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
作者：互联网
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
标签：tcga,TP53,BC,DD,TCGA,11A,配对 来源： https://blog.csdn.net/coding_Joash/article/details/120792599