{"id":60998,"date":"2024-10-11T11:22:10","date_gmt":"2024-10-11T03:22:10","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=60998"},"modified":"2024-10-11T11:22:10","modified_gmt":"2024-10-11T03:22:10","slug":"%e5%8d%95%e7%bb%86%e8%83%9e%e6%b3%a8%e9%87%8a%e8%bf%98%e8%83%bd%e8%bf%99%e6%a0%b7%e7%8e%a9%ef%bc%9fpipet%e5%b8%a6%e4%bd%a0%e9%80%9a%e8%bf%87%e8%a1%a8%e5%9e%8b%e4%bf%a1%e6%81%af%e9%a2%84%e6%b5%8b","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=60998","title":{"rendered":"\u5355\u7ec6\u80de\u6ce8\u91ca\u8fd8\u80fd\u8fd9\u6837\u73a9\uff1fPIPET\u5e26\u4f60\u901a\u8fc7\u8868\u578b\u4fe1\u606f\u9884\u6d4b\u5355\u7ec6\u80de\u6570\u636e\u4e2d\u7684\u76f8\u5173\u4e9a\u7fa4"},"content":{"rendered":"\n

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

\u57fa\u4e8e\u7279\u5b9amarker\u7684\u5355\u7ec6\u80de\u4e9a\u7fa4\u6ce8\u91ca\u662f\u4e0d\u662f\u5df2\u7ecf\u624b\u5230\u64d2\u6765\u3001\u8f7b\u677e\u641e\u5b9a\u4e86\uff1f\u662f\u4e0d\u662f\u60f3\u5c1d\u8bd5\u4e00\u4e0b\u65b0\u7684\u5355\u7ec6\u80de\u6ce8\u91ca\u65b9\u6cd5\u4e86\uff1f\u57fa\u4e8e\u8868\u578b\u4fe1\u606f\u9884\u6d4b\u5355\u7ec6\u80de\u6570\u636e\u4e2d\u7684\u76f8\u5173\u4e9a\u7fa4\u7684\u65b9\u6cd5\u4f60\u542c\u8bf4\u8fc7\u5417\uff1f\u76ee\u524d\uff0c\u5728\u5927\u5bb9\u91cf\u6570\u636e\u4e2d\u6574\u5408\u6765\u81ea\u5177\u6709\u5e7f\u6cdb\u4e34\u5e8a\u7279\u5f81\u7684\u5c0f\u6837\u672c\u7684\u5355\u7ec6\u80de\u6570\u636e\u4ecd\u672a\u5f97\u5230\u5145\u5206\u63a2\u7d22\u3002\u56e0\u6b64\uff0cPIPET\u6a2a\u7a7a\u51fa\u4e16\uff01PIPET\u4ece\u6279\u91cf\u6570\u636e\u4e2d\u5dee\u5f02\u8868\u8fbe\u7684\u57fa\u56e0\u751f\u6210\u6bcf\u79cd\u8868\u578b\u7684\u7279\u5f81\u8f7d\u4f53\uff0c\u7136\u540e\u901a\u8fc7\u8bc4\u4f30\u5355\u7ec6\u80de\u6570\u636e\u4e0e\u8fd9\u4e9b\u8f7d\u4f53\u4e4b\u95f4\u7684\u76f8\u4f3c\u6027\u6765\u8bc6\u522b\u76f8\u5173\u7684\u7ec6\u80de\u4e9a\u7fa4\u3002\u968f\u540e\uff0c\u53ef\u4ee5\u6839\u636e\u8fd9\u4e9b\u4e9a\u7fa4\u5206\u6790\u4e0e\u8868\u578b\u76f8\u5173\u7684\u7ec6\u80de\u72b6\u6001\u3002PIPET\u4ece\u6279\u91cf\u6570\u636e\u4e2d\u5dee\u5f02\u8868\u8fbe\u7684\u57fa\u56e0\u751f\u6210\u6bcf\u79cd\u8868\u578b\u7684\u7279\u5f81\u8f7d\u4f53\uff0c\u7136\u540e\u901a\u8fc7\u8bc4\u4f30\u5355\u7ec6\u80de\u6570\u636e\u4e0e\u8fd9\u4e9b\u8f7d\u4f53\u4e4b\u95f4\u7684\u76f8\u4f3c\u6027\u6765\u8bc6\u522b\u76f8\u5173\u7684\u7ec6\u80de\u4e9a\u7fa4\u3002\u968f\u540e\uff0c\u53ef\u4ee5\u6839\u636e\u8fd9\u4e9b\u4e9a\u7fa4\u5206\u6790\u4e0e\u8868\u578b\u76f8\u5173\u7684\u7ec6\u80de\u72b6\u6001\u3002\u5355\u7ec6\u80de\u6570\u636e\u5e26\u6709\u8868\u578b\u3001\u4e34\u5e8a\u76f8\u5173\u4fe1\u606f\u7684\u5c0f\u4f19\u4f34\u4eec\u5f00\u59cb\u5b66\u8d77\u6765\u5427\uff01\u5927\u6d77\u54e5\u5c31\u662f\u8fd9\u4e48\u7684\u5ba0\u7c89\uff0c\u6709\u4ec0\u4e48\u751f\u4fe1\u5206\u6790\u4e0a\u7684\u95ee\u9898\u5927\u5bb6\u5c3d\u7ba1\u54a8\u8be2\u5927\u6d77\u54e5\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\u5927\u6d77\u54e5\u54e6\uff01\u7ec3\u4e86\u5341\u5e74\u751f\u4fe1\u5206\u6790\u7684\u5927\u6d77\u54e5\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

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

\u751f\u4fe1\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\u5927\u6d77\u54e5\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\u5927\u6d77\u54e5\uff0c\u4fdd\u8bc1\u670d\u52a1\u5668\u7684\u6027\u4ef7\u6bd4\u6700\u9ad8\u54e6\uff01<\/p>\n\n\n\n

\u4ee3\u7801\u6559\u7a0b<\/strong><\/strong><\/p>\n\n\n\n

\u8981\u5b89\u88c5\u6b64\u5305\uff0c\u8bf7\u542f\u52a8 R\uff08\u7248\u672c >= 4.3.1\uff09\u5e76\u8f93\u5165\uff1a<\/p>\n\n\n\n

if (!require(“devtools”))<\/p>\n\n\n\n

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

devtools::install_github(“ruan2ruan\/PIPET”)<\/p>\n\n\n\n

\u5982\u679c\u5728\u5b89\u88c5\u8fc7\u7a0b\u4e2d\u51fa\u73b0\u9519\u8bef\uff0c\u6211\u4eec\u5efa\u8bae\u60a8\u5148\u5c1d\u8bd5 \u901a\u8fc7\u5f15\u7528 DESCRIPTION \u6587\u4ef6\u4e2d\u7684Imports\u6765\u5b89\u88c5\u6240\u6709 R \u4f9d\u8d56\u9879\uff0c\u7136\u540e\u5b89\u88c5 PIPET\u3002<\/p>\n\n\n\n

##\u52a0\u8f7dR\u5305<\/p>\n\n\n\n

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

\u5728\u672c\u6559\u7a0b\u4e2d\uff0c\u6211\u4eec\u7684\u5355\u7ec6\u80de\u6570\u636e\u96c6\u6765\u81ea\u4e8e\u5916\u5468\u8840\u5355\u4e2a\u6838\u7ec6\u80de(PBMC)\uff0c\u53ef\u4ece10X\u57fa\u56e0\u7ec4\u514d\u8d39\u83b7\u5f97\u3002\u7ecf\u8fc7\u9884\u5904\u7406\u548c\u5355\u5143\u683c\u7c7b\u578b\u6807\u6ce8\u7684\u6570\u636e\u53ef\u4ee5\u4eceSeuratData\u4e0b\u8f7d\u3002\u56e0\u6b64\uff0c\u5728\u52a0\u8f7dSeurat\u5bf9\u8c61\u6570\u636e\u4e4b\u524d\uff0c\u6211\u4eec\u8fd8\u9700\u8981\u52a0\u8f7d\u4e00\u4e9b\u76f8\u5173\u7684\u5305:<\/p>\n\n\n\n

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

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

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

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

\u672c\u6587\u5c55\u793a\u4e86\u5355\u7ec6\u80de\u6570\u636ePIPET\u9884\u6d4b\u7684\u5173\u952e\u6b65\u9aa4\uff0c\u5e76\u63d0\u4f9b\u4e86\u4e00\u4e9b\u4e0b\u6e38\u53ef\u89c6\u5316\u7ed3\u679c\u3002PIPET\u7684\u8f93\u5165\u9700\u8981\u4e00\u4e2a\u5355\u7ec6\u80de\u8868\u8fbe\u77e9\u9635\u548c\u4e00\u7ec4\u5305\u542b\u8868\u578b\u4fe1\u606f\u7684\u7279\u5f81\u5411\u91cf\u3002\u8be5\u8f6f\u4ef6\u5305\u5305\u542b\u7528\u4e8eAML\u5206\u7c7b\u7684571\u4e2a\u57fa\u56e0\u9762\u677f\uff0c\u4ee5\u53caTCGA-LAML\u7684TPM\u8868\u8fbe\u6570\u636e\u548c\u751f\u5b58\u6570\u636e\u7684\u9884\u5904\u7406\uff0c\u73b0\u5728\u6211\u4eec\u5c06\u5c55\u793a\u5982\u4f55\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u6267\u884cPIPET\u3002<\/p>\n\n\n\n

#InstallData(“pbmc3k”)<\/p>\n\n\n\n

pbmc3k.final <- LoadData(“pbmc3k”, type = “pbmc3k.final”)<\/p>\n\n\n\n

SC = UpdateSeuratObject(object = pbmc3k.final)<\/p>\n\n\n\n

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

\u7136\u540e\uff0c\u6211\u4eec\u52a0\u8f7d\u4e86AML\u5206\u7c7b\u7684\u7279\u5f81\u6570\u636e(571\u4e2a\u57fa\u56e0\u8bc6\u522b\u4e86\u4e09\u4e2a\u4e3b\u8981\u7c07c1, c2, c3)\u3002\u751f\u6210\u4e86\u8fd9\u4e9b\u7279\u5f81\u57fa\u56e0\uff0c\u5e76\u8bc1\u660e\u5b83\u4eec\u5728AML\u4e34\u5e8a\u76f8\u5173\u4e9a\u578b\u7684\u5206\u7c7b\u4e2d\u5177\u6709\u826f\u597d\u7684\u6027\u80fd\u3002<\/p>\n\n\n\n

markers <- read.table(“..\/data\/AML_Subtypes_signature.txt”,sep = “\\t”,check.names = F,stringsAsFactors = F,header = T,row.names = NULL)<\/p>\n\n\n\n

table(markers$class)<\/p>\n\n\n\n

head(markers,5)<\/p>\n\n\n\n

\u4f7f\u7528PIPET<\/a>\u8bc6\u522b\u7ec6\u80de\u4e9a\u7fa4<\/p>\n\n\n\n

\u57fa\u4e8e\u4e0a\u8ff0\u8f93\u5165\u6570\u636e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528PIPET\u8bc6\u522b\u8868\u578b\u76f8\u5173\u7684\u7ec6\u80de\u4e9a\u7fa4\u3002\u8ba1\u7b97\u53ef\u80fd\u9700\u8981\u51e0\u5206\u949f\uff0c\u8fd9\u53d6\u51b3\u4e8e\u4f60\u7684\u6570\u636e\u91cf\u548c\u4f60\u8bbe\u7f6e\u7684\u6838\u5fc3\u6570\u91cf\u3002<\/p>\n\n\n\n

set.seed(123)<\/p>\n\n\n\n

tmp <- PIPET(SC_data=SC, markers=markers, gene_col= “genes”, class_col= “class”,<\/p>\n\n\n\n

             nCores=4)<\/p>\n\n\n\n

\u6211\u4eec\u5c06PIPET\u9884\u6d4b\u7684\u7ec6\u80de\u4e9a\u7fa4\u7ed3\u679c\u4e0e\u539f\u59cb\u5355\u7ec6\u80de\u6570\u636e\u5408\u5e76\u3002<\/p>\n\n\n\n

tmp$ID <- rownames(tmp)<\/p>\n\n\n\n

metadata <- SC@meta.data<\/p>\n\n\n\n

metadata$ID <- rownames(metadata)<\/p>\n\n\n\n

meta <- merge(metadata,tmp,by=”ID”)<\/p>\n\n\n\n

rownames(meta) <- meta$ID<\/p>\n\n\n\n

SC <- AddMetaData(SC, metadata = meta)<\/p>\n\n\n\n

\u6ee4\u9664\u663e\u8457\u76f8\u5173\u7684\u7ec6\u80de\u4e9a\u7fa4(P\u503c\u5c0f\u4e8e0.05)\u540e\uff0cPIPET\u9274\u5b9a\u51fa\u4e0eAML-c1\u4e9a\u578b\u76f8\u5173\u7684PIPET_c1\u7ec6\u80de66\u4e2a\uff0c\u4e0eAML-c2\u4e9a\u578b\u76f8\u5173\u7684PIPET_c2\u7ec6\u80de517\u4e2a\uff0c\u4e0eAML-c3\u4e9a\u578b\u76f8\u5173\u7684PIPET_c3\u7ec6\u80de34\u4e2a\u3002<\/p>\n\n\n\n

SC1 <- subset(x = SC, subset= (Pvalue < 0.05))<\/p>\n\n\n\n

table(SC1$prediction)<\/p>\n\n\n\n

\u4f7f\u7528UMAP\u53ef\u89c6\u5316\u6240\u6709\u7ec6\u80de<\/p>\n\n\n\n

\u6211\u4eec\u9996\u5148\u4f7f\u7528UMAP\u56fe\u53ef\u89c6\u5316\u4e86\u5206\u6790\u4e2d\u5305\u542b\u7684\u6240\u6709\u7ec6\u80de\u3002\u4e0d\u540c\u7684\u7ec6\u80de\u7c7b\u578b\u901a\u8fc7\u989c\u8272\u6765\u533a\u5206\u3002<\/p>\n\n\n\n

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

\u4f7f\u7528UMAP\u53ef\u89c6\u5316\u9884\u6d4b\u7684\u7ec6\u80de\uff0c\u7136\u540e\u6211\u4eec\u53ea\u7ed8\u5236\u5e26PIPET\u6ce8\u91ca\u7684\u7ec6\u80de\u3002<\/p>\n\n\n\n

metadata <- SC@meta.data<\/p>\n\n\n\n

temp <- data.frame(ID=SC1$ID,type=SC1$prediction)<\/p>\n\n\n\n

meta <- merge(metadata,temp,by=”ID”,all = TRUE)<\/p>\n\n\n\n

meta$type <- as.character(meta$type)<\/p>\n\n\n\n

meta$type  <- ifelse(is.na(meta$type), “Undefined”, meta$type)<\/p>\n\n\n\n

rownames(meta) <- meta$ID<\/p>\n\n\n\n

meta <- meta[SC$ID,]<\/p>\n\n\n\n

SC <- AddMetaData(SC, metadata = meta)<\/p>\n\n\n\n

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

colors<-brewer.pal(n = 8, name = “Set1”)<\/p>\n\n\n\n

PIPET_DotPlot(SC,colors,group=”type”,title=”AML-Subtypes”)<\/p>\n\n\n\n

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

\u4e3a\u4e86\u66f4\u76f4\u89c2\u7684\u6bd4\u8f83\uff0c\u6211\u4eec\u540c\u65f6\u7ed8\u5236\u4e24\u4e2a\u7ed3\u679c\u3002<\/p>\n\n\n\n

p1 <- DimPlot(SC, reduction = “umap”, group.by = ‘seurat_annotations’, label = F) +<\/p>\n\n\n\n

  ggtitle(“pbmc3k-Celltypes”)<\/p>\n\n\n\n

p2 <- PIPET_DotPlot(SC,colors,group=”type”,title=”AML-Subtypes”)<\/p>\n\n\n\n

p1 | p2<\/p>\n\n\n\n

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

\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0c\u6bcf\u4e2aPIPET\u4e9a\u7fa4\u4e2d\u7684\u7ec6\u80de\u6765\u81ea\u4e0d\u540c\u7684\u7ec6\u80de\u7c7b\u578b\u3002PIPET_BarPlot()\u53ef\u4ee5\u901a\u8fc7\u5806\u53e0\u7684\u76f4\u65b9\u56fe\u663e\u793a\u6bcf\u4e2a\u7ec6\u80de\u5b50\u7fa4\u7684\u4e3b\u8981\u7ec6\u80de\u7ec4\u6210\u3002<\/p>\n\n\n\n

table(SC1$seurat_annotations,SC1$prediction)<\/p>\n\n\n\n

PIPET_BarPlot(SC1,group=”seurat_annotations”,prediction=”prediction”,ylim=530)<\/p>\n\n\n\n

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

\u7136\u540e\u6211\u4eec\u53d1\u73b0\u4e86PIPET_c1, PIPET_c2\u548cPIPET_c3\u7684\u5dee\u5f02\u8868\u8fbe\u7279\u5f81\uff0c\u5e76\u9009\u62e9\u9876\u7ea7\u6807\u8bb0\u57fa\u56e0\u6765\u53ef\u89c6\u5316\u3002<\/p>\n\n\n\n

DefaultAssay(SC1) <- “RNA”<\/p>\n\n\n\n

Idents(SC1)=”prediction”<\/p>\n\n\n\n

SC.markers <- FindAllMarkers(SC1, only.pos = TRUE,<\/p>\n\n\n\n

                             min.pct = 0.25, logfc.threshold = 0.25)<\/p>\n\n\n\n

biomarkers <- SC.markers %>%<\/p>\n\n\n\n

  dplyr::filter(p_val_adj<0.05) %>%<\/p>\n\n\n\n

  group_by(cluster) %>%<\/p>\n\n\n\n

  slice_max(n = 20000, order_by = avg_log2FC)<\/p>\n\n\n\n

top <- biomarkers %>%<\/p>\n\n\n\n

  group_by(cluster) %>%<\/p>\n\n\n\n

  slice_head(n = 4)<\/p>\n\n\n\n

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

\u6211\u4eec\u4f7f\u7528PIPET_MultiViolin()\u521b\u5efa\u4e86\u4e00\u4e2a\u5806\u53e0\u5c0f\u63d0\u7434\u56fe\uff0c\u5b83\u53ef\u4ee5\u5728\u540c\u4e00\u5e45\u56fe\u4e2d\u663e\u793a\u591a\u4e2a\u7ec6\u80de\u7fa4\u4f53\u6216\u7ec6\u80de\u7c7b\u578b\u7684\u5c0f\u63d0\u7434\u56fe\uff0c\u4ee5\u6bd4\u8f83\u5b83\u4eec\u4e4b\u95f4\u7684\u57fa\u56e0\u8868\u8fbe\u53d8\u5316\u3002<\/p>\n\n\n\n

features <- top$gene<\/p>\n\n\n\n

PIPET_MultiViolin(SC1, features, preprocess=FALSE)<\/p>\n\n\n\n

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

\u52a0\u8f7d\u9884\u5904\u7406\u540e\u7684TCGA-LAML\u4f53\u8868\u8fbe\u77e9\u9635\u548c\u76f8\u5e94\u7684\u4e34\u5e8a\u751f\u5b58\u6570\u636e\u3002TCGA-LAML\u7684FPKM\u6570\u636e\u548c\u76f8\u5e94\u7684\u4e34\u5e8a\u6570\u636e\u4eceXena\u516c\u5171\u5e73\u53f0(https:\/\/xena.ucsc.edu\/public-hubs)\u4e0b\u8f7d\u3002\u6211\u4eec\u5c06FPKM\u8868\u8fbe\u6570\u636e\u8f6c\u6362\u4e3aTPM\uff0c\u5e76\u5c06\u6837\u672c\u4e0e\u8868\u8fbe\u6570\u636e\u548c\u4e34\u5e8a\u6570\u636e\u8fdb\u884c\u5339\u914d\u3002<\/p>\n\n\n\n

data(“TCGA_LAML_TPM”)<\/p>\n\n\n\n

TPM <- TCGA_LAML_TPM<\/p>\n\n\n\n

data(“TCGA_LAML_Surv”)<\/p>\n\n\n\n

surv.info <- TCGA_LAML_Surv<\/p>\n\n\n\n

\u5728\u8fd9\u4e2aTPM\u8868\u8fbe\u77e9\u9635\u4e2d\uff0c\u6bcf\u884c\u4ee3\u8868\u4e00\u4e2a\u57fa\u56e0\uff0c\u6bcf\u5217\u4ee3\u8868\u4e00\u4e2a\u6837\u672c\u3002\u540e\u7eed\u5206\u6790\u5171\u7eb3\u516549038\u4e2a\u57fa\u56e0\u548c130\u4e2a\u6837\u672c\u3002<\/p>\n\n\n\n

dim(TPM)<\/p>\n\n\n\n

\u6211\u4eec\u53ea\u9009\u53d6\u751f\u5b58\u4fe1\u606f\u7684\u4e34\u5e8a\u6570\u636e\uff0c\u5305\u62ec\u6837\u672cID\u3001\u7ed3\u5c40\u4e8b\u4ef6(1\u8868\u793a\u6b7b\u4ea1\uff0c0\u8868\u793a\u5b58\u6d3b)\u548c\u751f\u5b58\u65f6\u95f4(\u4ee5\u5929\u4e3a\u5355\u4f4d)\u3002<\/p>\n\n\n\n

head(surv.info,3)<\/p>\n\n\n\n

\u9009\u53d6PIPET_c3\u4e2d\u6709\u663e\u8457\u5dee\u5f02\u8868\u8fbe\u7684\u57fa\u56e0(c3\u4e9a\u578b\u4e3a\u9884\u540e\u6700\u5dee\u7684\u4e9a\u578b)\uff0c\u5efa\u7acb\u591a\u53d8\u91cfCox\u56de\u5f52\u6a21\u578b\u3002<\/p>\n\n\n\n

c3 <- biomarkers %>%<\/p>\n\n\n\n

  filter(cluster==”c3″) %>%<\/p>\n\n\n\n

  filter(avg_log2FC>=2)<\/p>\n\n\n\n

TPM.norm <- log2(TPM + 1)<\/p>\n\n\n\n

TPM.norm <- data.frame(t(na.omit(TPM.norm[c3$gene,])))<\/p>\n\n\n\n

genes <- colnames(TPM.norm)<\/p>\n\n\n\n

tmp <-TPM.norm[rownames(surv.info),]<\/p>\n\n\n\n

survival_info_df <- cbind(tmp,surv.info)<\/p>\n\n\n\n

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

multi_COX<-coxph(Surv(futime, fustat) ~ ., data=survival_info_df)<\/p>\n\n\n\n

PIPET_KM()\u6839\u636e\u591a\u53d8\u91cfCox\u56de\u5f52\u6a21\u578b\u5f97\u5230\u7684\u7cfb\u6570\u8ba1\u7b97\u6bcf\u4e2a\u6837\u672c\u7684\u98ce\u9669\u8bc4\u5206\u3002\u7136\u540e\u6839\u636e\u98ce\u9669\u8bc4\u5206\u4e2d\u4f4d\u6570\u5c06\u6837\u672c\u5206\u4e3a\u9ad8\/\u4f4e\u98ce\u9669\u7ec4\u3002\u6700\u540e\uff0c\u6839\u636e\u98ce\u9669\u7fa4\u4f53\u7ed8\u5236KM\u751f\u5b58\u66f2\u7ebf\u3002\u5982\u4e0b\u56fe\u6240\u793a\uff0c\u9ad8\u5371\u7ec4\u60a3\u8005\u9884\u540e\u5dee\u4e8e\u4f4e\u5371\u7ec4\u60a3\u8005\u3002<\/p>\n\n\n\n

PIPET_KM(data=survival_info_df, genes=genes,coxph=multi_COX, surv_cols=c(“fustat”,”futime”),<\/p>\n\n\n\n

         xlim=100,legend.title=”TCGA-LAML”, legend.labs = c(“HRisk”, “LRisk”),<\/p>\n\n\n\n

         pval = TRUE, conf.int = TRUE,  palette = “simpsons”)<\/p>\n\n\n\n

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

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

\u4ee5\u4e0a\uff0c\u6211\u4eec\u4ecb\u7ecd\u4e86PIPET\u5305\u5305\u542b\u7684\u4e3b\u8981\u529f\u80fd\u3002\u5b83\u53ef\u4ee5\u5e2e\u52a9\u60a8\u6574\u5408\u6765\u81ea\u6279\u91cf\u6570\u636e\u7684\u4fe1\u606f\uff0c\u5e76\u6ce8\u91ca\u5355\u7ec6\u80de\u6570\u636e\uff0c\u5e76\u8fdb\u884c\u591a\u7ec4\u5b66\u5206\u6790\u3002PIPET\u5305\u4e0d\u4ec5\u53ef\u4ee5\u7528\u4e8e\u9884\u6d4b\u5355\u4e2a\u7ec6\u80de\u4e9a\u7fa4\u4e2d\u7684\u76f8\u5173\u7ec6\u80de\u4e9a\u7fa4\uff0c\u800c\u4e14\u8fd8\u63d0\u4f9b\u4e86\u51e0\u79cd\u4e0b\u6e38\u5206\u6790\u7684\u53ef\u89c6\u5316\u529f\u80fd\u3002\u6700\u540e\u4e86\u5927\u6d77\u54e5\u8fd8\u662f\u8981\u7ed9\u5927\u5bb6\u5c0f\u9001\u4e00\u6ce2\u798f\u5229\uff01\u5927\u6d77\u54e5\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<\/u><\/a>\uff01<\/p>\n","protected":false},"excerpt":{"rendered":"

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