基本概念
基迪奧有篇文章寫得非常的簡單明了，我這里就不再贅述，大家移步去搞清楚基本知識。
STRUCTURE軟件的使用準則
軟件假設輸入的標記數據中，每個標記都是獨立的，所以在分析之前，需要對標記按照一定規則進行篩選。常見篩選方法有如下三種Nat Rev Genet, 2015：
- 一定物理距離取一個代表用于分析
- 全基因組上隨機抽取一部分標記進行分析
- 按照LD篩選：LD強度大于一定閾值的標記只保留一個用于分析
STRUCTURE軟件實操：
前期準備
給標記加上ID
SNP data通常都是以VCF格式文件呈現，拿到VCF文件的第一件事情就是添加各個SNP位點的ID。
先看一下最開始生成的VCF文件：
原始VCF文件
可以看到，ID列都是"."，需要我們自己加上去。我用的是某不知名大神寫好的perl腳本，可以去我的github上下載，用法：
perl path2file/VCF_add_id.pl YourDataName.vcf YourDataName-id.vcf`
當然也可以用excel手工添加。添加后的文件如下圖所示（格式：CHROMID__POS）：
添加ID后VCF文件
SNP位點過濾（Missing rate and maf filtering）
SNP位點過濾前需要問自己一個問題，我的數據需要過濾嗎？
一般要看后期是否做關聯分析（GWAS）；如果只是單純研究群體結構建議不過濾，因為過濾掉低頻位點可能會改變某些樣本之間的關系；如果需要和表型聯系其來做關聯分析，那么建議過濾，因為在后期分析中低頻位點是不在考慮范圍內的，需要保持前后一致。
如果過濾，此處用到強大的plink軟件，用法：
plink --vcf YourDataName-id.vcf --maf 0.05 --geno 0.2 --recode vcf-iid -out YourDataName-id-maf0.05 --allow-extra-chr
參數解釋：--maf 0.05：過濾掉次等位基因頻率低于0.05的位點；--geno 0.2：過濾掉有20%的樣品缺失的SNP位點；--allow-extra-chr：我的參考數據是Contig級別的，個數比常見分析所用的染色體多太多，所以需要加上此參數。
LD篩選（LD pruning and make bed file）
前文提到STRUCTURE軟件假設輸入的標記數據中，每個標記都是獨立的，所以我們需要對標記按照一定規則進行篩選，這里用其中的一種方法——LD篩選。
plink --vcf YourDataName-id-maf0.05.vcf --indep-pairwise 100 50 0.2 -out YourDataName-id-maf0.05-LD --allow-extra-chr --make-bed
100—以100個kb為單位；50—SNP數目，50個SNP的步長；0.2—LD強度。
轉換為STRUCTURE格式
plink --bfile YourDataName-id-maf0.05-LD --extract YourDataName-id-maf0.05-LD.prune.in --out YourDataName-id-maf0.05-LD-structure --recode structure --allow-extra-chr
填寫STRUCTURE配置文件：
配置文件有兩個，分別是mainparams和extraparams。我們需要填寫mainparams同時生成空extraparams文件。
注意：mainparams配置文件的個數為最大K值乘重復次數，如計算K從1到10，每個重復3次，則要有30個該文件，也要有對應的30個命令行。
K=1第一次重復的mainparams配置文件及參數介紹
如果計算K從1到10，每個重復3次，30個配置文件可以這樣命名：
mainparams配置文件命名方法
STRUCTURE運行
運行STRUCTURE很簡單：
#單個運行：
structure -m mainparams_1_1 -e extraparams
structure -m mainparams_1_2 -e extraparams
structure -m mainparams_1_3 -e extraparams
。
。
#同時運行：將mainparams配置文件名放到一個list中，用for循環調用運行STRUCTURE：
for i in $(less mainparams.list); do nohup structure -m ${i} -e extraparams & done
結果可視化
Structure的結果可視化用到一個R包——pophelper，需要在R環境中安裝后調用。注意：新版pophelper用下述命令會報錯，最好使用V2.2.9
#安裝pophelper 2.2.9軟件：
install.packages(c("Cairo","devtools","ggplot2","gridExtra","gtable","tidyr"),dependencies=T)
devtools::install_github('royfrancis/pophelper')
數據可視化包括兩個方面，1）計算K值并畫圖，2）繪制Structure堆疊圖。方法很簡單，把所有的結果都放在同一個文件夾里，調用pophelpe即可，寫好的R命令如下所示，按需求執行：
另外，需要準備分組文件（pop_list.txt）,我分了如下圖的幾列，大家可以自己DIY。注意：該文件中的樣品排序需要與VCF中的樣品排序相對應
pop_list.txt
# read structure results
#更改工作路徑（該路徑下存有Structure所有的運行結果）
setwd("F:structure_results")
#調用pophelper
library(pophelper)
file_list <- list.files(path = "./out/", full.names = T) # list file directory
qlist <- readQ(file_list) # read result files
# evanno method to calculate deltaK
tbq <- tabulateQ(qlist)
smq <- summariseQ(tbq)
###繪制最佳K值線
evannoMethodStructure(smq, exportplot = T, writetable = T,
imgtype = "png", height = 16, width = 18,outputfilename = "evanno")
evannoMethodStructure(smq, exportplot = T, writetable = T,
imgtype = "pdf", height = 16, width = 18,outputfilename = "evanno")
# clumpp repeat results
clumppExport(qlist = qlist, parammode = 3, prefix = "pop", useexe = T) # run clumpp
collectClumppOutput(prefix = "pop", filetype = "both", runsdir = getwd()) # collect clumpp results
# read clumpp merged results
fclum <- list.files(path = "pop-both", full.names = T, pattern = "merge")
qclum <- readQ(fclum)
sample_order <- read.table("./pop_list.txt", header = T, stringsAsFactors = F)
ind_name <- sample_order[,1]
for(i in 1:length(qclum)){
row.names(qclum[[i]]) <- ind_name
}
mink <- 2
maxk <- 10
k_order <- vector()
if(maxk < 10){
k_order <- 1:length(qclum)
} else if (maxk < 20) {
end1 <- maxk - 10 + 1
start2 <- end1 + 1
k_order <- c(start2:length(qclum), 1:end1)
}
klab <- vector()
if(mink == 1){
klab <- 2:maxk
} else {
klab <- mink:maxk
}
# 繪制全局structure圖
# plot global barplot without group information
prefix <- "demo"
height <- 2
width <- 16
plotQ(qclum[k_order], imgoutput="join",showindlab=T, showlegend=F, sortind = "all",
indlabsize=0.5,indlabheight=0,indlabspacer=0.05,barbordersize=NA,
outputfilename=prefix,imgtype="pdf", sharedindlab = F,
useindlab = T, showyaxis = T, basesize = 10, sppos = "right", showticks = T,
splab = paste0("K = ", klab), splabsize = 6, splabface = "bold",
width = width, height = height, panelspacer = 0.02, dpi = 600, barbordercolour = NA)
plotQ(qclum[k_order], imgoutput="join",showindlab=T, showlegend=F, sortind = "all",
indlabsize=0.5,indlabheight=0,indlabspacer=0.05,barbordersize=0.1,
outputfilename=prefix,imgtype="png", sharedindlab = F,
useindlab = T, showyaxis = T, basesize = 10, sppos = "right", showticks = T,
splab = paste0("K = ", klab), splabsize = 6, splabface = "bold",
width = width, height = height, panelspacer = 0.02, dpi = 600, barbordercolour = NA)
# 繪制全局并帶有組信息的structure圖
# plot global barplot with group information
prefix <- "demo_label"
plotQ(qclum[k_order], imgoutput="join",showindlab=T, showlegend=F, sortind = "all",
indlabsize=0.5,indlabheight=0,indlabspacer=0.05,barbordersize=NA,
outputfilename=prefix,imgtype="pdf", sharedindlab = F,
useindlab = T, showyaxis = T, basesize = 10, sppos = "right", showticks = T,
splab = paste0("K = ", klab), splabsize = 6, splabface = "bold",
width = width, height = height, panelspacer = 0.02, dpi = 600, barbordercolour = NA,
grplab=sample_order[,2:3,drop=FALSE],ordergrp=T, grplabsize=2, grplabheight = 4)
plotQ(qclum[k_order], imgoutput="join",showindlab=T, showlegend=F, sortind = "all",
indlabsize=0.5,indlabheight=0,indlabspacer=0.05,barbordersize=0.1,
outputfilename=prefix,imgtype="png", sharedindlab = F,
useindlab = T, showyaxis = T, basesize = 10, sppos = "right", showticks = T,
splab = paste0("K = ", klab), splabsize = 6, splabface = "bold",
width = width, height = height, panelspacer = 0.02, dpi = 600, barbordercolour = NA,
grplab=sample_order[,2:3,drop=FALSE],ordergrp=T,grplabsize=2, grplabheight = 4)
# 繪制各個k值的structure圖
# plot single K barplot
plotQ(qclum, imgoutput = "sep",showindlab=T, showlegend=F, sortind = "all",
indlabsize=0.5,indlabheight=0,indlabspacer=0.05,barbordersize=NA,
imgtype="pdf", sharedindlab = F,
useindlab = T, showyaxis = T, basesize = 10, sppos = "right", showticks = T,
splabsize = 6, splabface = "bold",
width = width, height = height, panelspacer = 0.02, dpi = 600, barbordercolour = NA)
plotQ(qclum, imgoutput = "sep",showindlab=T, showlegend=F, sortind = "all",
indlabsize=0.5,indlabheight=0,indlabspacer=0.05,barbordersize=NA,
imgtype="pdf", sharedindlab = F,
useindlab = T, showyaxis = T, basesize = 10, sppos = "right", showticks = T,
splabsize = 6, splabface = "bold",
width = width, height = height, panelspacer = 0.02, dpi = 600, barbordercolour = NA,
grplab=sample_order[,2:3,drop=FALSE],ordergrp=T,grplabsize=2, grplabheight = 4)
## for admixture plot
library(pophelper)
setwd("F:/works/developing/course/gwas/data/lecture07/admixture_results")
file_list_admix <- list.files("admixture_output/", pattern = ".Q", full.names = T)
info <- read.table("sample_order.txt", header = T, stringsAsFactors = F)
qlist_admix <- readQ(file_list_admix)
for(i in 1:length(qlist_admix)){
row.names(qlist_admix[[i]]) <- info$sample
}
k_order <- vector()
mink <- 1
maxk <- 10
if(maxk < 10){
k_order <- 1:length(qlist_admix)
} else if (maxk < 20) {
end1 <- maxk - 10 + 1
start2 <- end1 + 1
k_order <- c(start2:length(qlist_admix), 1:end1)
}
klab <- vector()
if(mink == 1){
klab <- 2:maxk
} else {
klab <- mink:maxk
}
prefix <- "admix"
height <- 1
width <- 16
plotQ(qlist_admix[k_order], imgoutput="join",showindlab=T, showlegend=F, sortind = "all",
indlabsize=0.5,indlabheight=0,indlabspacer=0.05,barbordersize=NA,
outputfilename=prefix,imgtype="pdf", sharedindlab = F,
useindlab = T, showyaxis = T, basesize = 10, sppos = "right", showticks = T,
splab = paste0("K = ", klab), splabsize = 6, splabface = "bold",
width = width, height = height, panelspacer = 0.02, dpi = 600, barbordercolour = NA)
plotQ(qlist_admix[k_order], imgoutput="join",showindlab=T, showlegend=F, sortind = "all",
indlabsize=0.5,indlabheight=0,indlabspacer=0.05,barbordersize=NA,
outputfilename=prefix,imgtype="png", sharedindlab = F,
useindlab = T, showyaxis = T, basesize = 10, sppos = "right", showticks = T,
splab = paste0("K = ", klab), splabsize = 6, splabface = "bold",
width = width, height = height, panelspacer = 0.02, dpi = 600, barbordercolour = NA)
參考：
群體結構圖形——structure堆疊圖
Sehraiber J G. Methods and models for unravelling human evolutionary history. Nature Reviews. Genetics, 2015
