【记录】安装生信的代码编写环境

安装 vscode-web#

安装好 docker
QUIC 转 SOCKS5

mkdir -p ~/app/vscode && cd ~/app/vscode && nano docker-compose.yml
sudo docker-compose up -d && sudo docker-compose logs

version: "2.1"
services:
  code-server:
    image: linuxserver/code-server:latest
    container_name: code-server
    environment:
      - PUID=1000
      - PGID=1000
      - TZ=Asia/Shanghai
      - PASSWORD=password
      - SUDO_PASSWORD=password
      - PROXY_DOMAIN=code-server.my.domain #optional
      - DEFAULT_WORKSPACE=/config/workspace #optional
    volumes:
      - ./config:/config
    ports:
      - 2441:8443
    restart: unless-stopped

配置代理和中文#

打开 Visual Studio Code，点击 Manage，在列表中选择 Settings
在弹出的搜索框中输入 "proxy"，即可看到代理的配置项 "Http"
宿主机获取 docker0 的 ip: ip address | grep docker0
然后 docker 内设置代理 http://docker0的ip:port
拓展内搜索 zh-cn，安装中文界面拓展

安装 conda#

回到 WORKSPACE，ctrl+~ 调出终端
sudo sed -i 's/archive.ubuntu.com/mirrors.aliyun.com/g' /etc/apt/sources.list
sudo apt update
sudo apt install wget
安装 conda

安装 nodejs#

conda create -n node -c conda-forge nodejs
conda activate node
npm config set registry https://registry.npmmirror.com

使用 git#

npm create astro@latest
git config --global user.email "youremail"
git config --global user.name "yourname"
git branch -M main && git add . && git commit -m 'Initial commit'
git remote add origin https://github.com/Limour-dev/chatGPT.git
git push --set-upstream origin main --force # Creating a personal access token
git config --global credential.helper cache
git push

hello world#

---
const search = Astro.url.searchParams.get('search')! || '';
---
<h1>{search}</h1>

Enabling SSR in Your Project
编辑 chatGPT/src/pages/index.astro
npm run dev
访问 https://vscode.domain/proxy/3000/?search=hello%20world 进行测试

安装 Jupyter#

持久化镜像存储#

mkdir -p ~/datascience && cd ~/datascience
nano docker-compose.yml
sudo docker-compose up -d
sudo docker-compose logs
sudo docker cp -a jupyterR:/opt /home/limour/upload/opt
sudo docker cp -a jupyterR:/home/jovyan /home/limour/upload/home
sudo docker-compose down && sudo docker volume prune

version: '3.3'
services:
    datascience-notebook:
        ports:
            - '57002:8888'
        container_name: jupyterR
        restart: always
        image: 'jupyter/datascience-notebook:r-4.1.3'
        command: start-notebook.sh --NotebookApp.token='***'

启动镜像#

nano docker-compose.yml
sudo chmod 777 -R /home/limour/upload/
sudo docker-compose up -d
sudo docker-compose logs

version: '3.3'
services:
    datascience-notebook:
        ports:
            - '57002:8888'
        container_name: jupyterR
        restart: always
        volumes:
            - '/home/limour/upload:/home/jovyan/upload'
            - '/home/limour/upload/opt/opt:/opt'
            - '/home/limour/upload/home/jovyan:/home/jovyan'
        image: 'jupyter/datascience-notebook:r-4.1.3'
        command: start-notebook.sh --NotebookApp.token='***'

NPS 内网穿透加 NPM 面板反代

R 包镜像#

nano .Rprofile
options()$repos ## 查看使用install.packages安装时的默认镜像
options()$BioC_mirror ##查看使用bioconductor的默认镜像

options(BioC_mirror="https://mirrors.ustc.edu.cn/bioc/") ##指定镜像，这个是中国科技大学镜像
options("repos" = c(CRAN="https://mirrors.tuna.tsinghua.edu.cn/CRAN/")) ##指定install.packages安装镜像，这个是清华镜像
options(ggrepel.max.overlaps = Inf)

安装 R 内核#

conda create -n seurat -c conda-forge r-seurat=4.1.1 -y
conda activate seurat
conda install -c conda-forge r-tidyverse -y
conda install -c conda-forge r-irkernel -y
Rscript -e "IRkernel::installspec(name='seurat', displayname='r-seurat')"
conda install -c conda-forge r-devtools -y
Rscript -e "BiocManager::install('glmGamPoi')"
wget -e "https_proxy=http://172.17.0.1:8580" https://github.com/chris-mcginnis-ucsf/DoubletFinder/archive/refs/heads/master.zip -O DoubletFinder-master.zip
Rscript -e "devtools::install_local('DoubletFinder-master.zip')"

安装 python 内核#

conda create -n markdown2pptx -c conda-forge python -y
conda install -n markdown2pptx ipykernel -c conda-forge -y
conda run -n markdown2pptx python -m ipykernel install --user --name markdown2pptx
conda run -n markdown2pptx pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

安装 Golang 内核#

项目地址 gophernotes

conda create -n golang -c conda-forge go -y
conda activate golang
go env -w GO111MODULE=on
go env -w GOPROXY=https://mirrors.aliyun.com/goproxy/
go install github.com/gopherdata/[email protected] #去仓库查看最新版本号
mkdir -p ~/.local/share/jupyter/kernels/golang
cd ~/.local/share/jupyter/kernels/golang
cp "$(go env GOPATH)"/pkg/mod/github.com/gopherdata/[email protected]/kernel/* "."
chmod +w ./kernel.json
sed "s|gophernotes|$(go env GOPATH)/bin/gophernotes|" < kernel.json.in > kernel.json

安装 rstudio#

mkdir -p ~/app/rstudio && cd ~/app/rstudio && nano docker-compose.yml
sudo docker-compose up -d && sudo docker-compose logs

version: '3'
services:
  rstudio:
    image: dceoy/rstudio-server
    container_name: Rstudio
    deploy:
      resources:
        limits:
          cpus: '0.50'
          memory: 500M
        reservations:
          cpus: '0.25'
          memory: 200M
    restart: always
    ports:
      - 57022:8787
    volumes:
      - /home/gene/zl_liu/rstudio:/home/rstudio
      - /home/gene/upload:/home/rstudio/upload
    working_dir: /home/rstudio

更改 R 版本#

安装 conda

# 容器内
conda create -n r_4_1_3 -c conda-forge r-base=4.1.3 -y
conda activate r_4_1_3
whereis R
# /home/rstudio/miniconda3/envs/r_4_1_3/bin/R
# 容器外
docker exec -it Rstudio /bin/bash
chmod 777 -R  /etc/rstudio/
exit 
nano -K /etc/rstudio/rserver.conf
# Server Configuration File
rsession-which-r=/home/rstudio/miniconda3/envs/r_4_1_3/bin/R
sudo docker-compose restart

安装 seurat#

# 进入terminal，以下操作均在terminal中进行
export R_LIBS_SITE=""
# 在terminal中进入R
.libPaths('/home/rstudio/miniconda3/envs/r_4_1_3/lib/R/library')
.libPaths() 确保没有其他路径
remove.packages("vctrs")
install.packages("vctrs")
install.packages('Seurat')
remove.packages("cli")
install.packages("cli")
install.packages("tidyverse")
install.packages("plotly")
重启R session
library(tidyverse)
library(Seurat)

绘制 3D-umap#

library(plotly)
library(Seurat)
sample13 <- readRDS("~/upload/zl_liu/work/Prognosis/scRNA/sample13.rds")
sample13 <- RunUMAP(sample13, dims = 1:10, n.components = 3L)
plot.data <- FetchData(object = sample13, vars = c("UMAP_1", "UMAP_2", "UMAP_3", "seurat_clusters"))
plot.data$label <- paste(rownames(plot.data))
# Plot your data, in this example my Seurat object had 21 clusters (0-20)
plot_ly(data = plot.data, 
        x = ~UMAP_1, y = ~UMAP_2, z = ~UMAP_3, 
        color = ~seurat_clusters, 
        colors = c("lightseagreen",
                   "gray50",
                   "darkgreen",
                   "red4",
                   "red",
                   "turquoise4",
                   "black",
                   "yellow4",
                   "royalblue1",
                   "lightcyan3",
                   "peachpuff3",
                   "khaki3",
                   "gray20",
                   "orange2",
                   "royalblue4",
                   "yellow3",
                   "gray80",
                   "darkorchid1",
                   "lawngreen",
                   "plum2",
                   "darkmagenta")[1:7],
        type = "scatter3d", 
        mode = "markers", 
        marker = list(size = 5, width=2), # controls size of points
        text=~label, #This is that extra column we made earlier for which we will use for cell ID
        hoverinfo="text") #When you visualize your plotly object, hovering your mouse pointer over a point shows cell names