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本教程原文：跟著NC學作圖 | TCGA VS EMT500數據集評分比較

文章

Genomic and microenvironmental heterogeneity shaping epithelial-to-mesenchymal trajectories in cancer

「數據代碼：」https://github.com/secrierlab/EMT

摘要

上皮-間充質轉化(EMT)是癌癥進展的關鍵細胞過程，具有多種中間狀態(tài)，其分子特征仍然不明顯。為了填補這一空白，我們提出了一種基于轉錄組信號強有力地評估個體腫瘤中 EMT 轉化的方法。我們應用這種方法來探索7180個上皮起源腫瘤的 EMT 軌跡，并鑒定具有預后和治療價值的三種宏觀狀態(tài)，歸因于上皮，雜交 E/M 和間充質表型。我們表明，雜種狀態(tài)是相對穩(wěn)定的，并與增加的非整倍體有關。我們進一步利用空間轉錄組學和單細胞數據集來探索 EMT 轉化的空間異質性，以及腫瘤微環(huán)境中與細胞毒性、 NK 細胞和成纖維細胞的不同相互作用模式。此外，我們提供了一個基因組事件的目錄潛在的不同進化約束的 EMT 轉化。這項研究揭示了 EMT 病程中不同階段的病因，并強調了形成原發(fā)性腫瘤間充質轉化的更廣泛的基因組和環(huán)境標志。

圖形繪制

導入數據

數據的前期準備，大家可以自己去論文中的查找，教程中不展開說明。教程的主要目的「是提供大家繪制類似圖形的代碼」。

input_cp_final2 <- read.table("HighConf_pseudotime_withEMT.txt",header = T)
head(input_cp_final2)

patients    tumors  mock_pseudospace    tgfb_pseudospace    EMT_scores
ACC.TCGA.OR.A5J1.01A.11R.A29S.07    ACC 52.3901898499394    42.4277905190899    1.23229228944451
ACC.TCGA.OR.A5J2.01A.11R.A29S.07    ACC 58.2219383281126    52.1312432022196    1.49097707952493
ACC.TCGA.OR.A5J3.01A.11R.A29S.07    ACC 51.5891775929671    41.0837306018038    1.36483064051018
ACC.TCGA.OR.A5J5.01A.11R.A29S.07    ACC 51.963211277127 46.4675216826843    1.79346466983594
ACC.TCGA.OR.A5J6.01A.31R.A29S.07    ACC 48.4272123781731    47.6193563695876    0.883014008020216
ACC.TCGA.OR.A5J7.01A.11R.A29S.07    ACC 62.4042554219295    88.0026656237261    1.10932266858008
ACC.TCGA.OR.A5J8.01A.11R.A29S.07    ACC 63.01585481594  58.0195945444141    2.73551973326235
ACC.TCGA.OR.A5J9.01A.11R.A29S.07    ACC 57.4298354766427    56.3298507121662    1.79610641797267
ACC.TCGA.OR.A5JA.01A.11R.A29S.07    ACC 60.0455600490082    70.6751155732968    1.48756584540459
ACC.TCGA.OR.A5JB.01A.11R.A29S.07    ACC 65.6103767426594    84.6863428316355    1.84836941118404

數據處理

input_cp_final2$new_ann<-rep("no",nrow(input_cp_final2))
## 分類
input_cp_final2$new_ann[grep(input_cp_final2$patients,pattern="TCGA")]<-"TCGA"  
input_cp_final2$new_ann[grep(input_cp_final2$patients,pattern="TCGA",invert=T)]<-"MET500"
input_cp_final2$new_ann<-as.factor(input_cp_final2$new_ann)

 patients tumors mock_pseudospace tgfb_pseudospace EMT_scores new_ann
1 ACC.TCGA.OR.A5J1.01A.11R.A29S.07    ACC         52.39019         42.42779   1.232292    TCGA
2 ACC.TCGA.OR.A5J2.01A.11R.A29S.07    ACC         58.22194         52.13124   1.490977    TCGA
3 ACC.TCGA.OR.A5J3.01A.11R.A29S.07    ACC         51.58918         41.08373   1.364831    TCGA
4 ACC.TCGA.OR.A5J5.01A.11R.A29S.07    ACC         51.96321         46.46752   1.793465    TCGA
5 ACC.TCGA.OR.A5J6.01A.31R.A29S.07    ACC         48.42721         47.61936   0.883014    TCGA
6 ACC.TCGA.OR.A5J7.01A.11R.A29S.07    ACC         62.40426         88.00267   1.109323    TCGA

High與Low分類

input_cp_final2$emt_status<-ifelse(input_cp_final2$EMT_scores>0,"High_EMT","Low_EMT")
summary(input_cp_final2$mock_pseudospace)
input_cp_final2$pseudospace_status<-ifelse(input_cp_final2$mock_pseudospace<=50,"Late_Pseudotime","Early_Pseudotime")

 patients tumors mock_pseudospace tgfb_pseudospace EMT_scores new_ann emt_status
1 ACC.TCGA.OR.A5J1.01A.11R.A29S.07    ACC         52.39019         42.42779   1.232292    TCGA   High_EMT
2 ACC.TCGA.OR.A5J2.01A.11R.A29S.07    ACC         58.22194         52.13124   1.490977    TCGA   High_EMT
3 ACC.TCGA.OR.A5J3.01A.11R.A29S.07    ACC         51.58918         41.08373   1.364831    TCGA   High_EMT
4 ACC.TCGA.OR.A5J5.01A.11R.A29S.07    ACC         51.96321         46.46752   1.793465    TCGA   High_EMT
5 ACC.TCGA.OR.A5J6.01A.31R.A29S.07    ACC         48.42721         47.61936   0.883014    TCGA   High_EMT
6 ACC.TCGA.OR.A5J7.01A.11R.A29S.07    ACC         62.40426         88.00267   1.109323    TCGA   High_EMT
  pseudospace_status
1   Early_Pseudotime
2   Early_Pseudotime
3   Early_Pseudotime
4   Early_Pseudotime
5    Late_Pseudotime

繪圖

library(ggplot2)
library(ggridges)
library(corrplot)

ggplot(input_cp_final2, 
          aes(x=mock_pseudospace, y=EMT_scores,color=new_ann)) +
  geom_point(shape=18,size=2,alpha=0.5)+scale_x_reverse()+ 
  scale_color_manual(values=c('#ee0c0c','#B5B7FF'))+
  geom_smooth(aes(group=new_ann),method = "lm", formula = y ~ poly(x, 10),se=TRUE, linetype="dashed",color="blue")+
  geom_hline(yintercept=0, linetype="dashed", color = "black")+theme_classic()

input_cp_final3<-input_cp_final2[input_cp_final2$new_ann%in%"MET500",]
chisq <- chisq.test(table(input_cp_final3$pseudospace_status,input_cp_final3$emt_status)[2:1,])
contrib <- 100*chisq$residuals^2/chisq$statistic
corrplot(chisq$residuals, is.cor = FALSE)