Contents

1 Instalation

if (!require("BiocManager"))
  install.packages("BiocManager")
BiocManager::install("glmSparseNet")

2 Required Packages

library(dplyr)
library(ggplot2)
library(survival)
library(futile.logger)
library(curatedTCGAData)
library(TCGAutils)
#
library(glmSparseNet)
#
# Some general options for futile.logger the debugging package
.Last.value <- flog.layout(layout.format('[~l] ~m'))
.Last.value <- glmSparseNet:::show.message(FALSE)
# Setting ggplot2 default theme as minimal
theme_set(ggplot2::theme_minimal())

3 Load data

The data is loaded from an online curated dataset downloaded from TCGA using curatedTCGAData bioconductor package and processed.

To accelerate the process we use a very reduced dataset down to around 100 variables only (genes), which is stored as a data object in this package. However, the procedure to obtain the data manually is described in the following chunk.

prad <- curatedTCGAData(diseaseCode = "PRAD", assays = "RNASeq2GeneNorm",
                        version = '1.1.38', dry.run = FALSE))

Build the survival data from the clinical columns.

# keep only solid tumour (code: 01)
prad.primary.solid.tumor <- TCGAutils::TCGAsplitAssays(prad, '01')
xdata.raw <- t(assay(prad.primary.solid.tumor[[1]]))

# Get survival information
ydata.raw <- colData(prad.primary.solid.tumor) %>% as.data.frame %>% 
  # Find max time between all days (ignoring missings)
  dplyr::rowwise() %>%
  dplyr::mutate(
    time = max(days_to_last_followup, days_to_death, na.rm = TRUE)
  ) %>%
  # Keep only survival variables and codes
  dplyr::select(patientID, status = vital_status, time) %>% 
  # Discard individuals with survival time less or equal to 0
  dplyr::filter(!is.na(time) & time > 0) %>% 
  as.data.frame()

# Set index as the patientID
rownames(ydata.raw) <- ydata.raw$patientID

# keep only features that have standard deviation > 0
xdata.raw  <- xdata.raw[TCGAbarcode(rownames(xdata.raw)) %in% 
                          rownames(ydata.raw),]
xdata.raw  <- xdata.raw %>% 
  { (apply(., 2, sd) != 0) } %>% 
  { xdata.raw[, .] } %>% 
  scale

# Order ydata the same as assay
ydata.raw    <- ydata.raw[TCGAbarcode(rownames(xdata.raw)), ]

set.seed(params$seed)
small.subset <- c(geneNames(c('ENSG00000103091', 'ENSG00000064787', 
                              'ENSG00000119915', 'ENSG00000120158', 
                              'ENSG00000114491', 'ENSG00000204176', 
                              'ENSG00000138399'))$external_gene_name, 
                  sample(colnames(xdata.raw), 100)) %>% unique %>% sort

xdata <- xdata.raw[, small.subset[small.subset %in% colnames(xdata.raw)]]
ydata <- ydata.raw %>% dplyr::select(time, status)

4 Fit models

Fit model model penalizing by the hubs using the cross-validation function by cv.glmHub.

set.seed(params$seed)
fitted <- cv.glmHub(xdata, Surv(ydata$time, ydata$status),
                    family  = 'cox',
                    nlambda = 1000,
                    network = 'correlation', 
                    network.options = networkOptions(cutoff = .6, 
                                                     min.degree = .2))

5 Results of Cross Validation

Shows the results of 100 different parameters used to find the optimal value in 10-fold cross-validation. The two vertical dotted lines represent the best model and a model with less variables selected (genes), but within a standard error distance from the best.

plot(fitted)

5.1 Coefficients of selected model from Cross-Validation

Taking the best model described by lambda.min

coefs.v <- coef(fitted, s = 'lambda.min')[,1] %>% { .[. != 0]}
coefs.v %>% { 
  data.frame(ensembl.id  = names(.), 
             gene.name   = geneNames(names(.))$external_gene_name, 
             coefficient = .,
             stringsAsFactors = FALSE)
  } %>%
  arrange(gene.name) %>%
  knitr::kable()
ensembl.id gene.name coefficient
AKAP9 AKAP9 AKAP9 0.2616307
ALPK2 ALPK2 ALPK2 -0.0714527
ATP5G2 ATP5G2 ATP5G2 -0.2575987
C22orf32 C22orf32 C22orf32 -0.2119992
CSNK2A1P CSNK2A1P CSNK2A1P -1.4875518
MYST3 MYST3 MYST3 -1.6177076
NBPF10 NBPF10 NBPF10 0.4507147
PFN1 PFN1 PFN1 0.4161846
SCGB2A2 SCGB2A2 SCGB2A2 0.0749064
SLC25A1 SLC25A1 SLC25A1 -0.8484827
STX4 STX4 STX4 -0.1690185
SYP SYP SYP 0.2425939
TMEM141 TMEM141 TMEM141 -0.8273147
UMPS UMPS UMPS 0.2214068
ZBTB26 ZBTB26 ZBTB26 0.3696515

5.2 Hallmarks of Cancer

geneNames(names(coefs.v)) %>% { hallmarks(.$external_gene_name)$heatmap }
## Error in curl::curl_fetch_memory(url, handle = handle): OpenSSL SSL_connect: SSL_ERROR_SYSCALL in connection to chat.lionproject.net:443 
## Request failed [ERROR]. Retrying in 2 seconds...
## Error in curl::curl_fetch_memory(url, handle = handle): OpenSSL SSL_connect: SSL_ERROR_SYSCALL in connection to chat.lionproject.net:443 
## Request failed [ERROR]. Retrying in 2 seconds...
## Cannot call Hallmark API, please try again later.
## NULL

5.3 Survival curves and Log rank test

separate2GroupsCox(as.vector(coefs.v), 
                   xdata[, names(coefs.v)], 
                   ydata, 
                   plot.title = 'Full dataset', legend.outside = FALSE)
## $pvalue
## [1] 0.001155155
## 
## $plot

## 
## $km
## Call: survfit(formula = survival::Surv(time, status) ~ group, data = prognostic.index.df)
## 
##             n events median 0.95LCL 0.95UCL
## Low risk  249      0     NA      NA      NA
## High risk 248     10   3502    3467      NA

6 Session Info

sessionInfo()
## R version 4.2.0 RC (2022-04-21 r82226)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              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] grid      parallel  stats4    stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] VennDiagram_1.7.3           reshape2_1.4.4             
##  [3] forcats_0.5.1               glmSparseNet_1.15.0        
##  [5] glmnet_4.1-4                Matrix_1.4-1               
##  [7] TCGAutils_1.17.0            curatedTCGAData_1.17.1     
##  [9] MultiAssayExperiment_1.23.0 SummarizedExperiment_1.27.0
## [11] Biobase_2.57.0              GenomicRanges_1.49.0       
## [13] GenomeInfoDb_1.33.0         IRanges_2.31.0             
## [15] S4Vectors_0.35.0            BiocGenerics_0.43.0        
## [17] MatrixGenerics_1.9.0        matrixStats_0.62.0         
## [19] futile.logger_1.4.3         survival_3.3-1             
## [21] ggplot2_3.3.5               dplyr_1.0.8                
## [23] BiocStyle_2.25.0           
## 
## loaded via a namespace (and not attached):
##   [1] backports_1.4.1               AnnotationHub_3.5.0          
##   [3] BiocFileCache_2.5.0           plyr_1.8.7                   
##   [5] splines_4.2.0                 BiocParallel_1.31.0          
##   [7] digest_0.6.29                 foreach_1.5.2                
##   [9] htmltools_0.5.2               magick_2.7.3                 
##  [11] fansi_1.0.3                   magrittr_2.0.3               
##  [13] memoise_2.0.1                 tzdb_0.3.0                   
##  [15] Biostrings_2.65.0             readr_2.1.2                  
##  [17] prettyunits_1.1.1             colorspace_2.0-3             
##  [19] blob_1.2.3                    rvest_1.0.2                  
##  [21] rappdirs_0.3.3                xfun_0.30                    
##  [23] crayon_1.5.1                  RCurl_1.98-1.6               
##  [25] jsonlite_1.8.0                zoo_1.8-10                   
##  [27] iterators_1.0.14              glue_1.6.2                   
##  [29] survminer_0.4.9               GenomicDataCommons_1.21.0    
##  [31] gtable_0.3.0                  zlibbioc_1.43.0              
##  [33] XVector_0.37.0                DelayedArray_0.23.0          
##  [35] car_3.0-12                    shape_1.4.6                  
##  [37] abind_1.4-5                   scales_1.2.0                 
##  [39] futile.options_1.0.1          DBI_1.1.2                    
##  [41] rstatix_0.7.0                 Rcpp_1.0.8.3                 
##  [43] xtable_1.8-4                  progress_1.2.2               
##  [45] bit_4.0.4                     km.ci_0.5-6                  
##  [47] httr_1.4.2                    ellipsis_0.3.2               
##  [49] pkgconfig_2.0.3               XML_3.99-0.9                 
##  [51] farver_2.1.0                  sass_0.4.1                   
##  [53] dbplyr_2.1.1                  utf8_1.2.2                   
##  [55] tidyselect_1.1.2              labeling_0.4.2               
##  [57] rlang_1.0.2                   later_1.3.0                  
##  [59] AnnotationDbi_1.59.0          munsell_0.5.0                
##  [61] BiocVersion_3.16.0            tools_4.2.0                  
##  [63] cachem_1.0.6                  cli_3.3.0                    
##  [65] generics_0.1.2                RSQLite_2.2.12               
##  [67] ExperimentHub_2.5.0           broom_0.8.0                  
##  [69] evaluate_0.15                 stringr_1.4.0                
##  [71] fastmap_1.1.0                 yaml_2.3.5                   
##  [73] knitr_1.38                    bit64_4.0.5                  
##  [75] survMisc_0.5.6                purrr_0.3.4                  
##  [77] KEGGREST_1.37.0               mime_0.12                    
##  [79] formatR_1.12                  xml2_1.3.3                   
##  [81] biomaRt_2.53.0                compiler_4.2.0               
##  [83] filelock_1.0.2                curl_4.3.2                   
##  [85] png_0.1-7                     interactiveDisplayBase_1.35.0
##  [87] ggsignif_0.6.3                tibble_3.1.6                 
##  [89] bslib_0.3.1                   stringi_1.7.6                
##  [91] highr_0.9                     GenomicFeatures_1.49.0       
##  [93] lattice_0.20-45               KMsurv_0.1-5                 
##  [95] vctrs_0.4.1                   pillar_1.7.0                 
##  [97] lifecycle_1.0.1               BiocManager_1.30.17          
##  [99] jquerylib_0.1.4               data.table_1.14.2            
## [101] bitops_1.0-7                  httpuv_1.6.5                 
## [103] rtracklayer_1.57.0            R6_2.5.1                     
## [105] BiocIO_1.7.0                  bookdown_0.26                
## [107] promises_1.2.0.1              gridExtra_2.3                
## [109] codetools_0.2-18              lambda.r_1.2.4               
## [111] assertthat_0.2.1              rjson_0.2.21                 
## [113] withr_2.5.0                   GenomicAlignments_1.33.0     
## [115] Rsamtools_2.13.0              GenomeInfoDbData_1.2.8       
## [117] hms_1.1.1                     tidyr_1.2.0                  
## [119] rmarkdown_2.14                carData_3.0-5                
## [121] ggpubr_0.4.0                  pROC_1.18.0                  
## [123] shiny_1.7.1                   restfulr_0.0.13