In general genome analysis carries relatively high demands for compute, RAM, and disk I/O resources (but not for graphics resources, at least not with the software used in this project).
This project also makes use of various resource-intensive R packages.
Nonetheless, the necessary hardware is attainable in high-end consumer-grade systems.
The following systems have been used to execute the R scripts in this project:
-
(verified) System76 "Serval" mobile workstation
- Intel i7-8700k CPU
- 64GB RAM (DDR4-3000, non-ECC)
- Samsung NVMe Pro SSD
- Pop!_OS 18.04 LTS
- RStudio
- R 3.6.3
-
(verified) PowerSpec G460 desktop computer
- Intel i7-8700k CPU
- 64GB RAM (DDR4-3200, non-ECC)
- Intel M.2 SATA SSD
- Samsung NVMe Evo+ SSD
- Windows 10 Pro
- RStudio for Windows
- R 4.0.3
Additional details of these environments are provided in the "Session Information" section below.
Please download all datasets from the following weblinks and store them at the following directory. The script will read all datasets from there.
~/Download/
This project makes use of data files that must be fetched from remote sources. The necessary links are provided below. Note that the .gz files must be unzipped after they are downloaded.
https://pancanatlas.xenahubs.net/download/TCGA_phenotype_denseDataOnlyDownload.tsv.gz
https://toil.xenahubs.net/download/TcgaTargetGtex_RSEM_Hugo_norm_count.gz
download https://toil.xenahubs.net/download/TcgaTargetGTEX_phenotype.txt.gz
https://github.com/a3609640/EIF-analysis/raw/master/LUAD%20Data/RNA.xlsx
https://github.com/a3609640/EIF-analysis/raw/master/LUAD%20Data/Protein.xlsx
https://github.com/a3609640/EIF-analysis/raw/master/LUAD%20Data/Phos.xlsx
https://ndownloader.figshare.com/files/27902091
https://gygi.hms.harvard.edu/data/ccle/protein_quant_current_normalized.csv.gz
The work here depends upon many R libraries.
The following command may be a useful way to install them all:
BiocManager::install(c("clusterProfiler",
"circlize",
"ComplexHeatmap",
"corrplot",
"data.table",
"dendextend",
"descr",
"devtools",
"dplyr",
"EnvStats",
"eulerr",
"factoextra",
"FactoMineR",
"forcats",
"forestmodel",
"forestplot",
"Hmisc",
"ggfortify",
"ggplot2",
"ggpubr",
"ggsignif",
"ggthemes",
"glmnet",
"gplots",
"grid",
"gridExtra",
"igraph",
"KEGG.db",
"lemon",
"limma",
"MASS",
"missMDA",
"org.Hs.eg.db",
"parallel",
"pca3d",
"pheatmap",
"plotmo",
"RColorBrewer",
"ReactomePA",
"readr",
"readxl",
"reshape2",
"rgl",
"scales",
"stringr",
"survival",
"survivalAnalysis",
"survMisc",
"survminer",
"tidyverse",
"vcd",
"vip"
), update = TRUE, ask = FALSE)
This is also necessary:
devtools::install_github("zeehio/facetscales")
The script will save all the generated plots into the following output folder. Please generate the output directory.
mkdir -p ~/Documents/EIF_output/{CNV,Expression,PCA,PCA/All,PCA/GTEX,PCA/TCGA,PCA/Lung,KM,Cox,Heatmap,CPTAC}
- Install R (a version verified in one of the systems listed above) and RStudio at the recommended Systems.
- Download all datasets from the listed web links above and store them under the following directory.
~/Download/
- Install all the R packages listed above.
- Create output directories
~/Documents/EIF_output/{CNV,Expression,PCA,PCA/All,PCA/GTEX,PCA/TCGA,PCA/Lung,KM,Cox,Heatmap,CPTAC}
- Download and open the
EIFanalysisv4.Rfile in RStudio, and run it with the "source" button. - The
EIFanalysisv4.Rwill produce all the figures from the EIF manuscript and store them in the output folders.
If the root directory paths ~/Download and ~/Documents/EIF_output do not suit, they may be adjusted trivially in these lines near the top of the script:
data.file.directory <- "~/Downloads"
output.directory <- "~/Documents/EIF_output"
Note that you must still pre-create all output subdirectories, under whatever parent output directory you choose.
R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Pop!_OS 18.04 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[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] grid parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] vip_0.2.2 vcd_1.4-8 stringr_1.4.0
[4] purrr_0.3.4 tidyr_1.1.2 tibble_3.0.4
[7] tidyverse_1.3.0 survminer_0.4.8 survMisc_0.5.5
[10] survivalAnalysis_0.1.3 scales_1.1.1 rgl_0.100.54
[13] reshape2_1.4.4 readxl_1.3.1 readr_1.4.0
[16] ReactomePA_1.28.0 RColorBrewer_1.1-2 plotmo_3.6.0
[19] TeachingDemos_2.12 plotrix_3.7-8 pheatmap_1.0.12
[22] pca3d_0.10.2 org.Hs.eg.db_3.8.2 nortest_1.0-4
[25] missMDA_1.17 limma_3.40.6 lemon_0.4.5
[28] KEGG.db_3.2.3 igraph_1.2.6 gridExtra_2.3
[31] gplots_3.1.0 glmnet_4.0-2 Matrix_1.2-18
[34] ggthemes_4.2.0 ggsignif_0.6.0 ggpubr_0.4.0
[37] ggfortify_0.4.11 Hmisc_4.4-1 Formula_1.2-4
[40] survival_3.2-7 lattice_0.20-41 forestplot_1.10
[43] checkmate_2.0.0 magrittr_1.5 forestmodel_0.6.2
[46] forcats_0.5.0 FactoMineR_2.3 factoextra_1.0.7
[49] ggplot2_3.3.2 facetscales_0.1.0.9000 eulerr_6.1.0
[52] EnvStats_2.3.1 dplyr_1.0.2 descr_1.1.4
[55] dendextend_1.14.0 data.table_1.13.2 corrplot_0.84
[58] ComplexHeatmap_2.0.0 circlize_0.4.10 clusterProfiler_3.12.0
[61] car_3.0-10 carData_3.0-4 AnnotationDbi_1.46.1
[64] IRanges_2.18.3 S4Vectors_0.22.1 Biobase_2.44.0
[67] BiocGenerics_0.30.0
loaded via a namespace (and not attached):
[1] rappdirs_0.3.1 bit64_4.0.5 knitr_1.30
[4] rpart_4.1-15 doParallel_1.0.16 generics_0.0.2
[7] cowplot_1.1.0 RSQLite_2.2.1 mice_3.11.0
[10] europepmc_0.4 bit_4.0.4 enrichplot_1.4.0
[13] webshot_0.5.2 xml2_1.3.2 lubridate_1.7.9
[16] httpuv_1.5.4 assertthat_0.2.1 viridis_0.5.1
[19] xfun_0.18 hms_0.5.3 promises_1.1.1
[22] fansi_0.4.1 progress_1.2.2 caTools_1.18.0
[25] dbplyr_1.4.4 km.ci_0.5-2 DBI_1.1.0
[28] htmlwidgets_1.5.2 ellipsis_0.3.1 crosstalk_1.1.0.1
[31] backports_1.1.10 vctrs_0.3.4 abind_1.4-5
[34] withr_2.3.0 ggforce_0.3.2 triebeard_0.3.0
[37] prettyunits_1.1.1 cluster_2.1.0 DOSE_3.10.2
[40] crayon_1.3.4 ellipse_0.4.2 labeling_0.4.2
[43] pkgconfig_2.0.3 tweenr_1.0.1 nnet_7.3-14
[46] rlang_0.4.8 lifecycle_0.2.0 miniUI_0.1.1.1
[49] modelr_0.1.8 cellranger_1.1.0 polyclip_1.10-0
[52] lmtest_0.9-38 graph_1.62.0 urltools_1.7.3
[55] KMsurv_0.1-5 zoo_1.8-8 reprex_0.3.0
[58] base64enc_0.1-3 ggridges_0.5.2 GlobalOptions_0.1.2
[61] png_0.1-7 viridisLite_0.3.0 rjson_0.2.20
[64] bitops_1.0-6 KernSmooth_2.23-17 blob_1.2.1
[67] shape_1.4.5 qvalue_2.16.0 manipulateWidget_0.10.1
[70] jpeg_0.1-8.1 rstatix_0.6.0 gridGraphics_0.5-0
[73] reactome.db_1.68.0 leaps_3.1 memoise_1.1.0
[76] graphite_1.30.0 plyr_1.8.6 compiler_3.6.3
[79] clue_0.3-57 cli_2.1.0 htmlTable_2.1.0
[82] MASS_7.3-51.6 tidyselect_1.1.0 stringi_1.5.3
[85] GOSemSim_2.10.0 latticeExtra_0.6-29 ggrepel_0.8.2
[88] fastmatch_1.1-0 tools_3.6.3 rio_0.5.16
[91] rstudioapi_0.11 foreach_1.5.1 foreign_0.8-76
[94] scatterplot3d_0.3-41 farver_2.0.3 ggraph_2.0.3
[97] digest_0.6.26 rvcheck_0.1.8 BiocManager_1.30.10
[100] shiny_1.5.0 Rcpp_1.0.5 broom_0.7.2
[103] later_1.1.0.1 httr_1.4.2 colorspace_1.4-1
[106] polylabelr_0.2.0 rvest_0.3.6 fs_1.5.0
[109] splines_3.6.3 graphlayouts_0.7.0 ggplotify_0.0.5
[112] xtable_1.8-4 jsonlite_1.7.1 tidygraph_1.2.0
[115] UpSetR_1.4.0 flashClust_1.01-2 R6_2.4.1
[118] pillar_1.4.6 htmltools_0.5.0 mime_0.9
[121] glue_1.4.2 fastmap_1.0.1 BiocParallel_1.18.1
[124] codetools_0.2-16 fgsea_1.10.1 mvtnorm_1.1-1
[127] curl_4.3 gtools_3.8.2 zip_2.1.1
[130] GO.db_3.8.2 openxlsx_4.2.2 munsell_0.5.0
[133] DO.db_2.9 GetoptLong_1.0.4 iterators_1.0.13
[136] haven_2.3.1 gtable_0.3.0 tidytidbits_0.2.2
> sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] grid parallel stats4 stats graphics grDevices utils datasets
[9] methods base
other attached packages:
[1] vip_0.2.2 vcd_1.4-8 stringr_1.4.0
[4] purrr_0.3.4 tidyr_1.1.2 tibble_3.0.4
[7] tidyverse_1.3.0 survminer_0.4.8 survMisc_0.5.5
[10] survivalAnalysis_0.1.3 scales_1.1.1 rgl_0.100.54
[13] reshape2_1.4.4 readxl_1.3.1 readr_1.4.0
[16] ReactomePA_1.32.0 RColorBrewer_1.1-2 plotmo_3.6.0
[19] TeachingDemos_2.12 plotrix_3.7-8 pheatmap_1.0.12
[22] pca3d_0.10.2 org.Hs.eg.db_3.11.4 nortest_1.0-4
[25] missMDA_1.17 limma_3.44.3 lemon_0.4.5
[28] KEGG.db_3.2.4 igraph_1.2.6 gridExtra_2.3
[31] gplots_3.1.0 glmnet_4.0-2 Matrix_1.2-18
[34] ggthemes_4.2.0 ggsignif_0.6.0 ggpubr_0.4.0
[37] ggfortify_0.4.11 Hmisc_4.4-1 Formula_1.2-4
[40] survival_3.2-7 lattice_0.20-41 forestplot_1.10
[43] checkmate_2.0.0 magrittr_1.5 forestmodel_0.6.2
[46] forcats_0.5.0 FactoMineR_2.3 factoextra_1.0.7
[49] ggplot2_3.3.2 facetscales_0.1.0.9000 eulerr_6.1.0
[52] EnvStats_2.4.0 dplyr_1.0.2 descr_1.1.4
[55] dendextend_1.14.0 data.table_1.13.2 corrplot_0.84
[58] ComplexHeatmap_2.4.3 circlize_0.4.10 clusterProfiler_3.16.1
[61] car_3.0-10 carData_3.0-4 AnnotationDbi_1.50.3
[64] IRanges_2.22.2 S4Vectors_0.26.1 Biobase_2.48.0
[67] BiocGenerics_0.34.0
loaded via a namespace (and not attached):
[1] rappdirs_0.3.1 bit64_4.0.5 knitr_1.30
[4] rpart_4.1-15 doParallel_1.0.16 generics_0.0.2
[7] cowplot_1.1.0 RSQLite_2.2.1 mice_3.11.0
[10] europepmc_0.4 bit_4.0.4 enrichplot_1.8.1
[13] lubridate_1.7.9 webshot_0.5.2 xml2_1.3.2
[16] httpuv_1.5.4 assertthat_0.2.1 viridis_0.5.1
[19] xfun_0.18 hms_0.5.3 promises_1.1.1
[22] fansi_0.4.1 progress_1.2.2 caTools_1.18.0
[25] dbplyr_1.4.4 km.ci_0.5-2 DBI_1.1.0
[28] htmlwidgets_1.5.2 ellipsis_0.3.1 crosstalk_1.1.0.1
[31] backports_1.1.10 vctrs_0.3.4 abind_1.4-5
[34] withr_2.3.0 ggforce_0.3.2 triebeard_0.3.0
[37] prettyunits_1.1.1 cluster_2.1.0 DOSE_3.14.0
[40] crayon_1.3.4 ellipse_0.4.2 labeling_0.4.2
[43] pkgconfig_2.0.3 tweenr_1.0.1 nnet_7.3-14
[46] rlang_0.4.8 lifecycle_0.2.0 miniUI_0.1.1.1
[49] downloader_0.4 modelr_0.1.8 cellranger_1.1.0
[52] polyclip_1.10-0 lmtest_0.9-38 graph_1.66.0
[55] urltools_1.7.3 KMsurv_0.1-5 zoo_1.8-8
[58] reprex_0.3.0 base64enc_0.1-3 ggridges_0.5.2
[61] GlobalOptions_0.1.2 png_0.1-7 viridisLite_0.3.0
[64] rjson_0.2.20 bitops_1.0-6 KernSmooth_2.23-17
[67] blob_1.2.1 shape_1.4.5 qvalue_2.20.0
[70] manipulateWidget_0.10.1 jpeg_0.1-8.1 rstatix_0.6.0
[73] gridGraphics_0.5-0 reactome.db_1.70.0 leaps_3.1
[76] memoise_1.1.0 graphite_1.34.0 plyr_1.8.6
[79] compiler_4.0.3 scatterpie_0.1.5 clue_0.3-57
[82] cli_2.1.0 htmlTable_2.1.0 MASS_7.3-53
[85] tidyselect_1.1.0 stringi_1.5.3 yaml_2.2.1
[88] GOSemSim_2.14.2 latticeExtra_0.6-29 ggrepel_0.8.2
[91] fastmatch_1.1-0 tools_4.0.3 rio_0.5.16
[94] rstudioapi_0.11 foreach_1.5.1 foreign_0.8-80
[97] scatterplot3d_0.3-41 farver_2.0.3 ggraph_2.0.3
[100] digest_0.6.26 rvcheck_0.1.8 BiocManager_1.30.10
[103] shiny_1.5.0 Rcpp_1.0.5 broom_0.7.2
[106] later_1.1.0.1 httr_1.4.2 colorspace_1.4-1
[109] polylabelr_0.2.0 rvest_0.3.6 fs_1.5.0
[112] splines_4.0.3 graphlayouts_0.7.0 ggplotify_0.0.5
[115] xtable_1.8-4 jsonlite_1.7.1 tidygraph_1.2.0
[118] flashClust_1.01-2 R6_2.4.1 pillar_1.4.6
[121] htmltools_0.5.0 mime_0.9 glue_1.4.2
[124] fastmap_1.0.1 BiocParallel_1.22.0 codetools_0.2-16
[127] fgsea_1.14.0 mvtnorm_1.1-1 curl_4.3
[130] gtools_3.8.2 zip_2.1.1 GO.db_3.11.4
[133] openxlsx_4.2.2 munsell_0.5.0 DO.db_2.9
[136] GetoptLong_1.0.4 iterators_1.0.13 haven_2.3.1
[139] gtable_0.3.0 tidytidbits_0.2.2