1. RTCGA包（了解）
数据库式的R包
优点：数据库式，将所有数据打包下载下来，可以在电脑上直接提取数据。
缺点：不是最新的数据。
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | library(RTCGA.rnaseq)#library(RTCGA.miRNASeq)library(RTCGA.clinical)ls("package:RTCGA.rnaseq")#> [1] "ACC.rnaseq" "BLCA.rnaseq" "BRCA.rnaseq" "CESC.rnaseq" #> [5] "CHOL.rnaseq" "COAD.rnaseq" "COADREAD.rnaseq" "DLBC.rnaseq" #> [9] "ESCA.rnaseq" "GBM.rnaseq" "GBMLGG.rnaseq" "HNSC.rnaseq" #> [13] "KICH.rnaseq" "KIPAN.rnaseq" "KIRC.rnaseq" "KIRP.rnaseq" #> [17] "LAML.rnaseq" "LGG.rnaseq" "LIHC.rnaseq" "LUAD.rnaseq" #> [21] "LUSC.rnaseq" "OV.rnaseq" "PAAD.rnaseq" "PCPG.rnaseq" #> [25] "PRAD.rnaseq" "READ.rnaseq" "SARC.rnaseq" "SKCM.rnaseq" #> [29] "STAD.rnaseq" "STES.rnaseq" "TGCT.rnaseq" "THCA.rnaseq" #> [33] "THYM.rnaseq" "UCEC.rnaseq" "UCS.rnaseq" "UVM.rnaseq"ls("package:RTCGA.clinical")#> [1] "ACC.clinical" "BLCA.clinical" "BRCA.clinical" #> [4] "CESC.clinical" "CHOL.clinical" "COAD.clinical" #> [7] "COADREAD.clinical" "DLBC.clinical" "ESCA.clinical" #> [10] "FPPP.clinical" "GBM.clinical" "GBMLGG.clinical" #> [13] "HNSC.clinical" "KICH.clinical" "KIPAN.clinical" #> [16] "KIRC.clinical" "KIRP.clinical" "LAML.clinical" #> [19] "LGG.clinical" "LIHC.clinical" "LUAD.clinical" #> [22] "LUSC.clinical" "MESO.clinical" "OV.clinical" #> [25] "PAAD.clinical" "PCPG.clinical" "PRAD.clinical" #> [28] "READ.clinical" "SARC.clinical" "SKCM.clinical" #> [31] "STAD.clinical" "STES.clinical" "TGCT.clinical" #> [34] "THCA.clinical" "THYM.clinical" "UCEC.clinical" #> [37] "UCS.clinical" "UVM.clinical"#表达矩阵exp2 = t(CHOL.rnaseq)exp2[1:4,1:4]#> [,1] #> bcr_patient_barcode "TCGA-3X-AAV9-01A-72R-A41I-07"#> ?|100130426 "0.0000" #> ?|100133144 " 2.2265" #> ?|100134869 "21.7256" #> [,2] #> bcr_patient_barcode "TCGA-3X-AAVA-01A-11R-A41I-07"#> ?|100130426 "0.0000" #> ?|100133144 " 4.0766" #> ?|100134869 "22.2241" #> [,3] #> bcr_patient_barcode "TCGA-3X-AAVB-01A-31R-A41I-07"#> ?|100130426 "0.0000" #> ?|100133144 " 1.4149" #> ?|100134869 " 4.4196" #> [,4] #> bcr_patient_barcode "TCGA-3X-AAVC-01A-21R-A41I-07"#> ?|100130426 "0.0000" #> ?|100133144 " 0.0000" #> ?|100134869 " 3.8152"dim(exp2)#> [1] 20532 45clinical2 = CHOL.clinical#去除临床信息里全是NA的列tmp = apply(clinical2, 2, function(x){all(is.na(x)|x=="")})clinical2 = clinical2[,!tmp] |
2. TCGAbiolinks（了解）
基于网页选择+GDC下载，自动化整理
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | cancer_type="TCGA-CHOL"library(TCGAbiolinks)#> Warning: package 'TCGAbiolinks' was built under R version 4.0.2clinical <- GDCquery_clinic(project = cancer_type, type = "clinical")clinical[1:4,1:4]#> submitter_id NA. tumor_grade ajcc_pathologic_m#> 1 TCGA-4G-AAZT NA not reported M0#> 2 TCGA-ZH-A8Y2 NA not reported M0#> 3 TCGA-W5-AA2J NA not reported M0#> 4 TCGA-W5-AA31 NA not reported M0dim(clinical)#> [1] 51 158#有些列全是NA，去掉。tmp = apply(clinical, 2, function(x){all(is.na(x)|x=="")})clinical = clinical[,!tmp]query <- GDCquery(project = cancer_type, data.category = "Transcriptome Profiling", data.type = "Gene Expression Quantification", workflow.type = "HTSeq - Counts")if(!dir.exists("GDCdata")) GDCdownload(query, method = "api", files.per.chunk = 50)expdat <- GDCprepare(query = query)#> | | 0% |= |2.222222% ~6 s remaining |== |4.444444% ~5 s remaining |=== |6.666667% ~5 s remaining |==== |8.888889% ~4 s remaining |===== |11.11111% ~4 s remaining |====== |13.33333% ~4 s remaining |======== |15.55556% ~4 s remaining |========= |17.77778% ~4 s remaining |========== | 20% ~4 s remaining |=========== |22.22222% ~4 s remaining |============ |24.44444% ~3 s remaining |============= |26.66667% ~3 s remaining |=============== |28.88889% ~3 s remaining |================ |31.11111% ~3 s remaining |================= |33.33333% ~3 s remaining |================== |35.55556% ~4 s remaining |=================== |37.77778% ~3 s remaining |==================== | 40% ~3 s remaining |===================== |42.22222% ~3 s remaining |======================= |44.44444% ~3 s remaining |======================== |46.66667% ~3 s remaining |========================= |48.88889% ~3 s remaining |========================== |51.11111% ~2 s remaining |=========================== |53.33333% ~2 s remaining |============================ |55.55556% ~2 s remaining |============================== |57.77778% ~2 s remaining |=============================== | 60% ~2 s remaining |================================ |62.22222% ~2 s remaining |================================= |64.44444% ~2 s remaining |================================== |66.66667% ~2 s remaining |=================================== |68.88889% ~1 s remaining |==================================== |71.11111% ~1 s remaining |====================================== |73.33333% ~1 s remaining |======================================= |75.55556% ~1 s remaining |======================================== |77.77778% ~1 s remaining |========================================= | 80% ~1 s remaining |========================================== |82.22222% ~1 s remaining |=========================================== |84.44444% ~1 s remaining |============================================= |86.66667% ~1 s remaining |============================================== |88.88889% ~1 s remaining |=============================================== |91.11111% ~0 s remaining |================================================ |93.33333% ~0 s remaining |================================================= |95.55556% ~0 s remaining |================================================== |97.77778% ~0 s remaining |====================================================|100% ~0 s remaining |====================================================|100% Completed after 5 slibrary(SummarizedExperiment)#> Warning: package 'SummarizedExperiment' was built under R version 4.0.2#> Warning: package 'DelayedArray' was built under R version 4.0.2exp = assay(expdat)exp[1:3,1:3]#> TCGA-4G-AAZT-01A-11R-A41I-07 TCGA-W5-AA2R-01A-11R-A41I-07#> ENSG00000000003 7542 4802#> ENSG00000000005 0 1#> ENSG00000000419 1121 1198#> TCGA-W5-AA34-01A-11R-A41I-07#> ENSG00000000003 8150#> ENSG00000000005 0#> ENSG00000000419 1770#过滤一下dim(exp)#> [1] 56493 45exp = exp[apply(exp, 1, function(x) sum(x > 1) > 9), ]dim(exp)#> [1] 29729 45exp[1:4,1:4]#> TCGA-4G-AAZT-01A-11R-A41I-07 TCGA-W5-AA2R-01A-11R-A41I-07#> ENSG00000000003 7542 4802#> ENSG00000000419 1121 1198#> ENSG00000000457 403 1099#> ENSG00000000460 127 290#> TCGA-W5-AA34-01A-11R-A41I-07 TCGA-W5-AA2G-01A-11R-A41I-07#> ENSG00000000003 8150 1777#> ENSG00000000419 1770 1299#> ENSG00000000457 1202 653#> ENSG00000000460 378 204 |
3. UCSC Xena（重点）
浏览器网站下载： https://xenabrowser.net/datapages/
优点：分类打包，直接下载
缺点：更新较慢，不是最新数据
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | exp3 = read.table("HiSeqV2",header = T,row.names = 1,check.names = F)exp3 = as.matrix(exp3)clinical3 = data.table::fread("CHOL_clinicalMatrix")dim(exp3)#> [1] 20530 45exp3 = exp3[apply(exp3, 1, function(x) sum(x > 1) > 9), ]dim(exp3)#> [1] 17710 45exp3[1:4,1:4]#> TCGA-4G-AAZT-01 TCGA-W5-AA2R-11 TCGA-W5-AA36-01 TCGA-W5-AA2U-01#> ARHGEF10L 10.1826 11.2568 10.6879 10.4950#> HIF3A 5.5601 4.6809 1.4479 4.5270#> RNF10 11.6858 11.9161 12.1831 10.8378#> RNF11 10.6041 11.1185 9.3851 10.6367clinical3[1:4,1:4]#> sampleID _INTEGRATION _PATIENT _cohort#> 1: TCGA-3X-AAV9-01 TCGA-3X-AAV9-01 TCGA-3X-AAV9 TCGA Bile Duct Cancer (CHOL)#> 2: TCGA-3X-AAVA-01 TCGA-3X-AAVA-01 TCGA-3X-AAVA TCGA Bile Duct Cancer (CHOL)#> 3: TCGA-3X-AAVB-01 TCGA-3X-AAVB-01 TCGA-3X-AAVB TCGA Bile Duct Cancer (CHOL)#> 4: TCGA-3X-AAVC-01 TCGA-3X-AAVC-01 TCGA-3X-AAVC TCGA Bile Duct Cancer (CHOL) |
4. GDCRNATools（了解）
基于gdc-client下载并简化整理，完全用R语言完成
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | rm(list = ls())library(GDCRNATools)project <- "TCGA-CHOL"rnadir <- paste(project, 'RNAseq', sep='/')mirdir <- paste(project, 'miRNAs', sep='/')clinicaldir <- paste(project, 'Clinical', sep='/')gdcRNADownload(project.id = project, data.type = 'RNAseq', write.manifest = FALSE, method = 'gdc-client', directory = rnadir)gdcRNADownload(project.id = project, data.type = 'miRNAs', write.manifest = FALSE, method = 'gdc-client', directory = mirdir)gdcClinicalDownload(project.id = project, write.manifest = FALSE, method = 'gdc-client', directory = clinicaldir)#2.临床信息解析----metaMatrix.RNA <- gdcParseMetadata(project.id = project, data.type = 'RNAseq', write.meta = FALSE)metaMatrix.RNA <- gdcFilterDuplicate(metaMatrix.RNA)metaMatrix.RNA <- gdcFilterSampleType(metaMatrix.RNA)dim(metaMatrix.RNA)#> [1] 45 15metaMatrix.MIR <- gdcParseMetadata(project.id = project, data.type = 'miRNAs', write.meta = FALSE)metaMatrix.MIR <- gdcFilterDuplicate(metaMatrix.MIR)metaMatrix.MIR <- gdcFilterSampleType(metaMatrix.MIR)dim(metaMatrix.MIR)#> [1] 45 15clinicalDa <- gdcClinicalMerge(path = clinicaldir, key.info = TRUE)clinicalDa[1:2,1:5]#> age_at_initial_pathologic_diagnosis ethnicity gender#> TCGA-W5-AA2Q 68 NOT HISPANIC OR LATINO MALE#> TCGA-W5-AA2O 57 NOT HISPANIC OR LATINO MALE#> race clinical_stage#> TCGA-W5-AA2Q WHITE NA#> TCGA-W5-AA2O WHITE NA#3.count矩阵----rnaCounts <- gdcRNAMerge(metadata = metaMatrix.RNA, path = rnadir, organized = FALSE, data.type = 'RNAseq')mirCounts <- gdcRNAMerge(metadata = metaMatrix.MIR, path = mirdir, organized = FALSE, data.type = 'miRNAs')dim(rnaCounts)#> [1] 60483 45dim(mirCounts)#> [1] 2588 45 |
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