.position = "bottom",
strip.background = element_rect(color = "white",fill = "gray90"),
text = element_text(size = 12))+
guides(color=guide_legend(ncol=7))
p
outDir <- c("/Users/Aoyue/project/wheatVMap2_1000/002_dataAnalysis/RProject/R4VMap2FigsS1062/figs/FigS_delCount/misc")
outDir <- c("~/Documents")
ggsave(p,filename = file.path(outDir,str_c(str_split_fixed(basename(filenamefun),".txt",2)[,1],".pdf")),height = 7.2,width = 7.2)
library("ggpubr")
ggp_legend <- get_legend(p)
ggsave(as_ggplot(ggp_legend),filename = "~/Documents/legend.pdf",height = 1,width = 5)
return(p)
}
p <- plot_getDelRatio("data/004_delCount/delRatio.txt.gz")
p
# p <- plot_getDelRatio("data/004_delCount/delRatio_triads.txt.gz")
# p
```
## Del load ratio - AB subgenome
```{r}
plot_getDelRatio <- function(filenamefun){
### 先处理del文件
dfdel <- read_tsv(filenamefun)
dftaxaDB <- read_tsv("data/001_TaxaDB/WheatVMap2_GermplasmInfo.txt") %>%
select(Taxa=VcfID,GenomeType,GroupbyContinent)
factorA <- c("AT","WE","DE","FTT","LR_EU","CL","LR_EA")
factorB <- c("Ratio_002_nonsynonymous","Ratio_031_GERP16way_1.5andDerived_PolyPhen2_0.5","Ratio_007_VEP")
labelsB <- c("Nonsynonymous","Deleterious","High impact")
factorC <- c("TotalDerivedSNPCount","HomoDerivedSNPCount")
labelsC <- c("Additive","Recessive")
df <- dfdel %>% left_join(.,dftaxaDB,by="Taxa") %>%
# filter(DelCountGroup == "TotalDerivedSNPCount") %>%
select(-c("IfSelected","Group_refobj")) %>%
filter(GroupbyContinent %in% factorA) %>%
filter(Stand_LoadGroup %in% factorB) %>%
filter(DelCountGroup %in% factorC) %>%
droplevels() %>%
mutate(GroupbyContinent= factor(GroupbyContinent, levels = factorA)) %>%
mutate(Stand_LoadGroup=factor(Stand_LoadGroup,levels = factorB, labels = labelsB)) %>%
mutate(DelCountGroup=factor(DelCountGroup,levels = factorC,labels = labelsC))
colB <- c("#87cef9","#ffd702","#7f5701","#016699", "#fc6e6e","#9900ff","gray80")
p <- df %>%
# filter(DelCountGroup=="Additive") %>%
# filter(Stand_LoadGroup=="Deleterious") %>%
ggplot(aes(x=Subgenome,y=Stand_delCount,fill=GroupbyContinent))+
# ylab("Mutation burden")+xlab("")+
ylab("Mutation load")+xlab("")+
# ylab("Derived del SNPs/syn SNPs per accession")+xlab("")+
### volin
# geom_violin(position = position_dodge(0.8),alpha=0.8,width=2) +
# geom_boxplot(position = position_dodge(0.8), width=0.01, color="white", alpha=0.2,outlier.color = NA) +
# # geom_point(position = position_jitterdodge(jitter.width = 0.5, dodge.width = 0.8),aes(color=GroupbyContinent),alpha=0.3)+
# stat_summary(fun=mean, geom="point", color="red",position = position_dodge(0.8),size=0.5)+
### boxplot
# geom_boxplot(position = position_dodge(0.8),outlier.color = NA, alpha = 0.8) +
# geom_point(position = position_jitterdodge(jitter.width = 0.5, dodge.width = 0.8),aes(color=GroupbyContinent),alpha=0.3)+
# stat_summary(fun=mean, geom="point", color="red",position = position_dodge(0.8),size=0.5)+
### boxplot2
geom_boxplot(position = position_dodge(0.8),outlier.color = 'white',alpha=0.5,outlier.size = 0.5)+
geom_point(position = position_jitterdodge(0.2),alpha = 0.3,aes(color=GroupbyContinent),size=0.03)+
stat_summary(fun=mean, geom="point", color="red",position = position_dodge(0.8),size=0.5)+
geom_vline(xintercept = c(1.5,2.5),color = "gray", linetype="dashed")+