https://mp.weixin.qq.com/s/ZsUQogkqcPXkaNDIV8GhWg

安装包

using函数是我写在 $HOME/.Rprofile 中的函数，因此每次打开R就能使用。

using 的功能是一次加载多个包，并且使用了 suppressPackageStartupMessages 函数，因此不会显示加载包过程中的信息。

使用pak管理R包，可以从Bioconductor、CRAN、Github、本地、URL安装R包，解决了R包安装需要多个不同R包去安装的问题。

因为github连接不稳定，所以想了其他办法把CellChat包从github镜像到jihulab.com，再从jihulab.com克隆下来，然后用 pak::local_install 安装，如果网络可以，可以直接用 pak::pkg_install("sqjin/CellChat") 安装

install.packages("pak")
using(pak)
pkgs <- c("BiocNeighbors", "ComplexHeatmap", "circlize", "NMF")
pak::pkg_install(pkgs, upgrade = FALSE, dependencies = TRUE)
pak::pkg_install("sqjin/CellChat")
git clone https://jihulab.com/BioQuest/cellchat.git
pak::local_install("cellchat")
# pak::local_install("CellChat-master", dependencies = TRUE)

Python环境

mamba create -n SC && mamba activate SC
mamba install -y -c conda-forge python=3.10 notebook ipywidgets pandas numpy seaborn matplotlib ipykernel openpyxl pyarrow scanpy python-igraph leidenalg pytables jaxlib leidenalg
which python
# /opt/homebrew/Caskroom/mambaforge/base/envs/SC/bin/python

每个分组的细胞通讯网络流程脚本

第一次使用CellChat，好像这个处理过程是固定的，但是可视化是个性化的，又因为处理过程需要消耗些时间，所以写成脚本,可同时在后台把所有分组处理好。

各个参数的意义

• w: 工作目录，默认为当前目录
• i: 输出文件的标识

根据i可以得到，normalise过的基因表达数据，行基因，列细胞；Arrow格式；第一列是为index，细胞barcode;

细胞类型信息；CSV格式，第一列是Cell，细胞barcode，第二列为CellType，细胞的类型

• s: 物种，可以选human和mouse，默认为human
• n: 线程数默认8
• y: python的路径

使用方法

nohup Rscript CellChat_1.R -i C -y /opt/homebrew/Caskroom/mambaforge/base/envs/SC/bin/python &> C.log &
nohup Rscript CellChat_1.R -i T -y /opt/homebrew/Caskroom/mambaforge/base/envs/SC/bin/python &> T.log &

运行后的文件目录

├── CellChat_1.R
├── C.arrow
├── C.csv
├── T.arrow
├── T.csv
├── cc_C.rds
├── cc_T.rds
├── estimationNumCluster_C_functional_dataset_single.pdf
├── estimationNumCluster_T_structural_dataset_single.pdf
├── estimationNumCluster_C_functional_dataset_single.pdf
└── estimationNumCluster_T_structural_dataset_single.pdf

Rscript

using(optparse, data.table, tidyverse, CellChat, arrow, reticulate)
option_list <- list(
    make_option(c("-w", "--workdir"), type = "character", action = "store", default = "./", help = "工作目录路径"),
    make_option(c("-i", "--id"), type = "character", action = "store", default = FALSE, help = "文件标识"),
    make_option(c("-s", "--species"), type = "character", action = "store", default = "human", help = "物种"),
    make_option(c("-n", "--n_jobs"), type = "integer", action = "store", default = 8, help = "线程"),
    make_option(c("-y", "--python"), type = "character", action = "store", default = FALSE, help = "Python路径")
opt <- parse_args(OptionParser(
    option_list = option_list, add_help_option = TRUE,
    usage = "Usage: %prog [options] \nDescription: CellChat pre!"
setwd(opt$workdir)
reticulate::use_python(opt$python)
# ==============================================================================
# 1.创建CellChat对象
# ==============================================================================
mt <- arrow::read_ipc_file(str_glue("{opt$id}.arrow")) %>%
    column_to_rownames("index") %>%
    t() %>%
    as.matrix()
df <- data.table::fread(str_glue("{opt$id}.csv")) %>%
    column_to_rownames("Cell")
cc <- createCellChat(mt, meta = df, group.by = "CellType")
cc@DB <- switch(opt$species,
    human = CellChat::CellChatDB.human,
    mouse = CellChat::CellChatDB.mouse
# ==============================================================================
# 2.预处理表达数据 + 细胞通讯网络
# ==============================================================================
future::plan("multisession", workers = opt$n_jobs)
cc <- subsetData(cc)
cc <- identifyOverExpressedGenes(cc)
cc <- identifyOverExpressedInteractions(cc)
cc <- projectData(cc, adjMatrix = switch(opt$species,
    human = PPI.human,
    mouse = PPI.mouse
cc <- computeCommunProb(cc, raw.use = FALSE, population.size = TRUE)
cc <- filterCommunication(cc, min.cells = 10)
cc <- computeCommunProbPathway(cc)
cc <- aggregateNet(cc)
cc <- netAnalysis_computeCentrality(cc, slot.name = "netP")
cc <- computeNetSimilarity(cc, type = "functional")
cc <- netEmbedding(cc, type = "functional")
cc <- netClustering(cc, fig.id = opt$id, type = "functional", do.parallel = FALSE)
cc <- computeNetSimilarity(cc, type = "structural")
cc <- netEmbedding(cc, type = "structural")
cc <- netClustering(cc, fig.id = opt$id, type = "structural", do.parallel = FALSE)
saveRDS(cc, file = str_glue("cc_{opt$id}.rds"))

查看帮助

Rscript CellChat_1.R -h

Usage: CellChat_1.R [options] 
Description: CellChat pre!
Options:
        -w WORKDIR, --workdir=WORKDIR
                工作目录路径
        -i ID, --id=ID
        -s SPECIES, --species=SPECIES
        -n N_JOBS, --n_jobs=N_JOBS
        -y PYTHON, --python=PYTHON