Integrative analysis workshop with TCGAbiolinks and ELMER - Analysis GUI
Tiago Chedraoui Silva, Simon Coetzee, Dennis Hazelett, Ben Berman, Houtan Noushmehr
2017-08-02
Introduction
In this section, we will perform the same analysis performed using ELMER, but instead of doing it programmatically we will use TCGAbiolinksGUI (Silva et al. 2017).
First we will launch the TCGAbiolinksGUI.
library(TCGAbiolinksGUI)
TCGAbiolinksGUI()Downloading data
Please download this two objects:
Analysis
Create MultiAssayExperiment object
To create the MultiAssayExperiment object go to Integrative analysis/ELMER/Create input data.
Select the DNA methylation object previously downloaded.
Select the gene expression object previously downloaded.
Fill the field Save as: and click on Create MAE object.
The object will be created.
Perform analysis
To perform ELMER analysis go to Integrative analysis/ELMER/Analysis.
Select the MAE data created in the previous section.
Select the groups that will be analysed: Primary solid Tumor and Solid Tissue Normal.
We will identify probes that are hypomethylated in Primary solid Tumor compared to Solid Tissue Normal.
For the significant differently methylated probes identified before we will correlated with the 20 nearest genes. Change the value of the field Number of permutations to 100, Raw P-value cut-off to 0.05 and Empirical P value cut-off to 0.01.
There will be no changes in the step 3.
There will be no changes in the step 4.
Click on Run the analysis.
If the analysis identified significant regulatory TF the results will be saved into an R object.
Visualize results
To visualize the results go to Integrative analysis/ELMER/Visualize results.
Click on Select results and select the object created on the previous section.
You will be able to visualize the correlation between DNA methyation levels and gene expression selecting a pair of gene and probe.
A probe and its near genes.
Or the avarage DNA methylation levels of probes of a Motif vs the expression of a TF.
For each enriched motif you can verify the ranking of sigificances between the correlation of DNA methylation level on the significant paired probes with that motif vs the TF expression (for all human TF).
The enrichement of each motif can be visualized.
You can take a look for a gene which was the probe linked.
You can see the plot and its neraby genes.
It is possible to visualize the table with the significant differently methylated probes.
It is possible to visualize the table with the pairs genes probes that have an negative correlation between DNA methyation levels and gene expression.
It is possible to visualize the table with the enriched motifs.
It is possible to visualize the table with the candidates regulatory TF.
Session Info
sessionInfo()## R version 3.4.1 (2017-06-30)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.2 LTS
##
## Matrix products: default
## BLAS: /usr/local/lib/R/lib/libRblas.so
## LAPACK: /usr/local/lib/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] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] Bioc2017.TCGAbiolinks.ELMER_0.0.0.9000
## [2] bindrcpp_0.2
## [3] MultiAssayExperiment_1.3.20
## [4] dplyr_0.7.2
## [5] DT_0.2
## [6] ELMER_2.0.1
## [7] ELMER.data_2.0.1
##
## loaded via a namespace (and not attached):
## [1] shinydashboard_0.6.1 R.utils_2.5.0
## [3] RSQLite_2.0 AnnotationDbi_1.39.2
## [5] htmlwidgets_0.9 grid_3.4.1
## [7] trimcluster_0.1-2 BiocParallel_1.11.4
## [9] devtools_1.13.2 DESeq_1.29.0
## [11] munsell_0.4.3 codetools_0.2-15
## [13] withr_2.0.0 colorspace_1.3-2
## [15] BiocInstaller_1.27.2 Biobase_2.37.2
## [17] knitr_1.16 stats4_3.4.1
## [19] robustbase_0.92-7 labeling_0.3
## [21] GenomeInfoDbData_0.99.1 KMsurv_0.1-5
## [23] mnormt_1.5-5 hwriter_1.3.2
## [25] bit64_0.9-7 rprojroot_1.2
## [27] downloader_0.4 biovizBase_1.25.1
## [29] ggthemes_3.4.0 EDASeq_2.11.0
## [31] diptest_0.75-7 R6_2.2.2
## [33] doParallel_1.0.10 GenomeInfoDb_1.13.4
## [35] locfit_1.5-9.1 AnnotationFilter_1.1.3
## [37] flexmix_2.3-14 reshape_0.8.6
## [39] bitops_1.0-6 DelayedArray_0.3.19
## [41] assertthat_0.2.0 scales_0.4.1
## [43] nnet_7.3-12 gtable_0.2.0
## [45] ensembldb_2.1.10 rlang_0.1.1
## [47] genefilter_1.59.0 cmprsk_2.2-7
## [49] GlobalOptions_0.0.12 splines_3.4.1
## [51] rtracklayer_1.37.3 lazyeval_0.2.0
## [53] acepack_1.4.1 dichromat_2.0-0
## [55] selectr_0.3-1 broom_0.4.2
## [57] checkmate_1.8.3 yaml_2.1.14
## [59] reshape2_1.4.2 GenomicFeatures_1.29.8
## [61] backports_1.1.0 httpuv_1.3.5
## [63] Hmisc_4.0-3 tools_3.4.1
## [65] psych_1.7.5 ggplot2_2.2.1
## [67] RColorBrewer_1.1-2 BiocGenerics_0.23.0
## [69] Rcpp_0.12.12 plyr_1.8.4
## [71] base64enc_0.1-3 progress_1.1.2
## [73] zlibbioc_1.23.0 purrr_0.2.2.2
## [75] RCurl_1.95-4.8 prettyunits_1.0.2
## [77] ggpubr_0.1.4 rpart_4.1-11
## [79] GetoptLong_0.1.6 viridis_0.4.0
## [81] zoo_1.8-0 S4Vectors_0.15.5
## [83] SummarizedExperiment_1.7.5 ggrepel_0.6.5
## [85] cluster_2.0.6 magrittr_1.5
## [87] data.table_1.10.4 circlize_0.4.1
## [89] survminer_0.4.0 mvtnorm_1.0-6
## [91] whisker_0.3-2 ProtGenerics_1.9.0
## [93] matrixStats_0.52.2 aroma.light_3.7.0
## [95] hms_0.3 mime_0.5
## [97] evaluate_0.10.1 xtable_1.8-2
## [99] XML_3.98-1.9 mclust_5.3
## [101] IRanges_2.11.12 gridExtra_2.2.1
## [103] shape_1.4.2 compiler_3.4.1
## [105] biomaRt_2.33.3 tibble_1.3.3
## [107] R.oo_1.21.0 htmltools_0.3.6
## [109] Formula_1.2-2 tidyr_0.6.3
## [111] geneplotter_1.55.0 DBI_0.7
## [113] matlab_1.0.2 ComplexHeatmap_1.15.0
## [115] MASS_7.3-47 fpc_2.1-10
## [117] BiocStyle_2.5.8 ShortRead_1.35.1
## [119] Matrix_1.2-10 readr_1.1.1
## [121] R.methodsS3_1.7.1 parallel_3.4.1
## [123] Gviz_1.21.1 bindr_0.1
## [125] km.ci_0.5-2 GenomicRanges_1.29.12
## [127] pkgconfig_2.0.1 GenomicAlignments_1.13.4
## [129] foreign_0.8-69 plotly_4.7.1
## [131] xml2_1.1.1 roxygen2_6.0.1
## [133] foreach_1.4.3 annotate_1.55.0
## [135] XVector_0.17.0 rvest_0.3.2
## [137] stringr_1.2.0 VariantAnnotation_1.23.6
## [139] digest_0.6.12 ConsensusClusterPlus_1.41.0
## [141] Biostrings_2.45.3 rmarkdown_1.6
## [143] survMisc_0.5.4 TCGAbiolinks_2.5.6
## [145] htmlTable_1.9 dendextend_1.5.2
## [147] edgeR_3.19.3 curl_2.8.1
## [149] kernlab_0.9-25 shiny_1.0.3
## [151] Rsamtools_1.29.0 commonmark_1.2
## [153] modeltools_0.2-21 rjson_0.2.15
## [155] nlme_3.1-131 jsonlite_1.5
## [157] viridisLite_0.2.0 limma_3.33.7
## [159] BSgenome_1.45.1 lattice_0.20-35
## [161] httr_1.2.1 DEoptimR_1.0-8
## [163] survival_2.41-3 interactiveDisplayBase_1.15.0
## [165] glue_1.1.1 prabclus_2.2-6
## [167] iterators_1.0.8 bit_1.1-12
## [169] class_7.3-14 stringi_1.1.5
## [171] blob_1.1.0 AnnotationHub_2.9.5
## [173] latticeExtra_0.6-28 memoise_1.1.0Bibliography
Silva, Tiago C., Antonio Colaprico, Catharina Olsen, Gianluca Bontempi, Michele Ceccarelli, Benjamin P. Berman, and Houtan Noushmehr. 2017. “TCGAbiolinksGUI: A Graphical User Interface to Analyze Cancer Molecular and Clinical Data.” BioRxiv. Cold Spring Harbor Labs Journals. doi:10.1101/147496.