{"id":61543,"date":"2024-10-30T15:35:27","date_gmt":"2024-10-30T07:35:27","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61543"},"modified":"2024-10-30T15:35:27","modified_gmt":"2024-10-30T07:35:27","slug":"%e5%8d%95%e7%bb%86%e8%83%9e%e7%b1%bb%e5%9e%8b%e9%89%b4%e5%ae%9a%e5%92%8c%e8%a7%a3%e9%87%8a%e7%9a%84%e5%87%86%e7%a1%ae%e6%80%a7%e6%ac%a0%e4%bd%b3%ef%bc%9fcascc%e8%87%aa%e9%80%82%e5%ba%94%e5%90%b8","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61543","title":{"rendered":"\u5355\u7ec6\u80de\u7c7b\u578b\u9274\u5b9a\u548c\u89e3\u91ca\u7684\u51c6\u786e\u6027\u6b20\u4f73\uff1fCASCC\u81ea\u9002\u5e94\u5438\u5f15\u5b50\u7b97\u6cd5\u7ed9\u4f60\u63d0\u4f9b\u989d\u5916\u89c6\u89d2\uff01"},"content":{"rendered":"\n

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

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

\u5c0f\u679c\u89c1\u9762\u4f1a\u53c8\u8981\u5f00\u59cb\u4e86\uff0c\u867d\u7136\u5c0f\u679c\u5b9e\u9a8c\u5f88\u5fd9\uff0c\u4f46\u662f\u770b\u5230\u597d\u7528\u7684\u5355\u7ec6\u80de\u805a\u7c7bR\u5305\u8fd8\u662f\u5fcd\u4e0d\u4f4f\u7b2c\u4e00\u65f6\u95f4\u5411\u5927\u5bb6\u5206\u4eab\uff01\u60f3\u5fc5\u5927\u591a\u6570\u5c0f\u4f19\u4f34\u4eec\u7684\u7ec6\u80de\u7c7b\u578b\u6807\u8bb0\u7269\u7531\u805a\u7c7b\u540e\u8ba1\u7b97\u7684\u5dee\u5f02\u57fa\u56e0\u6765\u9274\u5b9a\u7684\u5427\uff01\u662f\u4e0d\u662f\u6709\u65f6\u5019\u5355\u7ec6\u80de\u7c7b\u578b\u9274\u5b9a\u548c\u89e3\u91ca\u7684\u51c6\u786e\u6027\u6b20\u4f73\uff1f\u4e0d\u59a8\u8ddf\u7740\u5c0f\u679c\u6765\u770b\u770bCASCC\u8fd9\u4e2a\u5171\u8868\u8fbe\u8f85\u52a9\u5355\u7ec6\u80de\u805a\u7c7b\u65b9\u6cd5\u5427\u3002CASCC\u7684\u4e00\u4e2a\u5173\u952e\u7279\u70b9\u662f\u4f7f\u7528\u4e86\u4e00\u79cd\u65b0\u7684\u81ea\u9002\u5e94\u5438\u5f15\u5b50\u7b97\u6cd5\uff0c\u8be5\u7b97\u6cd5\u6709\u52a9\u4e8e\u6b63\u786e\u7684\u7279\u5f81\u9009\u62e9\u3001\u805a\u7c7b\u548c\u7ec6\u80de\u7c7b\u578b\u9274\u5b9a\uff0c\u4ece\u800c\u4e0e\u73b0\u6709\u65b9\u6cd5\u76f8\u6bd4\u63d0\u9ad8\u4e86\u751f\u7269\u5b66\u51c6\u786e\u6027\u3002CASCC\u4f18\u4e8e\u5176\u4ed6\u65b9\u6cd5\uff0c\u8fd9\u5df2\u7ecf\u591a\u4e2a\u8bc4\u4f30\u6307\u6807\u5f97\u5230\u8bc1\u660e\u5566\uff01\u6240\u4ee5\u5c0f\u4f19\u4f34\u4eec\u653e\u5fc3\u4f7f\u7528\u54e6\uff01\u6ca1\u6709\u65f6\u95f4\u5b66\u4e60\u7684\u5c0f\u4f19\u4f34\u4eec\u4e5f\u4e0d\u8981\u7740\u6025\u54e6\uff01\u6709\u9700\u8981\u751f\u4fe1\u5206\u6790\u7684\u5c0f\u4f19\u4f34\u4eec\u4e5f\u53ef\u4ee5\u627e\u5c0f\u679c\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\u4ece\u5206\u6790\u5230\u53ef\u89c6\u5316\u76f4\u5230\u4f60\u6ee1\u610f\u4e3a\u6b62\uff01<\/p>\n\n\n\n

\u5355\u7ec6\u80de\u6570\u636e\u5904\u7406\u8d77\u6765\u5360\u7528\u5185\u5b58\u5b9e\u5728\u592a\u5927\u4e86\uff0c\u653e\u8fc7\u81ea\u5df1\u7684\u7535\u8111\u5427\uff01\u5c0f\u679c\u5728\u8fd9\u91cc\u7ed9\u5927\u5bb6\u9001\u4e0a\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

\u5b9e\u6218\u6559\u7a0b<\/strong><\/strong><\/p>\n\n\n\n

1.<\/strong>\u5b89\u88c5CASCC<\/strong><\/strong><\/p>\n\n\n\n

1.1\u4f7f\u7528anaconda\u5b89\u88c5R\u548cCASCC<\/p>\n\n\n\n

conda create -n CASCC_env<\/p>\n\n\n\n

source activate CASCC_env<\/p>\n\n\n\n

conda install conda-forge::r-fs # devtools\u662f\u5fc5\u5907\u7684<\/p>\n\n\n\n

conda install conda-forge::r-devtools<\/p>\n\n\n\n

R<\/p>\n\n\n\n

.libPaths(“.\/anaconda3\/envs\/CASCC_env\/lib\/R\/library”) # \u8bbe\u7f6econda R\u5e93\u8def\u5f84<\/p>\n\n\n\n

install.packages(“rlang”,version=”1.1.0″) # \u4f9d\u8d56\u5305:\u66f4\u65b0rlang\u5305<\/p>\n\n\n\n

remotes::install_version(“Seurat”, “4.3.0”) # \u4f9d\u8d56\u5305<\/p>\n\n\n\n

devtools::install_github(“weiyi-bitw\/cafr”) # \u4f9d\u8d56\u5305<\/p>\n\n\n\n

devtools::install_github(“LingyiC\/CASCC”)<\/p>\n\n\n\n

CASCC::checkDependencies()<\/p>\n\n\n\n

1.2\u9009\u98792:\u5728R\u4e2d\u5b89\u88c5<\/p>\n\n\n\n

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

remotes::install_version(“Seurat”, “4.3.0”) # dependency<\/p>\n\n\n\n

install_github(“weiyi-bitw\/cafr”) # dependency<\/p>\n\n\n\n

install_github(“LingyiC\/CASCC”)<\/p>\n\n\n\n

CASCC::checkDependencies()<\/p>\n\n\n\n

2. CASCC\u4f7f\u7528\u6559\u7a0b<\/p>\n\n\n\n

\u5728\u672c\u6559\u7a0b\u4e2d\uff0c\u6211\u4eec\u5c06\u5206\u6790\u6765\u81ea(Peng et al. 2019)\u7814\u7a76\u7684\u80f0\u817a\u5bfc\u7ba1\u817a\u764c(PDAC)\u60a3\u8005\u7684\u793a\u4f8b\u6570\u636e\u96c6\u3002\u8fd9\u4e2a\u4f8b\u5b50\u5305\u62ec8\u79cd\u4e0d\u540c\u7684\u7ec6\u80de\u7c7b\u578b\u3002<\/p>\n\n\n\n

2.1\u5feb\u901f\u5165<\/p>\n\n\n\n

2.1.1\u7528\u4e00\u884c\u4ee3\u7801\u8fd0\u884cCASCC<\/p>\n\n\n\n

\u8fd9\u91cc\u6211\u4eec\u4f7f\u7528main\u51fd\u6570run.CASCC\u6765\u8fd0\u884cCASCC\u3002\u9010\u6b65\u8fd0\u884cCASCC\u8bf7\u53c2\u89c1\u201c\u9010\u6b65\u8fd0\u884cCASCC\u201d\u4e00\u8282\u3002<\/p>\n\n\n\n

rm(list = ls()); gc()<\/p>\n\n\n\n

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

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

set.seed(1)<\/p>\n\n\n\n

## \u52a0\u8f7d\u793a\u4f8b\u6570\u636e<\/p>\n\n\n\n

data(“Data_PDAC_peng_2k”)<\/p>\n\n\n\n

dim(Data_PDAC_peng_2k) # 400 cells<\/p>\n\n\n\n

## \u8fd0\u884cCASCC<\/p>\n\n\n\n

CASCC::checkDependencies()<\/p>\n\n\n\n

##inputDataType ‘ data ‘\u88ab\u89c4\u8303\u5316\u548c\u5bf9\u6570\u8f6c\u6362\u3002<\/p>\n\n\n\n

res <- CASCC::run.CASCC(Data_PDAC_peng_2k)<\/p>\n\n\n\n

labels <- res$mainType.output$clusteringResults<\/p>\n\n\n\n

2.1.2UMAP\u53ef\u89c6\u5316<\/p>\n\n\n\n

## \u7ed8\u56fe<\/p>\n\n\n\n

adata <- res$res.predictK$adata # UMAP<\/p>\n\n\n\n

labels <- res$mainType.output$clusteringResults<\/p>\n\n\n\n

 # \u805a\u7c7b\u8f93\u51fa:\u5177\u6709\u5438\u5f15\u5b50\u4e2d\u5fc3\u7684k\u5747\u503c<\/p>\n\n\n\n

Idents(adata) <- labels<\/p>\n\n\n\n

DimPlot(adata)<\/p>\n\n\n\n

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

2.1.3\u5438\u5f15\u5b50\u5171\u8868\u8fbe\u5f0f\u8f93\u51fa<\/p>\n\n\n\n

\u5206\u6790\u786e\u5b9a\u4e86\u516b\u4e2a\u4e0d\u540c\u96c6\u7fa4\u7684\u5b58\u5728\uff0c\u6bcf\u4e2a\u96c6\u7fa4\u5bf9\u5e94\u4e00\u4e2a\u7279\u5b9a\u7684\u5438\u5f15\u5b50\uff0c\u5982\u4e0b\u8868\u6240\u793a\u3002\u6211\u4eec\u5c55\u793a\u4e86\u6bcf\u4e2a\u5438\u5f15\u5b50\u7684\u524d10\u4e2a\u57fa\u56e0\uff0c\u5b83\u4eec\u4e0e\u5df2\u77e5\u7684\u7ec6\u80de\u7c7b\u578b\u6807\u8bb0\u5bc6\u5207\u76f8\u5173\u3002\u4ece\u5de6\u5230\u53f3\u4f9d\u6b21\u4e3a\u5de8\u566c\u7ec6\u80de\u30011\u578b\u5bfc\u7ba1\u7ec6\u80de\u3001B\u7ec6\u80de\u3001\u5185\u76ae\u7ec6\u80de\u30012\u578b\u5bfc\u7ba1\u7ec6\u80de\u3001T\u7ec6\u80de\u3001\u661f\u72b6\u7ec6\u80de\u548c\u6210\u7ea4\u7ef4\u7ec6\u80de\u3002<\/p>\n\n\n\n

## \u7ed3\u679c\u5217\u8868<\/p>\n\n\n\n

attr.raw <- res$fs.res$attr.raw<\/p>\n\n\n\n

# \u539f\u59cb\u7684\u5438\u5f15\u5668\u626b\u63cf\u7ed3\u679c\uff0c\u5982\u679c\u91cd\u65b0\u8fd0\u884cCASCC\u53ef\u4ee5\u4f7f\u7528\u3002<\/p>\n\n\n\n

# res$fs.res$attrs # \u5220\u9664\u76f8\u540c\u7684\u5438\u5f15\u5b50<\/p>\n\n\n\n

finalAttrs <- res$fs.res$finalAttrs # \u8fc7\u6ee4\u5438\u5f15\u5b50 <\/p>\n\n\n\n

## \u5438\u5f15\u5b50\u7528\u4f5c\u805a\u7c7b\u4e2d\u5fc3<\/p>\n\n\n\n

attrs <- res$mainType.output$attrs.center<\/p>\n\n\n\n

df <- as.data.frame(do.call(cbind, lapply(attrs, function(x) {data.frame(“gene” = names(x))})))<\/p>\n\n\n\n

titles <- c()<\/p>\n\n\n\n

for (attr.ID in paste0(“attr_”, unlist(lapply(attrs, function(x){names(head(x)[1])})))) {<\/p>\n\n\n\n

  titles <- c(titles, c(attr.ID))<\/p>\n\n\n\n

}<\/p>\n\n\n\n

colnames(df) <- titles<\/p>\n\n\n\n

df[1:10, ] # \u6bcf\u4e2a\u5438\u5f15\u5b50\u7684\u524d10\u4e2a\u57fa\u56e0(\u884c)(\u5217)<\/p>\n\n\n\n

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

2.1.4 UMAP\uff0c\u7531\u5438\u5f15\u5b50\u6807\u8bb0<\/p>\n\n\n\n

\u5c06\u786e\u5b9a\u76848\u4e2a\u5438\u5f15\u5b50\u7528\u4f5c\u6bcf\u4e2a\u4e9a\u7c07\u7684\u4e2d\u5fc3\u3002<\/p>\n\n\n\n

Idents(adata) <- res$mainType.output$clusteringResults<\/p>\n\n\n\n

nClust <- length(unique(Idents(adata)))<\/p>\n\n\n\n

mycolor <- grDevices::colorRampPalette(RColorBrewer::brewer.pal(n = 8, name = “Dark2”))(nClust)<\/p>\n\n\n\n

attractorDimplot(adata, attr.list = res$mainType.output$attrs.center, col = mycolor)<\/p>\n\n\n\n

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

2.1.5 Attractor plot<\/p>\n\n\n\n

\u6bcf\u4e2a\u5438\u5f15\u5b50\u4e2d\u524d5\u4e2a\u57fa\u56e0\u7684\u5e73\u5747\u8868\u8fbe\u91cf\u3002<\/p>\n\n\n\n

attractorPlot(adata, attrs[1:4], n.col = 2)<\/p>\n\n\n\n

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

2.1.6\u5bfb\u627e\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0<\/p>\n\n\n\n

\u6211\u4eec\u53d1\u73b0\u4e9a\u7fa4\u4e4b\u95f4\u5b58\u5728\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0(DEGs)\u3002<\/p>\n\n\n\n

DEGs <- Seurat::FindAllMarkers(adata, only.pos = T)<\/p>\n\n\n\n

head(DEGs<\/p>\n\n\n\n

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

2.2\u5feb\u67e5\u627e\u5171\u8868\u8fbe\u7279\u5f81\u57fa\u56e0<\/p>\n\n\n\n

\u6211\u4eec\u63d0\u4f9b\u4e86\u4e00\u79cd\u81ea\u9002\u5e94\u7248\u672c\u7684\u5438\u5f15\u5b50\u7b97\u6cd5\uff0c\u57fa\u4e8e\u6240\u63d0\u4f9b\u7684\u79cd\u5b50\u57fa\u56e0\u6765\u5bfb\u627e\u6536\u655b\u7684\u5438\u5f15\u5b50\u3002\u5982\u679c\u7528\u6237\u53ea\u5bf9\u5171\u540c\u8868\u8fbe\u5f0f\u5438\u5f15\u5668\u611f\u5174\u8da3\uff0c\u5219\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u67e5\u627e\u5171\u540c\u8868\u8fbe\u5f0f\uff0c\u800c\u65e0\u9700\u8fd0\u884c\u6574\u4e2aCASCC\u7ba1\u9053\u3002<\/p>\n\n\n\n

library(“CASCC”)<\/p>\n\n\n\n

library(“cafr”)<\/p>\n\n\n\n

data(“Data_PDAC_peng_2k”)<\/p>\n\n\n\n

# \u627e\u5230AIF1\u79cd\u5b50\u5438\u5f15\u5b50\u3002AIF1\u57fa\u56e0\u662f\u5de8\u566c\u7ec6\u80de\u7684\u6807\u5fd7\u7269\u3002<\/p>\n\n\n\n

attr.res <- findAttractor.adaptive(Data_PDAC_peng_2k, “AIF1”)<\/p>\n\n\n\n

# \u5171\u8868\u8fbe\u7684\u5438\u5f15\u5b50<\/p>\n\n\n\n

head(names(attr.res$attractor.final))   <\/p>\n\n\n\n

#> [1] “FCER1G” “AIF1”   “TYROBP” “IGSF6” “CD163” “C1QA”<\/p>\n\n\n\n

2.3\u9010\u6b65\u8fd0\u884cCASCC<\/p>\n\n\n\n

\u5728\u672c\u8282\u4e2d\uff0c\u6211\u4eec\u5c06\u9010\u6b65\u5c55\u793a\u5982\u4f55\u8fd0\u884cCASCC\u3002<\/p>\n\n\n\n

rm(list = ls()); gc()<\/p>\n\n\n\n

library(“CASCC”)<\/p>\n\n\n\n

data(“Data_PDAC_peng_2k”)<\/p>\n\n\n\n

# 1-3\u6b65<\/p>\n\n\n\n

fs.res <- CASCC.featureSelection(Data_PDAC_peng_2k, inputDataType = “well-normalized”, attr.raw = NULL, exponent.max = 10, exponent.min = 2,<\/p>\n\n\n\n

                                 generalSeedList = NULL, mc.cores = 1, topDEGs = 10, topAttr = 50,<\/p>\n\n\n\n

                                 removeMT.RP.ERCC = TRUE, removeNonProtein = FALSE,<\/p>\n\n\n\n

                                 topN.DEG.as.seed = 1,<\/p>\n\n\n\n

                                 overlapN = 10)<\/p>\n\n\n\n

adata  <- fs.res$adata;<\/p>\n\n\n\n

SeuratVersionCheck = as.numeric(strsplit(as.character(packageVersion(“Seurat”)), “\\\\.”)[[1]][1])<\/p>\n\n\n\n

if (SeuratVersionCheck == 4) {<\/p>\n\n\n\n

  data <- adata[[“RNA”]]@data<\/p>\n\n\n\n

}else if (SeuratVersionCheck == 5) {<\/p>\n\n\n\n

  data <- adata[[“RNA”]]$data<\/p>\n\n\n\n

}<\/p>\n\n\n\n

data <- as.matrix(data)<\/p>\n\n\n\n

#\u7b2c\u56db\u6b65<\/p>\n\n\n\n

res.predictK <- predictClusterK(data, fs.res, method = “ward.D2”, index = “silhouette”, min.nc_fix = FALSE)<\/p>\n\n\n\n

# \u7ed3\u679c\u5217\u8868<\/p>\n\n\n\n

res <- list()<\/p>\n\n\n\n

res$fs.res <- fs.res<\/p>\n\n\n\n

res$res.predictK <- res.predictK<\/p>\n\n\n\n

# \u7b2c\u4e94\u6b65<\/p>\n\n\n\n

res$mainType.output <- kmeansCenterModify(res)<\/p>\n\n\n\n

3.\u5176\u4ed6\u811a\u672c<\/p>\n\n\n\n

3.1\u751f\u6210\u793a\u4f8b\u6570\u636e\u96c6<\/p>\n\n\n\n

set.seed(1)<\/p>\n\n\n\n

rm(list = ls()); gc()<\/p>\n\n\n\n

## \u539f\u59cb\u6570\u636e\u6765\u81ea\u4e8e PRJCA001063<\/p>\n\n\n\n

load(“.\/adata.T15.RData”)<\/p>\n\n\n\n

load(“.\/cell.type.RData”)<\/p>\n\n\n\n

cell.type <- cell.type[which(cell.type$cell.name %in% colnames(adata)), ]<\/p>\n\n\n\n

table(cell.type$cluster)<\/p>\n\n\n\n

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

sampled_cells <- cell.type %>%<\/p>\n\n\n\n

  group_by(cluster) %>%<\/p>\n\n\n\n

  ## if a cell type does not have more than 50 cells, we removed this cell type<\/p>\n\n\n\n

  sample_n(size = if_else(n() >= 50, 50, 0)) %>%<\/p>\n\n\n\n

  ungroup()<\/p>\n\n\n\n

print(sampled_cells)<\/p>\n\n\n\n

genes <- Seurat::VariableFeatures(adata)<\/p>\n\n\n\n

cells <- sampled_cells$cell.name<\/p>\n\n\n\n

SeuratVersionCheck = as.numeric(strsplit(as.character(packageVersion(“Seurat”)), “\\\\.”)[[1]][1])<\/p>\n\n\n\n

if (SeuratVersionCheck == 4) {<\/p>\n\n\n\n

  data <- as.matrix(adata[[“RNA”]]@data[genes, cells])<\/p>\n\n\n\n

}else if (SeuratVersionCheck == 5) {<\/p>\n\n\n\n

  data <- as.matrix(adata[[“RNA”]]$data[genes, cells])<\/p>\n\n\n\n

}<\/p>\n\n\n\n

save(data, file = “.\/CASCC\/data\/Data_PDAC_peng_2k.RData”)<\/p>\n\n\n\n

start_time <- Sys.time()<\/p>\n\n\n\n

res <- CASCC::run.CASCC(data)<\/p>\n\n\n\n

end_time <- Sys.time()<\/p>\n\n\n\n

end_time – start_time<\/p>\n\n\n\n

save(res, file = “.\/CASCC\/data\/Res_PDAC_peng_2k.RData”)<\/p>\n\n\n\n

3.2\u5728Seurat\u5de5\u4f5c\u6d41\u4e2d\u8fd0\u884cCASCC<\/p>\n\n\n\n

\u8fd9\u91cc\u6211\u4eec\u5c06\u5c55\u793a\u5982\u4f55\u5728Seurat\u5de5\u4f5c\u6d41\u4e2d\u8fd0\u884cCASCC\u3002<\/p>\n\n\n\n

SeuratVersionCheck = as.numeric(strsplit(as.character(packageVersion(“Seurat”)), “\\\\.”)[[1]][1])<\/p>\n\n\n\n

if (SeuratVersionCheck == 4) {<\/p>\n\n\n\n

  data <- SeuratObject[[“RNA”]]@data<\/p>\n\n\n\n

}else if (SeuratVersionCheck == 5) {<\/p>\n\n\n\n

  data <- SeuratObject[[“RNA”]]$data<\/p>\n\n\n\n

}<\/p>\n\n\n\n

res <- CASCC::run.CASCC(data)<\/p>\n\n\n\n

Idents(SeuratObject) <- res$mainType.output$clusteringResults<\/p>\n\n\n\n

SeuratObject<\/p>\n\n\n\n

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

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