TCGA.pipe is a function for easily downloading TCGA data from GDC using TCGAbiolinks package (Colaprico et al. 2016) and performing all the analyses in ELMER. For illustration purpose, we skip the downloading step. The user can use the getTCGA function to download TCGA data or use TCGA.pipe by including “download” in the analysis option.
The following command will do distal DNA methylation analysis and predict putative target genes, motif analysis and identify regulatory transcription factors.
TCGA.pipe("LUSC",
wd = "./ELMER.example",
cores = parallel::detectCores()/2,
mode = "unsupervised"
permu.size = 300,
Pe = 0.01,
analysis = c("distal.probes","diffMeth","pair","motif","TF.search"),
diff.dir = "hypo",
rm.chr = paste0("chr",c("X","Y")))In this new version we added the argument mode in the TCGA.pipe function. This will automatically set the minSubgroupFrac to the following values:
Modes available:
unsupervised:
minSubgroupFrac = 0.2 in get.diff.meth)minSubgroupFrac = 0.4 in get.pairs and get.TFs functions)supervised:
The unsupervised mode should be used when want to be able to detect a specific (possibly unknown) molecular subtype among tumor; these subtypes often make up only a minority of samples, and 20% was chosen as a lower bound for the purposes of statistical power. If you are using pre-defined group labels, such as treated replicates vs. untreated replicated, use supervised mode (all samples),
For more information please read the analysis section of the vignette.
We add in TCGA.pipe function (download step) the option to identify mutant samples to perform WT vs Mutant analysis. It will download open MAF file from GDC database (Grossman et al. 2016), select a gene and identify the which are the mutant samples based on the following classification: (it can be changed using the atgument mutant_variant_classification).
| Argument | Description |
|---|---|
| Frame_Shift_Del | Mutant |
| Frame_Shift_Ins | Mutant |
| Missense_Mutation | Mutant |
| Nonsense_Mutation | Mutant |
| Splice_Site | Mutant |
| In_Frame_Del | Mutant |
| In_Frame_Ins | Mutant |
| Translation_Start_Site | Mutant |
| Nonstop_Mutation | Mutant |
| Silent | WT |
| 3’UTR | WT |
| 5’UTR | WT |
| 3’Flank | WT |
| 5’Flank | WT |
| IGR1 (intergenic region) | WT |
| Intron | WT |
| RNA | WT |
| Target_region | WT |
The arguments to be used are below:
TCGA.pipe mutation arguments
| Argument | Description |
|---|---|
| genes | List of genes for which mutations will be verified. A column in the MAE with the name of the gene will be created with two groups WT (tumor samples without mutation), MUT (tumor samples w/ mutation), NA (not tumor samples) |
| mutant_variant_classification | List of GDC variant classification from MAF files to consider a samples mutant. Only used when argument gene is set. |
| group.col | A column defining the groups of the sample. You can view the available columns using: colnames(MultiAssayExperiment::colData(data)). |
| group1 | A group from group.col. ELMER will run group1 vs group2. That means, if direction is hyper, get probes hypermethylated in group 1 compared to group 2. |
| group2 | A group from group.col. ELMER will run group1 vs group2. That means, if direction is hyper, get probes hypermethylated in group 1 compared to group 2. |
Here is an example we TCGA-LUSC data is downloaded and we will compare TP53 Mutant vs TP53 WT samples.
TCGA.pipe("LUSC",
wd = "./ELMER.example",
cores = parallel::detectCores()/2,
mode = "supervised"
genes = "TP53",
group.col = "TP53",
group1 = "Mutant",
group2 = "WT",
permu.size = 300,
Pe = 0.01,
analysis = c("download","diffMeth","pair","motif","TF.search"),
diff.dir = "hypo",
rm.chr = paste0("chr",c("X","Y")))sessionInfo()## R version 3.4.3 (2017-11-30)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.6-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.6-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] SummarizedExperiment_1.8.1 DelayedArray_0.4.1
## [3] matrixStats_0.52.2 Biobase_2.38.0
## [5] GenomicRanges_1.30.1 GenomeInfoDb_1.14.0
## [7] IRanges_2.12.0 S4Vectors_0.16.0
## [9] BiocGenerics_0.24.0 bindrcpp_0.2
## [11] ELMER_2.2.7 MultiAssayExperiment_1.4.4
## [13] BiocStyle_2.6.1 dplyr_0.7.4
## [15] DT_0.2 ELMER.data_2.2.2
##
## loaded via a namespace (and not attached):
## [1] shinydashboard_0.6.1 R.utils_2.6.0
## [3] RSQLite_2.0 AnnotationDbi_1.40.0
## [5] htmlwidgets_0.9 grid_3.4.3
## [7] BiocParallel_1.12.0 devtools_1.13.4
## [9] DESeq_1.30.0 munsell_0.4.3
## [11] codetools_0.2-15 withr_2.1.1
## [13] colorspace_1.3-2 BiocInstaller_1.28.0
## [15] knitr_1.17 rstudioapi_0.7
## [17] labeling_0.3 GenomeInfoDbData_1.0.0
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## [39] ensembldb_2.2.0 rlang_0.1.6
## [41] genefilter_1.60.0 cmprsk_2.2-7
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## [53] reshape2_1.4.3 GenomicFeatures_1.30.0
## [55] backports_1.1.2 httpuv_1.3.5
## [57] Hmisc_4.1-0 RMySQL_0.10.13
## [59] tools_3.4.3 psych_1.7.8
## [61] ggplot2_2.2.1 RColorBrewer_1.1-2
## [63] Rcpp_0.12.14 plyr_1.8.4
## [65] base64enc_0.1-3 progress_1.1.2
## [67] zlibbioc_1.24.0 purrr_0.2.4
## [69] RCurl_1.95-4.8 prettyunits_1.0.2
## [71] ggpubr_0.1.6 rpart_4.1-11
## [73] GetoptLong_0.1.6 zoo_1.8-0
## [75] ggrepel_0.7.0 cluster_2.0.6
## [77] magrittr_1.5 data.table_1.10.4-3
## [79] circlize_0.4.3 survminer_0.4.1
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## [83] hms_0.4.0 mime_0.5
## [85] evaluate_0.10.1 xtable_1.8-2
## [87] XML_3.98-1.9 gridExtra_2.3
## [89] shape_1.4.3 testthat_2.0.0
## [91] compiler_3.4.3 biomaRt_2.34.1
## [93] tibble_1.4.1 R.oo_1.21.0
## [95] htmltools_0.3.6 mgcv_1.8-22
## [97] Formula_1.2-2 tidyr_0.7.2
## [99] geneplotter_1.56.0 DBI_0.7
## [101] matlab_1.0.2 ComplexHeatmap_1.17.1
## [103] ShortRead_1.36.0 Matrix_1.2-12
## [105] readr_1.1.1 R.methodsS3_1.7.1
## [107] Gviz_1.22.2 bindr_0.1
## [109] pkgconfig_2.0.1 km.ci_0.5-2
## [111] GenomicAlignments_1.14.1 foreign_0.8-69
## [113] plotly_4.7.1 xml2_1.1.1
## [115] roxygen2_6.0.1 foreach_1.4.4
## [117] annotate_1.56.1 XVector_0.18.0
## [119] rvest_0.3.2 stringr_1.2.0
## [121] VariantAnnotation_1.24.4 digest_0.6.13
## [123] ConsensusClusterPlus_1.42.0 Biostrings_2.46.0
## [125] rmarkdown_1.8 TCGAbiolinks_2.6.7
## [127] survMisc_0.5.4 htmlTable_1.11.0
## [129] edgeR_3.20.3 curl_3.1
## [131] shiny_1.0.5 Rsamtools_1.30.0
## [133] commonmark_1.4 rjson_0.2.15
## [135] nlme_3.1-131 jsonlite_1.5
## [137] viridisLite_0.2.0 limma_3.34.5
## [139] BSgenome_1.46.0 pillar_1.0.1
## [141] lattice_0.20-35 httr_1.3.1
## [143] survival_2.41-3 interactiveDisplayBase_1.16.0
## [145] glue_1.2.0 iterators_1.0.9
## [147] bit_1.1-12 stringi_1.1.6
## [149] blob_1.1.0 AnnotationHub_2.10.1
## [151] latticeExtra_0.6-28 memoise_1.1.0Colaprico, Antonio, Tiago C. Silva, Catharina Olsen, Luciano Garofano, Claudia Cava, Davide Garolini, Thais S. Sabedot, et al. 2016. “TCGAbiolinks: An R/Bioconductor Package for Integrative Analysis of Tcga Data.” Nucleic Acids Research 44 (8): e71. doi:10.1093/nar/gkv1507.
Grossman, Robert L, Allison P Heath, Vincent Ferretti, Harold E Varmus, Douglas R Lowy, Warren A Kibbe, and Louis M Staudt. 2016. “Toward a Shared Vision for Cancer Genomic Data.” New England Journal of Medicine 375 (12). Mass Medical Soc: 1109–12.