TCGA的微卫星不稳定性数据获取和可视化

1.背景知识

在一篇文章里看到了微卫星不稳定性（Microsatellite Instability，MSI）与riskscore的关系图，就去查了一下，很好的背景知识资料：

https://mp.weixin.qq.com/s/zx5eaNvBrwWxKWgIl7CC4w

核心知识：

1.计算MSI分数的工具：MANTIS，默认阈值0.4，高于阈值为MSI-H，低于阈值为MSS(无明显的MSI出现)。
2.最早再结直肠癌种发现，是预后良好的标志，MSI结直肠癌5年生存率要显著高于MSS结直肠癌，MSI-H结直肠癌比MSS结直肠癌有更好的预后。

2.寻找TCGA的MSI数据

那么TCGA微卫星不稳定的数据上哪找呢？隐约记得在cbioportal有。但是他们现在把下载接口删掉了，不好找。

继续搜索，搜到了这个包：cBioPortalData，可以下载数据。

一个不错的教程：

https://zhuanlan.zhihu.com/p/406088178

我的需求，主要是下载cBioPortal的临床信息，有很多是其他渠道找不到的。

使用起来非常简单：

library(cBioPortalData)
cbio <- cBioPortal()
studies = getStudies(cbio)
head(studies$studyId)
## [1] "acc_tcga"                     "blca_plasmacytoid_mskcc_2016"
## [3] "bcc_unige_2016"               "all_stjude_2015"             
## [5] "ampca_bcm_2016"               "blca_dfarber_mskcc_2014"
id = "ucec_tcga_pan_can_atlas_2018"
clinical = clinicalData(cbio, id)
colnames(clinical)
##  [1] "patientId"                                
##  [2] "AGE"                                      
##  [3] "AJCC_STAGING_EDITION"                     
##  [4] "BUFFA_HYPOXIA_SCORE"                      
##  [5] "CANCER_TYPE_ACRONYM"                      
##  [6] "DAYS_LAST_FOLLOWUP"                       
##  [7] "DAYS_TO_INITIAL_PATHOLOGIC_DIAGNOSIS"     
##  [8] "DSS_MONTHS"                               
##  [9] "DSS_STATUS"                               
## [10] "ETHNICITY"                                
## [11] "FORM_COMPLETION_DATE"                     
## [12] "HISTORY_NEOADJUVANT_TRTYN"                
## [13] "ICD_10"                                   
## [14] "ICD_O_3_HISTOLOGY"                        
## [15] "ICD_O_3_SITE"                             
## [16] "INFORMED_CONSENT_VERIFIED"                
## [17] "IN_PANCANPATHWAYS_FREEZE"                 
## [18] "NEW_TUMOR_EVENT_AFTER_INITIAL_TREATMENT"  
## [19] "OS_MONTHS"                                
## [20] "OS_STATUS"                                
## [21] "OTHER_PATIENT_ID"                         
## [22] "PERSON_NEOPLASM_CANCER_STATUS"            
## [23] "PFS_MONTHS"                               
## [24] "PFS_STATUS"                               
## [25] "PRIOR_DX"                                 
## [26] "RACE"                                     
## [27] "RADIATION_THERAPY"                        
## [28] "RAGNUM_HYPOXIA_SCORE"                     
## [29] "SAMPLE_COUNT"                             
## [30] "SEX"                                      
## [31] "SUBTYPE"                                  
## [32] "WEIGHT"                                   
## [33] "WINTER_HYPOXIA_SCORE"                     
## [34] "DAYS_TO_BIRTH"                            
## [35] "DFS_MONTHS"                               
## [36] "DFS_STATUS"                               
## [37] "sampleId"                                 
## [38] "ANEUPLOIDY_SCORE"                         
## [39] "CANCER_TYPE"                              
## [40] "CANCER_TYPE_DETAILED"                     
## [41] "FRACTION_GENOME_ALTERED"                  
## [42] "GRADE"                                    
## [43] "MSI_SCORE_MANTIS"                         
## [44] "MSI_SENSOR_SCORE"                         
## [45] "MUTATION_COUNT"                           
## [46] "ONCOTREE_CODE"                            
## [47] "SAMPLE_TYPE"                              
## [48] "SOMATIC_STATUS"                           
## [49] "TISSUE_PROSPECTIVE_COLLECTION_INDICATOR"  
## [50] "TISSUE_RETROSPECTIVE_COLLECTION_INDICATOR"
## [51] "TISSUE_SOURCE_SITE"                       
## [52] "TISSUE_SOURCE_SITE_CODE"                  
## [53] "TMB_NONSYNONYMOUS"                        
## [54] "TUMOR_TISSUE_SITE"                        
## [55] "TUMOR_TYPE"

这是临床信息的数据列名，里面就包括了MSI_SCORE_MANTIS这一列。

3.画个图玩

R语言的好处就是拿到了数据可以进行自定义的可视化，比网页工具更加灵活，也更好重复。

df = na.omit(clinical[,c("patientId","MSI_SCORE_MANTIS")])
colnames(df)[2] = "MSI_score"
df$MSI_score = as.numeric(df$MSI_score)
k= df$MSI_score >0.4;table(k)
## k
## FALSE  TRUE 
##   358   168
df$Group =  ifelse(k,"MSI","MSS")
head(df)
## # A tibble: 6 x 3
##   patientId    MSI_score Group
##   <chr>            <dbl> <chr>
## 1 TCGA-2E-A9G8     0.323 MSS  
## 2 TCGA-4E-A92E     0.340 MSS  
## 3 TCGA-5B-A90C     0.334 MSS  
## 4 TCGA-5S-A9Q8     0.320 MSS  
## 5 TCGA-A5-A0G1     0.311 MSS  
## 6 TCGA-A5-A0G2     0.400 MSI

整理好了数据，就可以画图啦

library(ggplot2)
ggplot(df,aes(x = Group,y = MSI_score,fill = Group))+
  geom_boxplot()+
  geom_jitter(size = 0.5)+
  geom_hline(yintercept = 0.4,lty = 4)+
  theme_bw()