{"id":60836,"date":"2024-09-12T15:07:57","date_gmt":"2024-09-12T07:07:57","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=60836"},"modified":"2024-09-12T15:07:57","modified_gmt":"2024-09-12T07:07:57","slug":"%e7%bb%86%e8%83%9e%e9%80%9a%e8%ae%af%e5%88%86%e6%9e%90%e8%bf%98%e5%9c%a8%e7%94%a8cellchat%ef%bc%9f%e7%9c%9f%e7%9a%84out%e4%ba%86%ef%bc%81icellnet-%e4%bd%a0%e5%80%bc%e5%be%97%e6%8b%a5%e6%9c%89%ef%bc%81","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=60836","title":{"rendered":"\u7ec6\u80de\u901a\u8baf\u5206\u6790\u8fd8\u5728\u7528Cellchat\uff1f\u771f\u7684OUT\u4e86\uff01ICELLNET \u4f60\u503c\u5f97\u62e5\u6709\uff01"},"content":{"rendered":"\n

\u524d\u8a00<\/strong><\/strong><\/p>\n\n\n\n

\u5929\u82cd\u82cd\uff0c\u91ce\u832b\u832b\uff0c\u4e00\u5929\u4e0d\u5206\u4eab\u5c0f\u679c\u5fc3\u75d2\u75d2\uff01\u4eca\u5929\u8ddf\u7740\u5c0f\u679c\u6765\u770b\u770b\u7ec6\u80de\u901a\u8baf\u53c8\u6709\u4ec0\u4e48\u65b0\u82b1\u6837\u4e86\uff01\u81ea\u4ece\u5355\u7ec6\u80de\u7206\u706b\u4e4b\u540e\uff0c\u5728\u8fc7\u53bb\u7684\u51e0\u5e74\u4e2d\u5df2\u7ecf\u5f00\u53d1\u4e86\u597d\u591a\u79cd\u57fa\u4e8e\u914d\u4f53\u548c\u53d7\u4f53\u8868\u8fbe\u7684\u8f6c\u5f55\u7ec4\u6570\u636e\u4e2d\u63a8\u65ad\u7ec6\u80de\u95f4\u901a\u8baf\u7f51\u7edc\u7684\u5de5\u5177\u3002\u50cf\u5927\u5bb6\u8033\u719f\u80fd\u8be6\u7684cellchat\u7b49R\u5305\u3002\u5f88\u591a\u7684\u751f\u4fe1\u6587\u7ae0\u4e2d\u90fd\u7528\u5230\u4e86\u8fd9\u4e2a\u5305\uff0c\u662f\u4e0d\u662f\u5df2\u7ecf\u5ba1\u7f8e\u75b2\u52b3\u4e86\uff1f\u662f\u4e0d\u662f\u60f3\u6362\u6362\u53e3\u5473\u4e86\uff1f\u90a3\u5c31\u8ddf\u7740\u5c0f\u679c\u6765\u770b\u770b\u6cd5\u56fd\u5723\u8def\u6613\u65af\u533b\u9662\u82cf\u6885\u5229\u65af\u6559\u6388\u56e2\u961f\u7684\u4f73\u4f5c\u5427\uff01ICELLNET \u914d\u4f53-\u53d7\u4f53\u6570\u636e\u5e93\u4ece 380 \u4e2a\u5927\u5e45\u6269\u5c55\u5230 1669 \u4e2a\u751f\u7269\u5b66\u7b56\u5212\u7684\u76f8\u4e92\u4f5c\u7528\uff0c\u6574\u5408\u4e86\u53c2\u4e0e\u514d\u75ab\u4e32\u6270\u3001\u7ec6\u80de\u7c98\u9644\u548c Wnt \u901a\u8def\u7684\u91cd\u8981\u901a\u8baf\u5206\u5b50\u5bb6\u65cf\uff0c\u4f18\u5316\u4e86\u7528\u4e8e\u5355\u7ec6\u80de RNA \u6d4b\u5e8f\u6570\u636e\u5206\u6790\u7684 ICELLNET \u6846\u67b6\uff0c\u63d0\u4f9b\u4e86\u7ec6\u80de\u95f4\u901a\u8baf\u7ed3\u679c\u7684\u65b0\u53ef\u89c6\u5316\uff0c\u4ee5\u4fc3\u8fdb\u4f18\u5148\u7ea7\u6392\u5e8f\u548c\u751f\u7269\u5b66\u89e3\u91ca\u3002\u542c\u5230\u8fd9\u91cc\u5fc3\u52a8\u4e86\u5417\uff01\u90a3\u5c31\u8d76\u5feb\u8ddf\u7740\u5c0f\u679c\u7684\u6b65\u4f10\u5b66\u8d77\u6765\uff01\u5f00\u59cb\u4e4b\u524d\u8bf7\u5141\u8bb8\u5c0f\u679c\u6253\u4e2a\u5e7f\u544a\uff0c\u5e7f\u544a\u4e4b\u540e\u66f4\u7cbe\u5f69\u54e6\uff01\u7ec3\u4e86\u5341\u5e74\u751f\u4fe1\u5206\u6790\u7684\u5c0f\u679c\u5bf9\u4e8e\u751f\u4fe1\u5206\u6790\u77e5\u8bc6\u5df2\u7ecf\u5982\u9c7c\u5f97\u6c34\uff0c\u76f8\u4fe1\u6709\u5c0f\u679c\u7684\u52a0\u6301\u4e00\u5b9a\u80fd\u8ba9\u5927\u5bb6\u5b66\u4f1a\uff01\u6ca1\u6709\u65f6\u95f4\u5b66\u4e60\u4e14\u6709\u751f\u4fe1\u5206\u6790\u7684\u5c0f\u4f19\u4f34\u4eec\u5feb\u6765\u6ef4\u6ef4\u5c0f\u679c\u5662\uff01\u4ece\u5206\u6790\u5230\u53ef\u89c6\u5316\u76f4\u5230\u4f60\u6ee1\u610f\u4e3a\u6b62\uff01<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u5c0f\u679c\u5728\u8fd9\u91cc\u7ed9\u5927\u5bb6\u9001\u798f\u5229\u4e86\uff0c\u6709\u9700\u8981\u670d\u52a1\u5668\u7684\u5c0f\u4f19\u4f34\u4eec\uff0c\u6b22\u8fce\u5927\u5bb6\u8054\u7cfb\u5c0f\u679c\uff0c\u4fdd\u8bc1\u670d\u52a1\u5668\u7684\u6027\u4ef7\u6bd4\u6700\u9ad8\u54e6\uff01<\/p>\n\n\n\n

\u6559\u7a0b<\/strong><\/strong><\/p>\n\n\n\n

\u5256\u6790\u764c\u75c7\u76f8\u5173\u6210\u7ea4\u7ef4\u7ec6\u80de\u4e9a\u7fa4\u7684\u7ec6\u80de\u95f4\u901a\u8baf\u3002\u764c\u75c7\u76f8\u5173\u6210\u7ea4\u7ef4\u7ec6\u80de \uff08CAF\uff09 \u662f\u4f4d\u4e8e\u80bf\u7624\u5fae\u73af\u5883\u4e2d\u7684\u57fa\u8d28\u7ec6\u80de\uff0c\u5df2\u77e5\u53ef\u589e\u5f3a\u80bf\u7624\u8868\u578b\uff0c\u4f8b\u5982\u764c\u7ec6\u80de\u589e\u6b96\u548c\u708e\u75c7\u3002\u5148\u524d\u7684\u7814\u7a76\u8868\u660e CAFs \u8868\u578b\u5b58\u5728\u5f02\u8d28\u6027\u3002\u4ed6\u4eec\u786e\u5b9a\u4e86 CAF \u7684 4 \u4e2a\u4e9a\u7fa4\uff0c\u5305\u62ec CAF-S1 \u548c CAF-S4 \u5728\u4e09\u9634\u6027\u4e73\u817a\u764c \uff08TNBC\uff09 \u4e2d\u79ef\u7d2f\u3002CAF-S1 \u4e0e\u514d\u75ab\u6291\u5236\u5fae\u73af\u5883\u663e\u8457\u76f8\u5173\u3002<\/p>\n\n\n\n

\u5728\u672c\u6559\u7a0b\u4e2d\uff0c\u6211\u4eec\u5e0c\u671b\u4f7f\u7528 TNBC \u4e2d CAF-S1 \u548c CAF-S4 \u7684\u53ef\u7528\u8f6c\u5f55\u8c31\u6765\u7814\u7a76 CAF-S1 \u548c CAF-S4 \u4e0e\u80bf\u7624\u5fae\u73af\u5883 \uff08TME\uff09 \u5176\u4ed6\u6210\u5206\u7684\u901a\u8baf\u5dee\u5f02<\/p>\n\n\n\n

1.\u76f8\u5173R\u5305\u5b89\u88c5<\/strong><\/strong><\/p>\n\n\n\n

ICELLNET\u5305\u662f\u4e00\u4e2a\u57fa\u4e8e\u8f6c\u5f55\u7ec4\u5b66\u7684\u6846\u67b6\uff0c\u7528\u4e8e\u4ee5\u5168\u5c40\u65b9\u5f0f\u5256\u6790\u7ec6\u80de\u901a\u8baf\u3002\u5b83\u96c6\u6210\u4e86\u4e00\u4e2a\u7531\u4e13\u5bb6\u7b56\u5212\u7684\u539f\u59cb\u914d\u4f53-\u53d7\u4f53\u76f8\u4e92\u4f5c\u7528\u6570\u636e\u5e93\uff0c\u540c\u65f6\u8003\u8651\u4e86\u591a\u4e2a\u4e9a\u57fa\u8868\u8fbe\u3002\u57fa\u4e8e\u8f6c\u5f55\u7ec4\u56fe\u8c31\uff0cICELLNET\u8f6f\u4ef6\u5305\u5141\u8bb8\u8ba1\u7b97\u7ec6\u80de\u4e4b\u95f4\u7684\u901a\u4fe1\u5206\u6570\uff0c\u5e76\u63d0\u4f9b\u591a\u79cd\u53ef\u89c6\u5316\u6a21\u5f0f\uff0c\u6709\u52a9\u4e8e\u6df1\u5165\u7814\u7a76\u7ec6\u80de\u95f4\u76f8\u4e92\u4f5c\u7528\u673a\u5236\u5e76\u6269\u5c55\u751f\u7269\u5b66\u77e5\u8bc6\u3002<\/p>\n\n\n\n

#\u4e0b\u8f7d\u5e76\u52a0\u8f7d\u6240\u6709\u4f9d\u8d56\u9879\u540e\uff0c\u60a8\u53ef\u4ee5\u52a0\u8f7d\u201cicellnet\u201d\u5305\u3002<\/p>\n\n\n\n

install.packages(c(“devtools”, “jetset”, “readxl”, “psych”, “GGally”, “gplots”, “ggplot2”, “RColorBrewer”, “data.table”, “grid”, “gridExtra”, “ggthemes”, “scales”,”rlist”)) ##Installs devtools and the icellnet CRAN dependancies<\/p>\n\n\n\n

if (!requireNamespace(“BiocManager”, quietly = TRUE)) # Installs icellnet Bioconductor dependancies<\/p>\n\n\n\n

    install.packages(“BiocManager”)<\/p>\n\n\n\n

BiocManager::install(c(“BiocGenerics”, “org.Hs.eg.db”, “hgu133plus2.db”, “annotate”))<\/p>\n\n\n\n

library(devtools)<\/p>\n\n\n\n

install_github(“soumelis-lab\/ICELLNET”,ref=”master”, subdir=”icellnet”)<\/p>\n\n\n\n

2..\u52a0\u8f7d<\/strong> ICELLNET \u6570\u636e\u5e93\u5e76\u5c06\u6570\u636e\u5e93\u9650\u5236\u4e3a\u4e0d\u540c\u7684\u7ec6\u80de\u56e0\u5b50\u5bb6\u65cf<\/strong><\/strong><\/p>\n\n\n\n

library(BiocGenerics)<\/p>\n\n\n\n

library(“org.Hs.eg.db”)<\/p>\n\n\n\n

library(“hgu133plus2.db”)<\/p>\n\n\n\n

library(jetset)<\/p>\n\n\n\n

library(ggplot2)<\/p>\n\n\n\n

library(dplyr)<\/p>\n\n\n\n

library(icellnet)<\/p>\n\n\n\n

library(gridExtra)<\/p>\n\n\n\n

##\u8bfb\u53d6\u6570\u636e<\/p>\n\n\n\n

db=as.data.frame(read.csv(curl::curl(url=”https:\/\/raw.githubusercontent.com\/soumelis-lab\/ICELLNET\/master\/data\/ICELLNETdb.tsv”), sep=”\\t”,header = T, check.names=FALSE, stringsAsFactors = FALSE, na.strings = “”))<\/p>\n\n\n\n

my.selection.LR=c(“Cytokine”)<\/p>\n\n\n\n

db2 <- db[grepl(paste(my.selection.LR, collapse=”|”),db$Family),] #if you want to use all the database, do instead : db2=db<\/p>\n\n\n\n

db.name.couple=name.lr.couple(db2, type=”Subfamily”)<\/p>\n\n\n\n

head(db.name.couple)<\/p>\n\n\n\n

3. \u4ece\u4eba\u7c7b\u539f\u4ee3\u7ec6\u80de\u56fe\u8c31\u6570\u636e\u96c6\u4e2d\u52a0\u8f7d\u4f34\u4fa3\u7ec6\u80de\u7c7b\u578b<\/strong><\/strong><\/p>\n\n\n\n

#\u4ecegithub\u4e0b\u8f7dPC.data.all\u548cPC.target.all\u5bf9\u8c61\uff0c\u5e76\u5728\u4f60\u7684Rstudio\u4f1a\u8bdd\u4e2d\u6253\u5f00\u5b83\u4eec<\/p>\n\n\n\n

PC.data.all=as.data.frame(read.csv(“~\/Downloads\/PC.data.all.csv”, sep=”,”, header = T, check.names=FALSE, stringsAsFactors = FALSE, na.strings = “”))<\/p>\n\n\n\n

rownames(PC.data.all)=PC.data.all$ID<\/p>\n\n\n\n

PC.target.all=as.data.frame(read.csv(“~\/Downloads\/PC.target.all.csv”, sep=”,”,header = T, check.names=FALSE, stringsAsFactors = FALSE, na.strings = “”))<\/p>\n\n\n\n

my.selection=c(“Epith”, “Fblast_B”, “Endoth”,”Mono”, “Macroph”, “pDC”, “DC2”, “DC1”, “NK”, “Neutrop”,”CD4 T cell”,”CD8 T cell”, “Treg”,”B cell”)<\/p>\n\n\n\n

PC.target = PC.target.all[which(PC.target.all$Class%in%my.selection | PC.target.all$Class%in%my.selection),c(“ID”,”Class”,”Cell_type”)]<\/p>\n\n\n\n

PC.data = PC.data.all[,PC.target$ID]<\/p>\n\n\n\n

\u4f7f\u7528\u4eba\u7c7b\u539f\u4ee3\u7ec6\u80de\u56fe\u8c31\u6570\u636e\u96c6\u4f5c\u4e3a\u4f34\u4fa3\u7ec6\u80de\u7684\u540e\u7eed\u6b65\u9aa4\uff1a<\/p>\n\n\n\n

    \n
  • \u4f7f\u7528 hgu133plus2.db\uff08\uff09 \u51fd\u6570\u5728 AffyID \u548c\u6570\u636e\u5e93\u4e2d\u4f7f\u7528\u7684\u57fa\u56e0\u7b26\u53f7\u4e4b\u95f4\u521b\u5efa\u8f6c\u6362\u56fe\u8868\u3002<\/li>\n\n\n\n
  • \u6267\u884c gene.scaling\uff08\uff09 \u51fd\u6570\uff0c\u8fd9\u5c06 a\uff09 \u9009\u62e9\u4e0e\u6570\u636e\u5e93\u4e2d\u5305\u542b\u7684\u914d\u4f53\u548c\/\u6216\u53d7\u4f53\u76f8\u5bf9\u5e94\u7684\u57fa\u56e0 \uff08db\uff09\u3002b\uff09 \u5728 0 \u5230 10 \u7684\u6240\u6709\u6761\u4ef6\u4e0b\u5bf9\u6bcf\u4e2a\u914d\u4f53\/\u53d7\u4f53\u57fa\u56e0\u8868\u8fbe\u8fdb\u884c\u7f29\u653e\u3002\u5bf9\u4e8e\u6bcf\u4e2a\u57fa\u56e0\uff0c\u6700\u5927\u503c\u5b9a\u4e49\u4e3a\u201cn\u201d\u4e2a\u6700\u9ad8\u8868\u8fbe\u503c\u7684\u5e73\u5747\u8868\u8fbe\u91cf\u3002\u5c06\u57fa\u56e0\u8868\u8fbe\u9664\u4ee5\u6700\u5927\u57fa\u56e0\u8868\u8fbe\uff0c\u7136\u540e\u4e58\u4ee5 10\uff0c\u5c06\u6bcf\u4e2a\u57fa\u56e0\u7684\u6570\u636e\u8868\u8fbe\u77e9\u9635\u5206\u522b\u7f29\u653e\u5728 0 \u5230 10 \u4e4b\u95f4\u3002 n \u7684\u9ed8\u8ba4\u503c\u4e3a 1\u3002 \u5728\u6b64\u793a\u4f8b\u4e2d\uff0c\u5c06 n \u8bbe\u7f6e\u4e3a 18\uff0c\u4ee5\u4fbf\u5c06 5% \u6781\u503c\u8868\u8fbe\u503c\u7684\u5e73\u5747\u503c\u4f5c\u4e3a\u4f34\u4fa3\u7ec6\u80de\u7684\u6700\u5927\u503c\u3002<\/li>\n<\/ul>\n\n\n\n

    ### \u5c06\u57fa\u56e0\u7b26\u53f7\u8f6c\u6362\u4e3aID<\/p>\n\n\n\n

    PC.affy.probes = as.data.frame(PC.data[,c(1,2)])<\/p>\n\n\n\n

    PC.affy.probes$ID = rownames(PC.affy.probes) # for format purpose<\/p>\n\n\n\n

    transform = db.hgu133plus2(db2,PC.affy.probes) # creation of a new db2 database with AffyID instead of gene symbol<\/p>\n\n\n\n

    ##\u4f34\u4fa3\u7ec6\u80de\u6570\u636e\u96c6\u7684\u57fa\u56e0\u7f29\u653e<\/p>\n\n\n\n

    PC.data=gene.scaling(data = PC.data, n=18, db = transform)<\/p>\n\n\n\n

    4. <\/strong>CAF-S1 \u548c CAF-S4 \u8f6c\u5f55\u7ec4\u5b66\u8c31<\/strong><\/strong><\/p>\n\n\n\n

    \u8be5\u6570\u636e\u96c6\u63d0\u4f9b\u4e86 77 \u4e2a\u6837\u672c\u7684\u57fa\u56e0\u8868\u8fbe\u77e9\u9635\uff0c\u8fd9\u4e9b\u6837\u672c\u5bf9\u5e94\u4e8e Costa \u7b49\u4eba\u4e8e 2018 \u5e74\u751f\u6210\u7684 CAF \u4e9a\u7fa4\u8f6c\u5f55\u7ec4\u56fe\u8c31\uff0c\u7528\u4e8e\u8bc6\u522b\u4e73\u817a\u764c\u4e2d\u7684\u56db\u4e2a CAF \u4e9a\u7fa4\u3002<\/p>\n\n\n\n

    #\u4e2d\u5fc3\u7ec6\u80de\u6570\u636e\u6587\u4ef6(\u5904\u7406\u8fc7\u7684\u57fa\u56e0\u8868\u8fbe\u77e9\u9635)<\/p>\n\n\n\n

    data=as.data.frame(read.table(curl::curl(url=”https:\/\/raw.githubusercontent.com\/soumelis-lab\/ICELLNET\/master\/data_CAF\/data_CAF.txt”), sep=”\\t”, header = T))<\/p>\n\n\n\n

    rownames(data)=data$SYMBOL<\/p>\n\n\n\n

    data=dplyr::select(data, -SYMBOL)<\/p>\n\n\n\n

    CC.data= gene.scaling(data = data, n=4, db = db2)<\/p>\n\n\n\n

    #\u76ee\u6807\u4e2d\u5fc3\u7ec6\u80de\u6587\u4ef6(\u4e0d\u540c\u6837\u672c\u7684\u63cf\u8ff0)<\/p>\n\n\n\n

    CC.target = as.data.frame(read.table(curl::curl(url=”https:\/\/raw.githubusercontent.com\/soumelis-lab\/ICELLNET\/master\/data_CAF\/target_CAF.txt”),sep = “\\t”,header=T))<\/p>\n\n\n\n

    head(CC.target)<\/p>\n\n\n\n

    \u4e0e PC.data \u76f8\u540c\uff0c\u5c06\u57fa\u56e0\u8868\u8fbe\u77e9\u9635\u91cd\u65b0\u8c03\u6574\u4e3a 0 \u5230 10\uff0c\u5c06\u6240\u6709 CAF \u6837\u672c\u8003\u8651\u5230\u5c3d\u53ef\u80fd\u5927\u7684\u5206\u5e03\u3002 \u5728\u8fd9\u4e2a\u9636\u6bb5\uff0c\u6211\u4eec\u9009\u62e9\u611f\u5174\u8da3\u7684\u6837\u672c\uff0c\u8fd9\u610f\u5473\u7740\u5728\u80bf\u7624\uff08TNBC\u764c\u75c7\u7c7b\u578b\uff09\u4e2d\u53d1\u73b0\u7684CAF-S1\u6216CAF-S4\u3002\u8fd9\u76f8\u5f53\u4e8e CAF-S1 \u7684 6 \u4e2a\u6837\u54c1\u548c CAF-S4 \u7684 3 \u4e2a\u6837\u54c1\u3002<\/p>\n\n\n\n

    CC.selection.S1 = CC.target[which(CC.target$Type==”T”&CC.target$subset==”S1″&CC.target$Cancer.subtype==”TN”),”Sample.Name”] # CAF-S1 in TNBC samples<\/p>\n\n\n\n

    CC.selection.S2 = CC.target[which(CC.target$Type==”T”&CC.target$subset==”S4″&CC.target$Cancer.subtype==”TN”),”Sample.Name”] # CAF-S4 in TNBC samples<\/p>\n\n\n\n

    CC.data.selection.S1 = CC.data[,which(colnames(CC.data)%in%CC.selection.S1)]<\/p>\n\n\n\n

    CC.data.selection.S2 = CC.data[,which(colnames(CC.data)%in%CC.selection.S2)]<\/p>\n\n\n\n

    5.<\/strong> ICELLNET\u7ec6\u80de\u95f4\u901a\u8baf\u5206\u6570\u7684\u8ba1\u7b97<\/strong><\/strong><\/p>\n\n\n\n

    \u7ec6\u80de\u95f4\u901a\u8baf\u7684\u5b9a\u91cf\u5305\u62ec\u5bf9\u5177\u6709\u5df2\u77e5\u8868\u8fbe\u8c31\u7684\u4e24\u79cd\u7ec6\u80de\u7c7b\u578b\u4e4b\u95f4\u6bcf\u79cd\u914d\u4f53\/\u53d7\u4f53\u76f8\u4e92\u4f5c\u7528\u7684\u5f3a\u5ea6\u8fdb\u884c\u8bc4\u5206\u3002\u4e0d\u5bf9 L\/R \u8868\u8fbe\u5f0f\u5e94\u7528\u7b5b\u9009\u9608\u503c\u3002\u5982\u679c\u901a\u8baf\u5206\u5b50\uff08\u914d\u4f53\u6216\u53d7\u4f53\u6216\u4e24\u8005\u517c\u800c\u6709\u4e4b\uff09\u672a\u7531\u7ec6\u80de\u8868\u8fbe\uff0c\u5219\u5206\u6570\u5c06\u4e3a\u96f6\u3002\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0c\u6570\u636e\u5e93\u7684\u6240\u6709\u4ea4\u4e92\u90fd\u88ab\u89c6\u4e3a\u8ba1\u7b97\u5206\u6570\u3002\u4e5f\u53ef\u4ee5\u901a\u8fc7\u624b\u52a8\u9009\u62e9\u6570\u636e\u5e93\u4e2d\u7684\u7279\u5b9a\u5206\u5b50\u5bb6\u65cf\u6216\u8003\u8651DEG\u6765\u8ba1\u7b97\u5206\u6570\u6765\u51cf\u5c11\u76f8\u4e92\u4f5c\u7528\u7684\u6570\u91cf\uff0c\u5177\u4f53\u53d6\u51b3\u4e8e\u751f\u7269\u5b66\u95ee\u9898\u3002<\/p>\n\n\n\n

    \u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u8ba1\u7b97\u5411\u5916\u901a\u4fe1\u5206\u6570\uff08direction = \u201cout\u201d\uff09\uff0c\u8fd9\u610f\u5473\u7740\u4ece CAF \u5b50\u96c6\u5230\u4f19\u4f34\u7ec6\u80de\u7684\u901a\u4fe1\u3002 \u8fd9\u610f\u5473\u7740\u6211\u4eec\u8003\u8651CAF\u8868\u8fbe\u7684\u914d\u4f53\u548c\u4f34\u4fa3\u7ec6\u80de\u8868\u8fbe\u7684\u53d7\u4f53\u6765\u8ba1\u7b97\u7ec6\u80de\u95f4\u901a\u8baf\u5206\u6570\u3002<\/p>\n\n\n\n

    score.computation.1= icellnet.score(direction=”out”, PC.data=PC.data, CC.data= CC.data.selection.S1,  <\/p>\n\n\n\n

                                        PC.target = PC.target, PC=my.selection, CC.type = “RNAseq”,<\/p>\n\n\n\n

                                        PC.type = “Microarray”,  db = db2)<\/p>\n\n\n\n

    score1=as.data.frame(score.computation.1[[1]])<\/p>\n\n\n\n

    lr1=score.computation.1[[2]]<\/p>\n\n\n\n

    score.computation.2= icellnet.score(direction=”out”, PC.data=PC.data, CC.data= CC.data.selection.S2,  <\/p>\n\n\n\n

                                          PC.target = PC.target,PC=my.selection, CC.type = “RNAseq”,<\/p>\n\n\n\n

                                          PC.type = “Microarray”,  db = db2)<\/p>\n\n\n\n

    score2=as.data.frame(score.computation.2[[1]])<\/p>\n\n\n\n

    lr2=score.computation.2[[2]]<\/p>\n\n\n\n

    Scores=cbind(score1,score2)<\/p>\n\n\n\n

    colnames(Scores)=c(“CAF-S1″,”CAF-S4”)<\/p>\n\n\n\n

    Scores<\/p>\n\n\n\n

    score1 \u548c score2 \u5bf9\u5e94\u4e8e\u5168\u5c40\u5206\u6570\uff0c\u5373\u5404\u4e2a\u5206\u6570\u7684\u603b\u548c\u3002\u5206\u6570\u77e9\u9635 \uff08Scores\uff09 \u5bf9\u5e94\u4e8e ICELLNET \u5728\u6240\u6709\u5916\u56f4\u5c0f\u533a\u548c\u4e2d\u5fc3\u5c0f\u533a\u4e4b\u95f4\u8ba1\u7b97\u7684\u5168\u5c40\u901a\u4fe1\u5206\u6570\u7684\u6458\u8981\u3002LR1 \u548c LR2 \u5bf9\u5e94\u4e8e\u5355\u4e2a\u5206\u6570\u77e9\u9635\uff0c\u5bf9\u4e8e\u8fdb\u4e00\u6b65\u7684\u53ef\u89c6\u5316\u6b65\u9aa4\u5f88\u6709\u7528\u3002<\/p>\n\n\n\n

    6. \u4f7f\u7528\u4e0d\u540c\u7684\u53ef\u89c6\u5316\u6a21\u5f0f\u6df1\u5165\u7814\u7a76\u7ec6\u80de\u95f4\u901a\u4fe1<\/strong><\/strong><\/p>\n\n\n\n

    6.1\u7ec6\u80de\u95f4\u901a\u8baf\u7f51\u7edc\u8868\u793a<\/p>\n\n\n\n

    \u8fd9\u5141\u8bb8\u901a\u8fc7network.create()\u529f\u80fd\u4ee5\u5168\u5c40\u65b9\u5f0f\u53ef\u89c6\u5316\u7ec6\u80de\u95f4\u901a\u4fe1\u7f51\u7edc\u3002\u5728\u8fd9\u4e9b\u6709\u5411\u56fe\u4e2d\uff0c\u8282\u70b9\u8868\u793a\u50cf\u5143\u7c7b\u578b\uff0c\u8fde\u63a5\u4e24\u79cd\u50cf\u5143\u7c7b\u578b\u7684\u8fb9\u7684\u5bbd\u5ea6\u4e0e\u5b83\u4eec\u4e4b\u95f4\u901a\u4fe1\u5f3a\u5ea6\u7684\u5168\u5c40\u6d4b\u91cf\u6210\u6b63\u6bd4\uff0c\u7bad\u5934\u8868\u793a\u901a\u4fe1\u65b9\u5411\u3002<\/p>\n\n\n\n

    #\u663e\u793a\u901a\u4fe1\u7f51\u7edc<\/p>\n\n\n\n

    PC.col = c(“Epith”=”#C37B90”, “Muscle_cell”=”#c100b9″,”Fblast_B”=”#88b04b”, “Fblast”=”#88b04b”,”Endoth”=”#88b04b”,<\/p>\n\n\n\n

               “Mono”=”#ff962c”,”Macroph”=”#ff962c”,”moDC”=”#ff962c”,”DC1″=”#ff962c”,”DC2″=”#ff962c”,”pDC”=”#ff962c”,”NK”=”#ff962c”,”Neutrop”=”#ff962c”,<\/p>\n\n\n\n

               “CD4 T cell”=”#5EA9C3″,”CD8 T cell”=”#5EA9C3″,”Treg”=”#5EA9C3″,”B cell”=”#5EA9C3”)<\/p>\n\n\n\n

    network.plot1 = network.create(icn.score = Scores[1], scale = c(round(min(Scores)-1),round(max(Scores))+1), direction = “out”, PC.col)<\/p>\n\n\n\n

    network.plot2 = network.create(icn.score =Scores[2], scale = c(round(min(Scores)-1),round(max(Scores))+1), direction = “out”,PC.col)<\/p>\n\n\n\n

    gridExtra::grid.arrange(network.plot1, network.plot2, ncol=2, nrow=1)<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    6.2\u901a\u8baf\u5206\u5b50\u5206\u5e03 – \u6761\u5f62\u56fe\u8868\u793a<\/p>\n\n\n\n

    LR.family.score()\u5141\u8bb8\u5728\u5206\u5b50\u7c7b\u522b\u7684\u6c34\u5e73\u4e0a\u5256\u6790\u5168\u5c40\u5206\u6570\uff0c\u5e76\u5141\u8bb8\u8bc6\u522b\u6765\u81ea\u540c\u4e00\u5bb6\u65cf\u7684\u5171\u8868\u8fbe\u5206\u5b50\u7684\u6a21\u5f0f\u3002\u8fd9\u4e00\u5c42\u5206\u6790\u6709\u52a9\u4e8e\u5b9a\u6027\u5c42\u9762\u7684\u89e3\u91ca\u3002 \u6bcf\u4e2a\u5206\u5b50\u5bb6\u65cf\u5bf9\u901a\u4fe1\u5206\u6570\u7684\u8d21\u732e\u53ef\u4ee5\u663e\u793a\u4e3a\u70ed\u56fe\uff08\u8bbe\u7f6e plot=\u201cheatmap\u201d\u65f6\uff09\u6216\u6761\u5f62\u56fe\uff08\u8bbe\u7f6e plot=\u201cbarplot\u201d\u65f6\uff09\u3002<\/p>\n\n\n\n

    ##\u6807\u7b7e\u548c\u8303\u56f4\u5b9a\u4e49<\/p>\n\n\n\n

    my.family=c(“type 1”, “type 2”, “IL1 family”, “IL17 family”, “TNF”,”TGF”,”RTK”)<\/p>\n\n\n\n

    family.col = c( “type 1″=  “#A8CF90”, “type 2″= “#676766”, “IL1 family”= “#1D1D18” ,<\/p>\n\n\n\n

                “IL17 family” =”#66ABDF”, “TNF” =”#12A039″, “TGF” = “#156399”, “RTK”=”#AECBE3”, “other” = “#908F90″,”NA”=”#908F90”)<\/p>\n\n\n\n

    ymax=round(max(Scores))+1 #to define the y axis range of the barplot<\/p>\n\n\n\n

    # to get family contribution as dataframe<\/p>\n\n\n\n

    LR.family.score(lr=lr1, family=my.family, db.couple=db.name.couple, plot= NULL)<\/p>\n\n\n\n

    # \u6761\u5f62\u56fe<\/p>\n\n\n\n

    contrib.family1= LR.family.score(lr=lr1, family=my.family, db.couple=db.name.couple, plot=”barplot”, ymax=ymax, family.col=family.col)<\/p>\n\n\n\n

    contrib.family2= LR.family.score(lr=lr2, family=my.family, db.couple=db.name.couple, plot=”barplot”, ymax=ymax, family.col=family.col)<\/p>\n\n\n\n

    gridExtra::grid.arrange(contrib.family1, contrib.family2, ncol=2, nrow=1)<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    6.3\u901a\u4fe1\u5206\u6570\u5206\u5e03 – \u6c14\u6ce1\u56fe\u8868\u793a<\/p>\n\n\n\n

    lr_ind=cbind(lr1[,”Fblast_B”],lr2[,”Fblast_B”])<\/p>\n\n\n\n

    colnames(lr_ind)=c(“S1_Fblast”, “S4_Fblast”)<\/p>\n\n\n\n

    balloon=icellnet::LR.balloon.plot(lr = lr_ind, sort.by=”var”, thresh = 25, topn=15, db.name.couple=db.name.couple, title=”Fblast”, family.col=family.col)<\/p>\n\n\n\n

    balloon<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    6.4 P\u503c\u8ba1\u7b97\u4ee5\u6bd4\u8f83\u901a\u4fe1\u5206\u6570<\/p>\n\n\n\n

    \u53ef\u4ee5\u8ba1\u7b97\u4e24\u79cd\u7c7b\u578b\u7684p\u503c\uff08\u51fd\u6570\uff09\uff0c\u4ee5\u6bd4\u8f83\u4ece\u540c\u4e00\u4e2d\u5fc3\u7ec6\u80de\u83b7\u5f97\u7684\u901a\u4fe1\u5206\u6570\u4e0e\u4e0d\u540c\u7684\u4f19\u4f34\u7ec6\u80de\uff08between=\u201ccells\u201d\uff09\uff0c\u6216\u6bd4\u8f83\u4ece\u5bf9\u5e94\u4e8e\u4e0d\u540c\u751f\u7269\u6761\u4ef6\u7684\u4e24\u4e2a\u4e0d\u540c\u4e2d\u5fc3\u7ec6\u80de\u83b7\u5f97\u7684\u901a\u4fe1\u5206\u6570\u4e0e\u540c\u4e00\u4f19\u4f34\u7ec6\u80de\uff08between=\u201cconditions\u201d\uff09\u3002\u5982\u679c between=\u201ccells\u201d\uff0c\u5219\u8003\u8651\u4e2d\u5fc3\u7ec6\u80de\u914d\u4f53\u7684\u5e73\u5747\u8868\u8fbe\u6765\u8ba1\u7b97\u901a\u8baf\u5206\u6570\uff0c\u5e76\u4e14\u6bcf\u4e2a\u914d\u4f53\u5206\u522b\u590d\u5236\u4f34\u4fa3\u7ec6\u80de\u7684\u53d7\u4f53\u8868\u8fbe\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u5bf9\u4e8e\u4e00\u4e2a\u4f34\u4fa3\u7ec6\u80de\uff0c\u6211\u4eec\u83b7\u5f97\u4e86 n \u4e2a\u901a\u4fe1\u5206\u6570\u7684\u5206\u5e03\uff0c\u5373\u8be5\u7279\u5b9a\u7ec6\u80de\u7c7b\u578b\u590d\u5236\u7684\u4f34\u4fa3\u7ec6\u80de\u6570\u91cf\u3002\u5982\u679c between=\u201cconditions\u201d\uff0c\u5219\u8ba1\u7b97\u901a\u4fe1\u5206\u6570\u65f6\u4f1a\u8003\u8651\u4e2d\u5fc3\u7ec6\u80de\u7684\u6bcf\u6b21\u91cd\u590d\uff0c\u4ee5\u53ca\u4f34\u4fa3\u7ec6\u80de\u7684\u5e73\u5747\u57fa\u56e0\u8868\u8fbe\u3002\u6211\u4eec\u83b7\u5f97\u4e86 n \u4e2a\u901a\u4fe1\u5206\u6570\u7684\u5206\u5e03\uff0cn \u4e2a\u5355\u4f4d\u662f\u5728\u4e00\u4e2a\u751f\u7269\u6761\u4ef6\u4e0b\u4e2d\u5fc3\u7ec6\u80de\u91cd\u590d\u7684\u6570\u91cf\u3002\u7136\u540e\uff0c\u6267\u884c Wilcoxon \u7edf\u8ba1\u68c0\u9a8c\u4ee5\u6bd4\u8f83\u901a\u4fe1\u5206\u6570\u5206\u5e03\u3002<\/p>\n\n\n\n

    # \u4eceCAF-S1\u548c\u4e0d\u540c\u7684\u4f34\u4fa3\u7ec6\u80de\u83b7\u5f97\u7684\u901a\u4fe1\u5206\u6570\u7684\u6bd4\u8f83<\/p>\n\n\n\n

    pvalue1=icellnet.score.pvalue(direction=”out”, PC.data=PC.data, CC.data= CC.data.selection.S1,<\/p>\n\n\n\n

                                  PC.target = PC.target,PC=my.selection, CC.type = “RNAseq”,PC.type = “Microarray”,<\/p>\n\n\n\n

                                  db = db2,  between=”cells”, method=”BH”)[[1]]<\/p>\n\n\n\n

    pvalue.plot1=pvalue.plot(pvalue1, PC=my.selection)<\/p>\n\n\n\n

    pvalue.plot1<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    \u5c0f\u7ed3<\/strong><\/strong><\/p>\n\n\n\n

    ICELLNET\u662f\u4e00\u79cd\u901a\u7528\u65b9\u6cd5\uff0c\u53ef\u5e94\u7528\u4e8e\u5e7f\u6cdb\u7684\u8f6c\u5f55\u7ec4\u5b66\u6570\u636e\uff0c\u5305\u62ec\u6279\u91cfRNAseq\u3001scRNAseq\u548c\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u5b66\u3002ICELLNET\u914d\u4f53-\u53d7\u4f53\u6570\u636e\u5e93\u7684\u91cd\u5927\u5bcc\u96c6\u5c06\u8986\u76d6\u66f4\u591a\u7684\u751f\u7269\u5b66\u9886\u57df\uff0c\u8fd9\u5c06\u6269\u5c55\u5176\u6027\u80fd\u5e76\u62d3\u5bbd\u5176\u5e94\u7528\u9886\u57df\u3002\u65b0\u7684\u56fe\u5f62\u53ef\u89c6\u5316\u5bf9\u4e8e\u4fc3\u8fdb\u7ed3\u679c\u7684\u4f18\u5148\u6392\u5e8f\u548c\u89e3\u91ca\u81f3\u5173\u91cd\u8981\uff0c\u5e76\u5c06\u63d0\u4f9b\u65b0\u7684\u751f\u7269\u5b66\u89c1\u89e3\u3002\u6700\u540e\u5c0f\u679c\u7ed9\u5927\u5bb6\u4ecb\u7ecd\u4e00\u4e2a\u4e91\u5de5\u5177\uff01\u540c\u5b66\u4eec\u5982\u679c\u89c9\u5f97\u81ea\u5df1\u7684\u4ee3\u7801\u6c34\u5e73\u4e00\u822c\uff0c\u5bf9\u4e8e\u5f88\u591a\u7684\u53c2\u6570\u4e0d\u77e5\u9053\u600e\u4e48\u6539\uff0c\u53ef\u4ee5\u4f53\u9a8c\u4e00\u4e0b\u6211\u4eec\u7684\u4e91\u751f\u4fe1\u5c0f\u5de5\u5177\uff0c\u53ea\u9700\u8f93\u5165\u6570\u636e\uff0c\u5373\u53ef\u8f7b\u677e\u751f\u6210\u6240\u9700\u56fe\u8868\uff0c\u5b57\u4f53\u5927\u5c0f\u3001\u6807\u9898\u7b49\u4e5f\u53ef\u4e00\u952e\u66f4\u6539\u3002\u611f\u5174\u8da3\u7684\u5c0f\u4f19\u4f34\u53bb\u4e91\u751f\u4fe1\uff08http:\/\/www.biocloudservice.com\/home.html\uff09\u4f53\u9a8c\u4e00\u4e0b\u5427\uff01<\/p>\n\n\n\n

    \"\"<\/figure>\n","protected":false},"excerpt":{"rendered":"

    \u524d\u8a00 \u5929\u82cd\u82cd\uff0c\u91ce\u832b\u832b\uff0c\u4e00\u5929\u4e0d\u5206\u4eab\u5c0f\u679c\u5fc3\u75d2\u75d2\uff01\u4eca\u5929\u8ddf\u7740\u5c0f\u679c\u6765\u770b\u770b\u7ec6\u80de\u901a\u8baf\u53c8\u6709\u4ec0\u4e48\u65b0\u82b1\u6837\u4e86\uff01\u81ea\u4ece\u5355\u7ec6\u80de\u7206\u706b\u4e4b\u540e\uff0c\u5728 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":""},"categories":[1],"tags":[],"jetpack_featured_media_url":"","_links":{"self":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/60836"}],"collection":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=60836"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/60836\/revisions"}],"predecessor-version":[{"id":60844,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/60836\/revisions\/60844"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=60836"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=60836"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=60836"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}