1、安装VMware tools或挂载共享文件夹
2、下载anaconda
https://repo.anaconda.com/archive/
将下载好的anaconda包放到虚拟机中
3、在anaconda安装包路径下，依次输入
chmod +x Anaconda3-5.3.0-Linux-x86_64.sh
./Anaconda3-5.3.0-Linux-x86_64.sh
回车*n yes，开始安装anaconda
4、安装完成后在目标路径下添加环境变量
cd /home/maki/anaconda3
sudo vim /etc/profile (这里可能需要先安装vim : sudo install vim)
按“i”进入编辑
在文件最后一行添加：
export PATH="/home/maki/anaconda3/bin:$PATH:"
image.png
esc :wq! :q! 退出编辑
更新配置
source ~/.bashrc5、添加镜像
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/ conda config --set show_channel_urls yes
conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge
6.1、下载安装
conda install admixture
6.2、本地安装admixture
先将admixture压缩包放入共享文件夹
进入共享文件夹，此处为/mnt/hgfs/LinuxShare
image.png
解压缩安装包
sudo tar -zxv -f admixture_linux-1.3.0.tar.gz -C /usr/local/srcadmixture
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7、admixture使用
注意这里的snp文件需要先质控
plink --bfile xxx --maf 0.05 --geno 0.05 --mind 0.05 --make-bed --out
admixture xxx.bed 3
for K in 1 2 3 4 5; do admixture --cv hapmap3.bed $K | tee log${K}.out; done
grep -h CV *out
8、R语言作图
setwd("f:/admixture")
ta3= read.table("hapmap3.3.Q")
barplot(t(as.matrix(ta3)),col = rainbow(3),xlab = "Individual",ylab = "Ancestry",border = NA, main="k=3")
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横向作图
barplot(t(as.matrix(ta3)),horiz=T,col = topo.colors(3),xlab = "品种占比",ylab = "个体",border = NA, main="个体品种纯度")
红橙黄3breed.jpeg
9、python作图
data_3breed = pd.read_csv(open(r'F:\admixture\7517id_2.3.Q'),sep='\s+',header=None)
data_id = pd.read_csv(open(r'F:\plink\7517id_2.fam'),sep='\s+',header=None)
data_3breed_id = pd.merge(data_id, data_3breed, how='outer',left_index=True,right_index=True)
data_3breed_id['breed']=data_3breed_id['1_x']
data_3breed_id['breed']=data_3breed_id['breed'].astype(str)
data_3breed_id['breed']=data_3breed_id.breed.apply(lambda x : x[:2])
plt.figure(figsize=(40,10),dpi=80)
s1=plt.bar(data_3breed_id['1_x'],data_3breed_id['0_y'],0.8,0,label='LL',color="#F08080")
s2=plt.bar(data_3breed_id['1_x'],data_3breed_id['1_y'],0.8,data_3breed_id['0_y'],label='YY',color="#F0E68C")
s3=plt.bar(data_3breed_id['1_x'],data_3breed_id['2_y'],0.8,(1-data_3breed_id['2_y']),label='DD',color="#3CB371")
plt.xticks(horizontalalignment='left',rotation=-30,fontsize=5)
plt.legend()
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