Instalation

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

Required Packages

library(dplyr)
library(ggplot2)
library(survival)
library(futile.logger)
library(curatedTCGAData)
library(MultiAssayExperiment)
library(TCGAutils)
#
library(glmSparseNet)
#
# Some general options for futile.logger the debugging package
flog.layout(layout.format("[~l] ~m"))
options(
    "glmSparseNet.show_message" = FALSE,
    "glmSparseNet.base_dir" = withr::local_tempdir()
)
# Setting ggplot2 default theme as minimal
theme_set(ggplot2::theme_minimal())

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

brca <- TCGAutils::TCGAsplitAssays(brca, c("01", "11"))
xdataRaw <- t(cbind(assay(brca[[1]]), assay(brca[[2]])))

# Get matches between survival and assay data
classV <- TCGAbiospec(rownames(xdataRaw))$sample_definition |> factor()
names(classV) <- rownames(xdataRaw)

# keep features with standard deviation > 0
xdataRaw <- xdataRaw[, apply(xdataRaw, 2, sd) != 0] |>
    scale()

set.seed(params$seed)
smallSubset <- c(
    "CD5", "CSF2RB", "HSF1", "IRGC", "LRRC37A6P", "NEUROG2",
    "NLRC4", "PDE11A", "PIK3CB", "QARS", "RPGRIP1L", "SDC1",
    "TMEM31", "YME1L1", "ZBTB11",
    sample(colnames(xdataRaw), 100)
)

xdata <- xdataRaw[, smallSubset[smallSubset %in% colnames(xdataRaw)]]
ydata <- classV

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,
    options = networkOptions(
        cutoff = .6,
        minDegree = .2
    )
)

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)

coefsCV <- Filter(function(.x) .x != 0, coef(fitted, s = "lambda.min")[, 1])
data.frame(
    ensembl.id = names(coefsCV),
    gene.name = geneNames(names(coefsCV))$external_gene_name,
    coefficient = coefsCV,
    stringsAsFactors = FALSE
) |>
    arrange(gene.name) |>
    knitr::kable()

## [INFO] Misclassified (11)

## [INFO]   * False primary solid tumour: 7

## [INFO]   * False normal              : 4

## 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.

Session Info

sessionInfo()

## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## 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       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] glmSparseNet_1.25.0         TCGAutils_1.25.1           
##  [3] curatedTCGAData_1.27.1      MultiAssayExperiment_1.31.5
##  [5] SummarizedExperiment_1.35.5 Biobase_2.67.0             
##  [7] GenomicRanges_1.57.2        GenomeInfoDb_1.41.2        
##  [9] IRanges_2.39.2              S4Vectors_0.43.2           
## [11] BiocGenerics_0.53.0         MatrixGenerics_1.17.1      
## [13] matrixStats_1.4.1           futile.logger_1.4.3        
## [15] survival_3.7-0              ggplot2_3.5.1              
## [17] dplyr_1.1.4                 BiocStyle_2.35.0           
## 
## loaded via a namespace (and not attached):
##   [1] sys_3.4.3                 jsonlite_1.8.9           
##   [3] shape_1.4.6.1             magrittr_2.0.3           
##   [5] GenomicFeatures_1.57.1    farver_2.1.2             
##   [7] rmarkdown_2.28            BiocIO_1.17.0            
##   [9] zlibbioc_1.51.2           vctrs_0.6.5              
##  [11] memoise_2.0.1             Rsamtools_2.21.2         
##  [13] RCurl_1.98-1.16           htmltools_0.5.8.1        
##  [15] S4Arrays_1.5.11           BiocBaseUtils_1.9.0      
##  [17] progress_1.2.3            AnnotationHub_3.15.0     
##  [19] lambda.r_1.2.4            curl_5.2.3               
##  [21] pROC_1.18.5               SparseArray_1.5.45       
##  [23] sass_0.4.9                bslib_0.8.0              
##  [25] plyr_1.8.9                httr2_1.0.5              
##  [27] futile.options_1.0.1      cachem_1.1.0             
##  [29] buildtools_1.0.0          GenomicAlignments_1.41.0 
##  [31] mime_0.12                 lifecycle_1.0.4          
##  [33] iterators_1.0.14          pkgconfig_2.0.3          
##  [35] Matrix_1.7-1              R6_2.5.1                 
##  [37] fastmap_1.2.0             GenomeInfoDbData_1.2.13  
##  [39] digest_0.6.37             colorspace_2.1-1         
##  [41] AnnotationDbi_1.69.0      ExperimentHub_2.13.1     
##  [43] RSQLite_2.3.7             filelock_1.0.3           
##  [45] labeling_0.4.3            fansi_1.0.6              
##  [47] httr_1.4.7                abind_1.4-8              
##  [49] compiler_4.4.1            bit64_4.5.2              
##  [51] withr_3.0.2               backports_1.5.0          
##  [53] BiocParallel_1.41.0       DBI_1.2.3                
##  [55] highr_0.11                biomaRt_2.63.0           
##  [57] rappdirs_0.3.3            DelayedArray_0.33.1      
##  [59] rjson_0.2.23              tools_4.4.1              
##  [61] glue_1.8.0                restfulr_0.0.15          
##  [63] grid_4.4.1                checkmate_2.3.2          
##  [65] generics_0.1.3            gtable_0.3.6             
##  [67] tzdb_0.4.0                hms_1.1.3                
##  [69] xml2_1.3.6                utf8_1.2.4               
##  [71] XVector_0.45.0            BiocVersion_3.21.1       
##  [73] foreach_1.5.2             pillar_1.9.0             
##  [75] stringr_1.5.1             splines_4.4.1            
##  [77] BiocFileCache_2.15.0      lattice_0.22-6           
##  [79] rtracklayer_1.65.0        bit_4.5.0                
##  [81] tidyselect_1.2.1          maketools_1.3.1          
##  [83] Biostrings_2.75.0         knitr_1.48               
##  [85] xfun_0.48                 stringi_1.8.4            
##  [87] UCSC.utils_1.1.0          yaml_2.3.10              
##  [89] evaluate_1.0.1            codetools_0.2-20         
##  [91] tibble_3.2.1              BiocManager_1.30.25      
##  [93] cli_3.6.3                 munsell_0.5.1            
##  [95] jquerylib_0.1.4           Rcpp_1.0.13              
##  [97] GenomicDataCommons_1.29.7 dbplyr_2.5.0             
##  [99] png_0.1-8                 XML_3.99-0.17            
## [101] parallel_4.4.1            readr_2.1.5              
## [103] blob_1.2.4                prettyunits_1.2.0        
## [105] bitops_1.0-9              glmnet_4.1-8             
## [107] scales_1.3.0              purrr_1.0.2              
## [109] crayon_1.5.3              rlang_1.1.4              
## [111] KEGGREST_1.45.1           rvest_1.0.4              
## [113] formatR_1.14