Contents

0.1 Instalation

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

1 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())

2 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 107 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.

brca <- tryCatch({
  curatedTCGAData(
    diseaseCode = "BRCA",
    assays = "RNASeq2GeneNorm",
    version = "1.1.38", 
    dry.run = FALSE
  )
}, error = function(err) {
  NULL
})
brca <- curatedTCGAData(diseaseCode = "BRCA", assays = "RNASeq2GeneNorm",
                        version = "1.1.38", dry.run = FALSE)
brca <- TCGAutils::TCGAsplitAssays(brca, c('01','11'))
xdata.raw <- t(cbind(assay(brca[[1]]), assay(brca[[2]])))

# Get matches between survival and assay data
class.v        <- TCGAbiospec(rownames(xdata.raw))$sample_definition %>% factor
names(class.v) <- rownames(xdata.raw)

# keep features with standard deviation > 0
xdata.raw <- xdata.raw %>% 
  { (apply(., 2, sd) != 0) } %>% 
  { xdata.raw[, .] } %>%
  scale()

set.seed(params$seed)
small.subset <- c('CD5', 'CSF2RB', 'HSF1', 'IRGC', 'LRRC37A6P', 'NEUROG2', 
                  'NLRC4', 'PDE11A', 'PIK3CB', 'QARS', 'RPGRIP1L', 'SDC1', 
                  'TMEM31', 'YME1L1', 'ZBTB11', 
                  sample(colnames(xdata.raw), 100))

xdata <- xdata.raw[, small.subset[small.subset %in% colnames(xdata.raw)]]
ydata <- class.v

3 Fit models

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

fitted <- cv.glmHub(xdata, ydata, 
                    family  = 'binomial',
                    network = 'correlation', 
                    nlambda = 1000,
                    network.options = networkOptions(cutoff = .6, 
                                                     min.degree = .2))

4 Results of Cross Validation

Shows the results of 1000 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)

4.1 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()

4.2 Hallmarks of Cancer

geneNames(names(coefs.v)) %>% { hallmarks(.$external_gene_name)$heatmap }

4.3 Accuracy

Histogram of predicted response

ROC curve

5 Session Info

sessionInfo()
## R version 4.3.2 Patched (2023-11-01 r85457)
## Platform: x86_64-apple-darwin20 (64-bit)
## Running under: macOS Monterey 12.7.1
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
## 
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: America/New_York
## tzcode source: internal
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] glmSparseNet_1.20.1         glmnet_4.1-8               
##  [3] Matrix_1.6-5                TCGAutils_1.22.2           
##  [5] curatedTCGAData_1.24.0      MultiAssayExperiment_1.28.0
##  [7] SummarizedExperiment_1.32.0 Biobase_2.62.0             
##  [9] GenomicRanges_1.54.1        GenomeInfoDb_1.38.5        
## [11] IRanges_2.36.0              S4Vectors_0.40.2           
## [13] BiocGenerics_0.48.1         MatrixGenerics_1.14.0      
## [15] matrixStats_1.2.0           futile.logger_1.4.3        
## [17] survival_3.5-7              ggplot2_3.4.4              
## [19] dplyr_1.1.4                 BiocStyle_2.30.0           
## 
## loaded via a namespace (and not attached):
##   [1] DBI_1.2.1                     bitops_1.0-7                 
##   [3] formatR_1.14                  biomaRt_2.58.2               
##   [5] rlang_1.1.3                   magrittr_2.0.3               
##   [7] compiler_4.3.2                RSQLite_2.3.5                
##   [9] GenomicFeatures_1.54.3        png_0.1-8                    
##  [11] vctrs_0.6.5                   rvest_1.0.3                  
##  [13] stringr_1.5.1                 shape_1.4.6                  
##  [15] pkgconfig_2.0.3               crayon_1.5.2                 
##  [17] fastmap_1.1.1                 dbplyr_2.4.0                 
##  [19] XVector_0.42.0                ellipsis_0.3.2               
##  [21] utf8_1.2.4                    Rsamtools_2.18.0             
##  [23] promises_1.2.1                rmarkdown_2.25               
##  [25] tzdb_0.4.0                    purrr_1.0.2                  
##  [27] bit_4.0.5                     xfun_0.41                    
##  [29] zlibbioc_1.48.0               cachem_1.0.8                 
##  [31] jsonlite_1.8.8                progress_1.2.3               
##  [33] blob_1.2.4                    later_1.3.2                  
##  [35] DelayedArray_0.28.0           BiocParallel_1.36.0          
##  [37] interactiveDisplayBase_1.40.0 parallel_4.3.2               
##  [39] prettyunits_1.2.0             R6_2.5.1                     
##  [41] stringi_1.8.3                 bslib_0.6.1                  
##  [43] rtracklayer_1.62.0            jquerylib_0.1.4              
##  [45] iterators_1.0.14              Rcpp_1.0.12                  
##  [47] bookdown_0.37                 knitr_1.45                   
##  [49] readr_2.1.5                   httpuv_1.6.14                
##  [51] splines_4.3.2                 tidyselect_1.2.0             
##  [53] abind_1.4-5                   yaml_2.3.8                   
##  [55] codetools_0.2-19              curl_5.2.0                   
##  [57] lattice_0.22-5                tibble_3.2.1                 
##  [59] shiny_1.8.0                   withr_3.0.0                  
##  [61] KEGGREST_1.42.0               evaluate_0.23                
##  [63] lambda.r_1.2.4                BiocFileCache_2.10.1         
##  [65] xml2_1.3.6                    ExperimentHub_2.10.0         
##  [67] Biostrings_2.70.2             pillar_1.9.0                 
##  [69] BiocManager_1.30.22           filelock_1.0.3               
##  [71] foreach_1.5.2                 generics_0.1.3               
##  [73] RCurl_1.98-1.14               BiocVersion_3.18.1           
##  [75] hms_1.1.3                     munsell_0.5.0                
##  [77] scales_1.3.0                  xtable_1.8-4                 
##  [79] glue_1.7.0                    tools_4.3.2                  
##  [81] BiocIO_1.12.0                 AnnotationHub_3.10.0         
##  [83] GenomicAlignments_1.38.2      forcats_1.0.0                
##  [85] XML_3.99-0.16.1               grid_4.3.2                   
##  [87] AnnotationDbi_1.64.1          colorspace_2.1-0             
##  [89] GenomeInfoDbData_1.2.11       restfulr_0.0.15              
##  [91] cli_3.6.2                     rappdirs_0.3.3               
##  [93] futile.options_1.0.1          fansi_1.0.6                  
##  [95] GenomicDataCommons_1.26.0     S4Arrays_1.2.0               
##  [97] gtable_0.3.4                  sass_0.4.8                   
##  [99] digest_0.6.34                 SparseArray_1.2.3            
## [101] rjson_0.2.21                  memoise_2.0.1                
## [103] htmltools_0.5.7               lifecycle_1.0.4              
## [105] httr_1.4.7                    mime_0.12                    
## [107] bit64_4.0.5