Instalation

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

Required Packages

library(dplyr)
library(ggplot2)
library(survival)
library(futile.logger)
library(curatedTCGAData)
library(TCGAutils)
library(MultiAssayExperiment)
#
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.

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

Build the survival data from the clinical columns.

• Merge survival times for patients, which have different columns in case they are alive or dead.
• Build two matrix objects that fit the data xdata and ydata

skcmMetastatic <- TCGAutils::TCGAsplitAssays(skcm, "06")
xdataRaw <- t(assay(skcmMetastatic[[1]]))

# Get survival information
ydataRaw <- colData(skcmMetastatic) |>
    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()

# Get survival information
ydataRaw <- colData(skcm) |>
    as.data.frame() |>
    # Find max time between all days (ignoring missings)
    dplyr::filter(
        !is.na(days_to_last_followup) | !is.na(days_to_death)
    ) |>
    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(ydataRaw) <- ydataRaw$patientID

# keep only features that have standard deviation > 0
xdataRaw <- xdataRaw[
    TCGAbarcode(rownames(xdataRaw)) %in% rownames(ydataRaw),
]
xdataRaw <- xdataRaw[, apply(xdataRaw, 2, sd) != 0] |>
    scale()

# Order ydata the same as assay
ydataRaw <- ydataRaw[TCGAbarcode(rownames(xdataRaw)), ]

set.seed(params$seed)
smallSubset <- c(
    "FOXL2", "KLHL5", "PCYT2", "SLC6A10P", "STRAP", "TMEM33",
    "WT1-AS", sample(colnames(xdataRaw), 100)
)

xdata <- xdataRaw[, smallSubset[smallSubset %in% colnames(xdataRaw)]]
ydata <- ydataRaw |> dplyr::select(time, status)

Fit models

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

fitted <- cv.glmHub(
    xdata,
    Surv(ydata$time, ydata$status),
    family = "cox",
    foldid = glmSparseNet:::balancedCvFolds(ydata$status)$output,
    network = "correlation",
    options = networkOptions(
        cutoff = .6,
        minDegree = .2
    )
)

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)

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()

separate2GroupsCox(as.vector(coefsCV),
    xdata[, names(coefsCV)],
    ydata,
    plotTitle = "Full dataset", legendOutside = FALSE
)

## $pvalue
## [1] 0.0001269853
## 
## $plot

## 
## $km
## Call: survfit(formula = survival::Surv(time, status) ~ group, data = prognosticIndexDf)
## 
##                 n events median 0.95LCL 0.95UCL
## Low risk - 1  180     79   4000    2927    6164
## High risk - 1 179    114   2005    1524    2829

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] grid      parallel  stats4    stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] glmnet_4.1-8                VennDiagram_1.7.3          
##  [3] reshape2_1.4.4              forcats_1.0.0              
##  [5] Matrix_1.7-1                glmSparseNet_1.25.0        
##  [7] TCGAutils_1.25.1            curatedTCGAData_1.27.1     
##  [9] MultiAssayExperiment_1.31.5 SummarizedExperiment_1.35.5
## [11] Biobase_2.67.0              GenomicRanges_1.57.2       
## [13] GenomeInfoDb_1.41.2         IRanges_2.39.2             
## [15] S4Vectors_0.43.2            BiocGenerics_0.53.0        
## [17] MatrixGenerics_1.17.1       matrixStats_1.4.1          
## [19] futile.logger_1.4.3         survival_3.7-0             
## [21] ggplot2_3.5.1               dplyr_1.1.4                
## [23] 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           rstatix_0.7.2            
##  [15] htmltools_0.5.8.1         S4Arrays_1.5.11          
##  [17] BiocBaseUtils_1.9.0       progress_1.2.3           
##  [19] AnnotationHub_3.15.0      lambda.r_1.2.4           
##  [21] curl_5.2.3                broom_1.0.7              
##  [23] Formula_1.2-5             pROC_1.18.5              
##  [25] SparseArray_1.5.45        sass_0.4.9               
##  [27] bslib_0.8.0               plyr_1.8.9               
##  [29] httr2_1.0.5               zoo_1.8-12               
##  [31] futile.options_1.0.1      cachem_1.1.0             
##  [33] buildtools_1.0.0          GenomicAlignments_1.41.0 
##  [35] mime_0.12                 lifecycle_1.0.4          
##  [37] iterators_1.0.14          pkgconfig_2.0.3          
##  [39] R6_2.5.1                  fastmap_1.2.0            
##  [41] GenomeInfoDbData_1.2.13   digest_0.6.37            
##  [43] colorspace_2.1-1          AnnotationDbi_1.69.0     
##  [45] ExperimentHub_2.13.1      RSQLite_2.3.7            
##  [47] ggpubr_0.6.0              filelock_1.0.3           
##  [49] labeling_0.4.3            km.ci_0.5-6              
##  [51] fansi_1.0.6               httr_1.4.7               
##  [53] abind_1.4-8               compiler_4.4.1           
##  [55] bit64_4.5.2               withr_3.0.2              
##  [57] backports_1.5.0           BiocParallel_1.41.0      
##  [59] carData_3.0-5             DBI_1.2.3                
##  [61] highr_0.11                ggsignif_0.6.4           
##  [63] biomaRt_2.63.0            rappdirs_0.3.3           
##  [65] DelayedArray_0.33.1       rjson_0.2.23             
##  [67] tools_4.4.1               glue_1.8.0               
##  [69] restfulr_0.0.15           checkmate_2.3.2          
##  [71] generics_0.1.3            gtable_0.3.6             
##  [73] KMsurv_0.1-5              tzdb_0.4.0               
##  [75] tidyr_1.3.1               survminer_0.4.9          
##  [77] data.table_1.16.2         hms_1.1.3                
##  [79] car_3.1-3                 xml2_1.3.6               
##  [81] utf8_1.2.4                XVector_0.45.0           
##  [83] BiocVersion_3.21.1        foreach_1.5.2            
##  [85] pillar_1.9.0              stringr_1.5.1            
##  [87] splines_4.4.1             BiocFileCache_2.15.0     
##  [89] lattice_0.22-6            rtracklayer_1.65.0       
##  [91] bit_4.5.0                 tidyselect_1.2.1         
##  [93] maketools_1.3.1           Biostrings_2.75.0        
##  [95] knitr_1.48                gridExtra_2.3            
##  [97] xfun_0.48                 stringi_1.8.4            
##  [99] UCSC.utils_1.1.0          yaml_2.3.10              
## [101] evaluate_1.0.1            codetools_0.2-20         
## [103] tibble_3.2.1              BiocManager_1.30.25      
## [105] cli_3.6.3                 xtable_1.8-4             
## [107] munsell_0.5.1             jquerylib_0.1.4          
## [109] survMisc_0.5.6            Rcpp_1.0.13              
## [111] GenomicDataCommons_1.29.7 dbplyr_2.5.0             
## [113] png_0.1-8                 XML_3.99-0.17            
## [115] readr_2.1.5               blob_1.2.4               
## [117] prettyunits_1.2.0         bitops_1.0-9             
## [119] scales_1.3.0              purrr_1.0.2              
## [121] crayon_1.5.3              rlang_1.1.4              
## [123] KEGGREST_1.45.1           rvest_1.0.4              
## [125] formatR_1.14