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()
ensembl.id gene.name coefficient
(Intercept) (Intercept) (Intercept) -6.8189813
CD5 CD5 AMOTL1 -1.1200445
NLRC4 NLRC4 ATR -1.4434578
PIK3CB PIK3CB B3GALT2 -0.3880002
ZBTB11 ZBTB11 BAG2 -0.3325729
ATR ATR C16orf82 1.2498304
IL2 IL2 CD5 0.6327083
GDF11 GDF11 CIITA -0.2676642
DCP1A DCP1A DCP1A 0.2994599
AMOTL1 AMOTL1 FAM86B1 0.4430643
BAG2 BAG2 FNIP2 -0.1841676
C16orf82 C16orf82 GDF11 0.0396368
FAM86B1 FAM86B1 GNG11 0.2025463
FNIP2 FNIP2 GREM2 0.6101759
MS4A4A MS4A4A GZMB 1.1614779
B3GALT2 B3GALT2 HAX1 -0.0867011
GNG11 GNG11 IL2 3.0659066
NDRG2 NDRG2 MMP28 1.1142519
HAX1 HAX1 MS4A4A -0.1516837
GREM2 GREM2 NDRG2 -0.2014884
CIITA CIITA NLRC4 0.4256103
GZMB GZMB PIK3CB -2.7663574
MMP28 MMP28 ZBTB11 -0.8438024

4.2 Hallmarks of Cancer

geneNames(names(coefs.v)) %>% { hallmarks(.$external_gene_name)$heatmap }
## Error in curl::curl_fetch_memory(url, handle = handle): error:0A000126:SSL routines::unexpected eof while reading
## Request failed [ERROR]. Retrying in 1.1 seconds...
## Error in curl::curl_fetch_memory(url, handle = handle): error:0A000126:SSL routines::unexpected eof while reading
## Request failed [ERROR]. Retrying in 1.1 seconds...
## Cannot call Hallmark API, please try again later.
## NULL

4.3 Accuracy

## [INFO] Misclassified (11)
## [INFO]   * False primary solid tumour: 7
## [INFO]   * False normal              : 4

Histogram of predicted response

ROC curve

## Setting levels: control = Primary Solid Tumor, case = Solid Tissue Normal
## Setting direction: controls < cases
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

5 Session Info

sessionInfo()
## R version 4.3.2 Patched (2023-11-13 r85521)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## 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       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## 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] jsonlite_1.8.8                shape_1.4.6                  
##   [3] magrittr_2.0.3                magick_2.8.2                 
##   [5] GenomicFeatures_1.54.3        farver_2.1.1                 
##   [7] rmarkdown_2.25                BiocIO_1.12.0                
##   [9] zlibbioc_1.48.0               vctrs_0.6.5                  
##  [11] memoise_2.0.1                 Rsamtools_2.18.0             
##  [13] RCurl_1.98-1.14               htmltools_0.5.7              
##  [15] S4Arrays_1.2.0                forcats_1.0.0                
##  [17] BiocBaseUtils_1.4.0           progress_1.2.3               
##  [19] AnnotationHub_3.10.0          lambda.r_1.2.4               
##  [21] curl_5.2.0                    pROC_1.18.5                  
##  [23] SparseArray_1.2.3             sass_0.4.8                   
##  [25] bslib_0.6.1                   plyr_1.8.9                   
##  [27] futile.options_1.0.1          cachem_1.0.8                 
##  [29] GenomicAlignments_1.38.2      mime_0.12                    
##  [31] lifecycle_1.0.4               iterators_1.0.14             
##  [33] pkgconfig_2.0.3               R6_2.5.1                     
##  [35] fastmap_1.1.1                 GenomeInfoDbData_1.2.11      
##  [37] shiny_1.8.0                   digest_0.6.34                
##  [39] colorspace_2.1-0              AnnotationDbi_1.64.1         
##  [41] ExperimentHub_2.10.0          RSQLite_2.3.5                
##  [43] filelock_1.0.3                labeling_0.4.3               
##  [45] fansi_1.0.6                   httr_1.4.7                   
##  [47] abind_1.4-5                   compiler_4.3.2               
##  [49] bit64_4.0.5                   withr_3.0.0                  
##  [51] BiocParallel_1.36.0           DBI_1.2.1                    
##  [53] highr_0.10                    biomaRt_2.58.2               
##  [55] rappdirs_0.3.3                DelayedArray_0.28.0          
##  [57] rjson_0.2.21                  tools_4.3.2                  
##  [59] interactiveDisplayBase_1.40.0 httpuv_1.6.14                
##  [61] glue_1.7.0                    restfulr_0.0.15              
##  [63] promises_1.2.1                grid_4.3.2                   
##  [65] generics_0.1.3                gtable_0.3.4                 
##  [67] tzdb_0.4.0                    hms_1.1.3                    
##  [69] xml2_1.3.6                    utf8_1.2.4                   
##  [71] XVector_0.42.0                BiocVersion_3.18.1           
##  [73] foreach_1.5.2                 pillar_1.9.0                 
##  [75] stringr_1.5.1                 later_1.3.2                  
##  [77] splines_4.3.2                 BiocFileCache_2.10.1         
##  [79] lattice_0.22-5                rtracklayer_1.62.0           
##  [81] bit_4.0.5                     tidyselect_1.2.0             
##  [83] Biostrings_2.70.2             knitr_1.45                   
##  [85] bookdown_0.37                 xfun_0.41                    
##  [87] stringi_1.8.3                 yaml_2.3.8                   
##  [89] evaluate_0.23                 codetools_0.2-19             
##  [91] tibble_3.2.1                  BiocManager_1.30.22          
##  [93] cli_3.6.2                     xtable_1.8-4                 
##  [95] munsell_0.5.0                 jquerylib_0.1.4              
##  [97] Rcpp_1.0.12                   GenomicDataCommons_1.26.0    
##  [99] dbplyr_2.4.0                  png_0.1-8                    
## [101] XML_3.99-0.16.1               parallel_4.3.2               
## [103] ellipsis_0.3.2                readr_2.1.5                  
## [105] blob_1.2.4                    prettyunits_1.2.0            
## [107] bitops_1.0-7                  scales_1.3.0                 
## [109] purrr_1.0.2                   crayon_1.5.2                 
## [111] rlang_1.1.3                   KEGGREST_1.42.0              
## [113] rvest_1.0.3                   formatR_1.14