task6b-哦别做梦了-TP53在TCGA的肝癌的有配对样本病人的转录组数据表达量配对图

2024-02-15 14:58:18

作业链接

0.作业题目

从ucsc的xena浏览器里面下载感兴趣癌症，比如肝癌的表达矩阵（counts值）
然后根据样本名字拿到有配对的几十个病人的癌症和正常对照数据（部分癌症数据并没有对照）
接着提取感兴趣基因（比如TP53）的表达量
最后套用上面的绘图代码即可！

1.数据下载

下载网址

然后找到LIHC

点击进去下载即可

2.数据提取以及简单统计

提取TP53的表达量数据

#TP53的ensemble id 为ENSG00000141510
zcat TCGA-LIHC.htseq_counts.tsv.gz | grep -E 'Ensembl_ID|ENSG00000141510' >TP53_tcga_expression.txt

library(dplyr)
tp53_tcga = read.table('TP53_tcga_expression.txt',header = T,check.names = F)
rownames(tp53_tcga) = 'TP53'
tp53_tcga = tp53_tcga[,-1]

统计正常样品和肿瘤样品个数

> table(colnames(tp53_tcga) %>% sub('TCGA-\\w+-\\w+-','',.) )
01A 01B 02A 02B 11A 
369   2   2   1  50 
#tumor个数:369+2+2+1=374
#normal个数:50
#一共424个样本

其中01-09是tumor样本；10-29是normal样本；
这里只保留01A和11A这两种最常用的样本
01代表的是Primary Solid Tumor；11代表的是Solid Tissue Normal，具体详见https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/sample-type-codes

#只保留01和11两类样本
tp53_tcga <- colnames(tp53_tcga) %>% grepl('-[01]1A', . ,perl = T) %>% which(.) %>% tp53_tcga[,.]

#对保留下来的样本进行统计
> table(colnames(tp53_tcga) %>% sub('TCGA-\\w+-\\w+-','',.) )
01A 11A 
369  50

提取既有normal又有tumor的病人id

如下所示，A10Q这个相同即代表的是同一个捐献者
01A代表是取的肿瘤部位的样品，11A代表取的正常组织的样品

TCGA-BC-A10Q-01A 11.514714054138487
TCGA-BC-A10Q-11A 9.843921051289035

提取有上述配对情况的病人id，共计50个

> names(which((colnames(tp53_tcga) %>% sub('-[01]1A','',.) %>% table(.))==2))
 [1] "TCGA-BC-A10Q" "TCGA-BC-A10R" "TCGA-BC-A10T" "TCGA-BC-A10U" "TCGA-BC-A10W" "TCGA-BC-A10X" "TCGA-BC-A10Y" "TCGA-BC-A10Z" "TCGA-BC-A110"
[10] "TCGA-BC-A216" "TCGA-BD-A2L6" "TCGA-BD-A3EP" "TCGA-DD-A113" "TCGA-DD-A114" "TCGA-DD-A116" "TCGA-DD-A118" "TCGA-DD-A119" "TCGA-DD-A11A"
[19] "TCGA-DD-A11B" "TCGA-DD-A11C" "TCGA-DD-A11D" "TCGA-DD-A1EB" "TCGA-DD-A1EC" "TCGA-DD-A1EE" "TCGA-DD-A1EG" "TCGA-DD-A1EH" "TCGA-DD-A1EI"
[28] "TCGA-DD-A1EJ" "TCGA-DD-A1EL" "TCGA-DD-A39V" "TCGA-DD-A39W" "TCGA-DD-A39X" "TCGA-DD-A39Z" "TCGA-DD-A3A1" "TCGA-DD-A3A2" "TCGA-DD-A3A3"
[37] "TCGA-DD-A3A4" "TCGA-DD-A3A5" "TCGA-DD-A3A6" "TCGA-DD-A3A8" "TCGA-EP-A12J" "TCGA-EP-A26S" "TCGA-EP-A3RK" "TCGA-ES-A2HT" "TCGA-FV-A23B"
[46] "TCGA-FV-A2QR" "TCGA-FV-A3I0" "TCGA-FV-A3I1" "TCGA-FV-A3R2" "TCGA-G3-A3CH"

根据上述提取到的样本名字进一步拿到其对应的tumor和normal的表达量数据

tumor_and_normal = names(which((colnames(tp53_tcga) %>% sub('-[01]1A','',.) %>% table(.))==2))
normal = tp53_tcga[,paste0(tumor_and_normal,"-11A")]
tumor = tp53_tcga[,paste0(tumor_and_normal,"-01A")]

> normal
     TCGA-BC-A10Q-11A TCGA-BC-A10R-11A TCGA-BC-A10T-11A TCGA-BC-A10U-11A TCGA-BC-A10W-11A TCGA-BC-A10X-11A TCGA-BC-A10Y-11A
TP53         9.843921         10.06474         10.13955         9.896332          9.79279         10.36304         10.56986
     TCGA-BC-A10Z-11A TCGA-BC-A110-11A TCGA-BC-A216-11A TCGA-BD-A2L6-11A TCGA-BD-A3EP-11A TCGA-DD-A113-11A TCGA-DD-A114-11A
TP53         10.71167         9.810572         9.575539         10.80574         9.930737         9.623881         10.87498
     TCGA-DD-A116-11A TCGA-DD-A118-11A TCGA-DD-A119-11A TCGA-DD-A11A-11A TCGA-DD-A11B-11A TCGA-DD-A11C-11A TCGA-DD-A11D-11A
TP53         9.847057         8.839204         10.01262         10.59246         9.259743         8.668885         9.971544
     TCGA-DD-A1EB-11A TCGA-DD-A1EC-11A TCGA-DD-A1EE-11A TCGA-DD-A1EG-11A TCGA-DD-A1EH-11A TCGA-DD-A1EI-11A TCGA-DD-A1EJ-11A
TP53         10.42731         10.11894         9.609179         10.03067         10.28309         10.29806         10.68212
     TCGA-DD-A1EL-11A TCGA-DD-A39V-11A TCGA-DD-A39W-11A TCGA-DD-A39X-11A TCGA-DD-A39Z-11A TCGA-DD-A3A1-11A TCGA-DD-A3A2-11A
TP53         10.13699         9.544964         10.26796         10.41574         9.854868         8.897845         10.16993
     TCGA-DD-A3A3-11A TCGA-DD-A3A4-11A TCGA-DD-A3A5-11A TCGA-DD-A3A6-11A TCGA-DD-A3A8-11A TCGA-EP-A12J-11A TCGA-EP-A26S-11A
TP53         9.346514         11.37829         9.529431         9.278449         9.544964         9.764872         9.949827
     TCGA-EP-A3RK-11A TCGA-ES-A2HT-11A TCGA-FV-A23B-11A TCGA-FV-A2QR-11A TCGA-FV-A3I0-11A TCGA-FV-A3I1-11A TCGA-FV-A3R2-11A
TP53          10.0348         10.89709         10.39553         10.47675         9.642052         10.02375          10.0348
     TCGA-G3-A3CH-11A
TP53         9.368506

3.可视化

input <- data.frame(normal = as.numeric(normal), tumor = as.numeric(tumor))
library(ggpubr)
ggpaired(input, cond1 = "normal", cond2 = "tumor",
         fill = "condition", palette = "jco")

参考
http://rpkgs.datanovia.com/ggpubr/reference/ggpaired.html

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