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新版TCGA不同癌种数据合并
很多文章对于TCGA中的一些癌症都是联合分析的，比如TCGA-COAD和TCGA-READ，首先是它们的疾病特点和治疗方式存在很多相似之处，同时这样做也可以增大样本量。
如果你是使用
TCGAbiolinks包下载的数据，那么它们的合并超级简单，直接cbind()即可！加载数据和R包
数据都是之前下载好的，可以参考之前的推文：
我们直接加载TCGA-COAD和TCGA-READ的数据。
#library(TCGAbiolinks) # COAD load(file = "./TCGA-mRNA/TCGA-COAD_mRNA.Rdata") coad <- data # READ load(file = "./TCGA-mRNA/TCGA-READ_mRNA.Rdata") read <- data- 1
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合并数据
现在
coad和read都是SummarizedExperiment对象，并且具有相同的行和行名：coad ## Loading required package: SummarizedExperiment ## Loading required package: MatrixGenerics ## Loading required package: matrixStats ## ## Attaching package: 'MatrixGenerics' ## The following objects are masked from 'package:matrixStats': ## ## colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, ## colCounts, colCummaxs, colCummins, colCumprods, colCumsums, ## colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, ## colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, ## colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, ## colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, ## colWeightedMeans, colWeightedMedians, colWeightedSds, ## colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, ## rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, ## rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, ## rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, ## rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, ## rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, ## rowWeightedMads, rowWeightedMeans, rowWeightedMedians, ## rowWeightedSds, rowWeightedVars ## Loading required package: GenomicRanges ## Loading required package: stats4 ## Loading required package: BiocGenerics ## ## Attaching package: 'BiocGenerics' ## The following objects are masked from 'package:stats': ## ## IQR, mad, sd, var, xtabs ## The following objects are masked from 'package:base': ## ## anyDuplicated, append, as.data.frame, basename, cbind, colnames, ## dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep, ## grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget, ## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, ## rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply, ## union, unique, unsplit, which.max, which.min ## Loading required package: S4Vectors ## ## Attaching package: 'S4Vectors' ## The following objects are masked from 'package:base': ## ## expand.grid, I, unname ## Loading required package: IRanges ## ## Attaching package: 'IRanges' ## The following object is masked from 'package:grDevices': ## ## windows ## Loading required package: GenomeInfoDb ## Loading required package: Biobase ## Welcome to Bioconductor ## ## Vignettes contain introductory material; view with ## 'browseVignettes()'. To cite Bioconductor, see ## 'citation("Biobase")', and for packages 'citation("pkgname")'. ## ## Attaching package: 'Biobase' ## The following object is masked from 'package:MatrixGenerics': ## ## rowMedians ## The following objects are masked from 'package:matrixStats': ## ## anyMissing, rowMedians ## class: RangedSummarizedExperiment ## dim: 60660 521 ## metadata(1): data_release ## assays(6): unstranded stranded_first ... fpkm_unstrand fpkm_uq_unstrand ## rownames(60660): ENSG00000000003.15 ENSG00000000005.6 ... ## ENSG00000288674.1 ENSG00000288675.1 ## rowData names(10): source type ... hgnc_id havana_gene ## colnames(521): TCGA-A6-5664-01A-21R-1839-07 ## TCGA-D5-6530-01A-11R-1723-07 ... TCGA-A6-2683-01A-01R-0821-07 ## TCGA-A6-2683-11A-01R-A32Z-07 ## colData names(107): barcode patient ... paper_vascular_invasion_present ## paper_vital_status read ## class: RangedSummarizedExperiment ## dim: 60660 177 ## metadata(1): data_release ## assays(6): unstranded stranded_first ... fpkm_unstrand fpkm_uq_unstrand ## rownames(60660): ENSG00000000003.15 ENSG00000000005.6 ... ## ENSG00000288674.1 ENSG00000288675.1 ## rowData names(10): source type ... hgnc_id havana_gene ## colnames(177): TCGA-AG-3580-01A-01R-0821-07 ## TCGA-AF-2692-11A-01R-A32Z-07 ... TCGA-AG-3894-01A-01R-1119-07 ## TCGA-AG-3574-01A-01R-0821-07 ## colData names(107): barcode patient ... paper_vascular_invasion_present ## paper_vital_status- 1
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对于这样的数据我们直接合并即可，我认为这是目前合并两个癌种最方便的方法了！
# 直接cbind colrectal <- cbind(coad,read) colrectal ## class: RangedSummarizedExperiment ## dim: 60660 698 ## metadata(2): data_release data_release ## assays(6): unstranded stranded_first ... fpkm_unstrand fpkm_uq_unstrand ## rownames(60660): ENSG00000000003.15 ENSG00000000005.6 ... ## ENSG00000288674.1 ENSG00000288675.1 ## rowData names(10): source type ... hgnc_id havana_gene ## colnames(698): TCGA-A6-5664-01A-21R-1839-07 ## TCGA-D5-6530-01A-11R-1723-07 ... TCGA-AG-3894-01A-01R-1119-07 ## TCGA-AG-3574-01A-01R-0821-07 ## colData names(107): barcode patient ... paper_vascular_invasion_present ## paper_vital_status- 1
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得到的结果也是一个
SummarizedExperiment对象。并且这个对象中各种信息也是保存好的，想用什么直接提取即可，非常方便。但是这样合并可能涉及批次效应的问题，大家在实际使用时可根据自己的情况选择要不要去除批次效应！
提取信息
比如提取样本的临床信息，非常简单，甚至不需要重新下载：
clin <- as.data.frame(colData(colrectal)) clin[1:10,1:10] ## barcode patient ## TCGA-A6-5664-01A-21R-1839-07 TCGA-A6-5664-01A-21R-1839-07 TCGA-A6-5664 ## TCGA-D5-6530-01A-11R-1723-07 TCGA-D5-6530-01A-11R-1723-07 TCGA-D5-6530 ## TCGA-AA-3556-01A-01R-0821-07 TCGA-AA-3556-01A-01R-0821-07 TCGA-AA-3556 ## TCGA-AA-3660-11A-01R-1723-07 TCGA-AA-3660-11A-01R-1723-07 TCGA-AA-3660 ## TCGA-AA-3818-01A-01R-0905-07 TCGA-AA-3818-01A-01R-0905-07 TCGA-AA-3818 ## TCGA-AA-3660-01A-01R-1723-07 TCGA-AA-3660-01A-01R-1723-07 TCGA-AA-3660 ## TCGA-DM-A28G-01A-11R-A16W-07 TCGA-DM-A28G-01A-11R-A16W-07 TCGA-DM-A28G ## TCGA-AA-3976-01A-01R-1022-07 TCGA-AA-3976-01A-01R-1022-07 TCGA-AA-3976 ## TCGA-G4-6307-01A-11R-1723-07 TCGA-G4-6307-01A-11R-1723-07 TCGA-G4-6307 ## TCGA-AA-3522-11A-01R-A32Z-07 TCGA-AA-3522-11A-01R-A32Z-07 TCGA-AA-3522 ## sample shortLetterCode ## TCGA-A6-5664-01A-21R-1839-07 TCGA-A6-5664-01A TP ## TCGA-D5-6530-01A-11R-1723-07 TCGA-D5-6530-01A TP ## TCGA-AA-3556-01A-01R-0821-07 TCGA-AA-3556-01A TP ## TCGA-AA-3660-11A-01R-1723-07 TCGA-AA-3660-11A NT ## TCGA-AA-3818-01A-01R-0905-07 TCGA-AA-3818-01A TP ## TCGA-AA-3660-01A-01R-1723-07 TCGA-AA-3660-01A TP ## TCGA-DM-A28G-01A-11R-A16W-07 TCGA-DM-A28G-01A TP ## TCGA-AA-3976-01A-01R-1022-07 TCGA-AA-3976-01A TP ## TCGA-G4-6307-01A-11R-1723-07 TCGA-G4-6307-01A TP ## TCGA-AA-3522-11A-01R-A32Z-07 TCGA-AA-3522-11A NT ## definition sample_submitter_id ## TCGA-A6-5664-01A-21R-1839-07 Primary solid Tumor TCGA-A6-5664-01A ## TCGA-D5-6530-01A-11R-1723-07 Primary solid Tumor TCGA-D5-6530-01A ## TCGA-AA-3556-01A-01R-0821-07 Primary solid Tumor TCGA-AA-3556-01A ## TCGA-AA-3660-11A-01R-1723-07 Solid Tissue Normal TCGA-AA-3660-11A ## TCGA-AA-3818-01A-01R-0905-07 Primary solid Tumor TCGA-AA-3818-01A ## TCGA-AA-3660-01A-01R-1723-07 Primary solid Tumor TCGA-AA-3660-01A ## TCGA-DM-A28G-01A-11R-A16W-07 Primary solid Tumor TCGA-DM-A28G-01A ## TCGA-AA-3976-01A-01R-1022-07 Primary solid Tumor TCGA-AA-3976-01A ## TCGA-G4-6307-01A-11R-1723-07 Primary solid Tumor TCGA-G4-6307-01A ## TCGA-AA-3522-11A-01R-A32Z-07 Solid Tissue Normal TCGA-AA-3522-11A ## sample_type_id ## TCGA-A6-5664-01A-21R-1839-07 01 ## TCGA-D5-6530-01A-11R-1723-07 01 ## TCGA-AA-3556-01A-01R-0821-07 01 ## TCGA-AA-3660-11A-01R-1723-07 11 ## TCGA-AA-3818-01A-01R-0905-07 01 ## TCGA-AA-3660-01A-01R-1723-07 01 ## TCGA-DM-A28G-01A-11R-A16W-07 01 ## TCGA-AA-3976-01A-01R-1022-07 01 ## TCGA-G4-6307-01A-11R-1723-07 01 ## TCGA-AA-3522-11A-01R-A32Z-07 11 ## sample_id ## TCGA-A6-5664-01A-21R-1839-07 3048539a-b914-4e43-b1cc-43ea707e3b3d ## TCGA-D5-6530-01A-11R-1723-07 50560725-c72d-4bab-b602-5e50e6bececd ## TCGA-AA-3556-01A-01R-0821-07 4794413c-ed92-451c-a3ce-f411fed5ca82 ## TCGA-AA-3660-11A-01R-1723-07 a0832917-75b9-45c8-9273-009c3737a43a ## TCGA-AA-3818-01A-01R-0905-07 0cf35153-2c04-4bdd-91e0-d63cb98da5bf ## TCGA-AA-3660-01A-01R-1723-07 87cf1a20-2dc5-4c06-b0c4-16103be40ef0 ## TCGA-DM-A28G-01A-11R-A16W-07 f5acd8b8-32c4-4f3c-aacd-259f8e1fdfee ## TCGA-AA-3976-01A-01R-1022-07 8d529023-abca-4ddc-a265-d5bd1fd48708 ## TCGA-G4-6307-01A-11R-1723-07 801b8d05-2d29-4f8c-8d8d-634b2e21b867 ## TCGA-AA-3522-11A-01R-A32Z-07 218bbd07-5fa3-4946-a2c1-0ece13466441 ## sample_type days_to_collection ## TCGA-A6-5664-01A-21R-1839-07 Primary Tumor NA ## TCGA-D5-6530-01A-11R-1723-07 Primary Tumor NA ## TCGA-AA-3556-01A-01R-0821-07 Primary Tumor NA ## TCGA-AA-3660-11A-01R-1723-07 Solid Tissue Normal NA ## TCGA-AA-3818-01A-01R-0905-07 Primary Tumor NA ## TCGA-AA-3660-01A-01R-1723-07 Primary Tumor NA ## TCGA-DM-A28G-01A-11R-A16W-07 Primary Tumor 3419 ## TCGA-AA-3976-01A-01R-1022-07 Primary Tumor NA ## TCGA-G4-6307-01A-11R-1723-07 Primary Tumor NA ## TCGA-AA-3522-11A-01R-A32Z-07 Solid Tissue Normal NA dim(clin) ## [1] 698 107 colnames(clin)[10:30] ## [1] "days_to_collection" "state" ## [3] "initial_weight" "intermediate_dimension" ## [5] "pathology_report_uuid" "submitter_id" ## [7] "shortest_dimension" "oct_embedded" ## [9] "longest_dimension" "is_ffpe" ## [11] "tissue_type" "synchronous_malignancy" ## [13] "ajcc_pathologic_stage" "days_to_diagnosis" ## [15] "treatments" "last_known_disease_status" ## [17] "tissue_or_organ_of_origin" "days_to_last_follow_up" ## [19] "age_at_diagnosis" "primary_diagnosis" ## [21] "prior_malignancy"- 1
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现在一共有698行，107列临床信息，你想要的生存时间、生存状态、样本类型、分期等信息都在里面，都不需要自己手动划分，想要什么直接取子集就好了。
比如大家最喜欢的生存信息：
clin_subset <- clin[,c("days_to_last_follow_up","vital_status")] head(clin_subset) ## days_to_last_follow_up vital_status ## TCGA-A6-5664-01A-21R-1839-07 672 Alive ## TCGA-D5-6530-01A-11R-1723-07 621 Alive ## TCGA-AA-3556-01A-01R-0821-07 700 Alive ## TCGA-AA-3660-11A-01R-1723-07 2375 Alive ## TCGA-AA-3818-01A-01R-0905-07 NA Dead ## TCGA-AA-3660-01A-01R-1723-07 2375 Alive- 1
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合并miRNA
也是一样的操作。
rm(list = ls()) load(file = "./TCGA-mirna/TCGA-COAD_miRNA.Rdata") coad <- data load(file = "./TCGA-mirna/TCGA-READ_miRNA.Rdata") read <- data- 1
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可以看到两个表达矩阵的第一列（miRNA的名字），完全一样：
identical(coad$miRNA_ID,read$miRNA_ID) ## [1] TRUE- 1
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所以我们直接合并即可：
# 第一列都是 colrectal_mi <- cbind(coad,read[,-1]) colrectal_mi[1:5,1:4] ## miRNA_ID read_count_TCGA-A6-5664-01A-21H-1838-13 ## 1 hsa-let-7a-1 6959 ## 2 hsa-let-7a-2 6941 ## 3 hsa-let-7a-3 7120 ## 4 hsa-let-7b 31616 ## 5 hsa-let-7c 4211 ## reads_per_million_miRNA_mapped_TCGA-A6-5664-01A-21H-1838-13 ## 1 8143.201 ## 2 8122.137 ## 3 8331.598 ## 4 36996.038 ## 5 4927.578 ## cross-mapped_TCGA-A6-5664-01A-21H-1838-13 ## 1 N ## 2 N ## 3 N ## 4 N ## 5 N- 1
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但是miRNA的表达矩阵现在还有点问题，它包含3种信息：count/rpm/cross-mapped，而我们只需要count，所以还是要处理一下。
dim(colrectal_mi) ## [1] 1881 1891 # 只要count colrec_mi <- colrectal_mi[,c(1,seq(2,1891,by=3))] dim(colrec_mi) ## [1] 1881 631 # 改下列名 colnames(colrec_mi)[-1] <- substr(colnames(colrec_mi)[-1],12,39) colrec_mi[1:5,1:5] ## miRNA_ID TCGA-A6-5664-01A-21H-1838-13 TCGA-A6-2683-01A-01T-0822-13 ## 1 hsa-let-7a-1 6959 50288 ## 2 hsa-let-7a-2 6941 50537 ## 3 hsa-let-7a-3 7120 51098 ## 4 hsa-let-7b 31616 143822 ## 5 hsa-let-7c 4211 3943 ## TCGA-D5-6530-01A-11H-1722-13 TCGA-DM-A28G-01A-11H-A16S-13 ## 1 35778 11788 ## 2 35334 11588 ## 3 35980 11885 ## 4 68674 12086 ## 5 605 1171- 1
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简单！
合并CNV
rm(list = ls()) load("G:/tcga/TCGA-CNV/TCGA-COAD_CNV.Rdata") coad <- data load("G:/tcga/TCGA-CNV/TCGA-READ_CNV.Rdata") read <- data colrec_cnv <- rbind(coad,read) head(colrec_cnv) ## GDC_Aliquot Chromosome Start End Num_Probes ## 1 741d4882-3a2c-4862-8402-636e6aebfdc6 1 3301765 247650984 129758 ## 2 741d4882-3a2c-4862-8402-636e6aebfdc6 2 480597 241537572 132218 ## 3 741d4882-3a2c-4862-8402-636e6aebfdc6 3 2170634 25586863 14093 ## 4 741d4882-3a2c-4862-8402-636e6aebfdc6 3 25587626 25587698 3 ## 5 741d4882-3a2c-4862-8402-636e6aebfdc6 3 25588064 197812401 93106 ## 6 741d4882-3a2c-4862-8402-636e6aebfdc6 4 1059384 124538250 69561 ## Segment_Mean Sample ## 1 -0.0019 TCGA-AA-3556-10A-01D-0819-01 ## 2 -0.0007 TCGA-AA-3556-10A-01D-0819-01 ## 3 -0.0009 TCGA-AA-3556-10A-01D-0819-01 ## 4 -1.9166 TCGA-AA-3556-10A-01D-0819-01 ## 5 0.0023 TCGA-AA-3556-10A-01D-0819-01 ## 6 0.0025 TCGA-AA-3556-10A-01D-0819-01- 1
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这个文件稍加整理就可以拿去给gistic用了。
合并SNP
rm(list = ls()) load("G:/tcga/TCGA-SNP/TCGA-READ_SNP.Rdata") read <- data load("G:/tcga/TCGA-SNP/TCGA-COAD_SNP.Rdata") coad <- data colrec_snp <- rbind(coad,read)- 1
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这样以后再分析就可以用合并后的数据了！
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- 原文地址：https://blog.csdn.net/Ayue0616/article/details/127824228
