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(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())

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)
pradPrimarySolidTumor <- TCGAutils::TCGAsplitAssays(prad, "01")
xdataRaw <- t(assay(pradPrimarySolidTumor[[1]]))

# Get survival information
ydataRaw <- colData(pradPrimarySolidTumor) |>
    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(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$seed)
smallSubset <- c(
    geneNames(c(
        "ENSG00000103091", "ENSG00000064787",
        "ENSG00000119915", "ENSG00000120158",
        "ENSG00000114491", "ENSG00000204176",
        "ENSG00000138399"
    ))$external_gene_name,
    sample(colnames(xdataRaw), 100)
) |>
    unique() |>
    sort()

xdata <- xdataRaw[, smallSubset[smallSubset %in% colnames(xdataRaw)]]
ydata <- ydataRaw |> 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",
    options = networkOptions(
        cutoff = .6,
        minDegree = .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

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()
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 Survival curves and Log rank test

separate2GroupsCox(as.vector(coefsCV),
    xdata[, names(coefsCV)],
    ydata,
    plotTitle = "Full dataset", legendOutside = FALSE
)
## $pvalue
## [1] 0.001155155
## 
## $plot

## 
## $km
## Call: survfit(formula = survival::Surv(time, status) ~ group, data = prognosticIndexDf)
## 
##                 n events median 0.95LCL 0.95UCL
## Low risk - 1  249      0     NA      NA      NA
## High risk - 1 248     10   3502    3467      NA

6 Session Info

sessionInfo()
## R version 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.19-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_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [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] 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_1.7-0                glmSparseNet_1.22.0        
##  [7] TCGAutils_1.24.0            curatedTCGAData_1.25.4     
##  [9] MultiAssayExperiment_1.30.0 SummarizedExperiment_1.34.0
## [11] Biobase_2.64.0              GenomicRanges_1.56.0       
## [13] GenomeInfoDb_1.40.0         IRanges_2.38.0             
## [15] S4Vectors_0.42.0            BiocGenerics_0.50.0        
## [17] MatrixGenerics_1.16.0       matrixStats_1.3.0          
## [19] futile.logger_1.4.3         survival_3.6-4             
## [21] ggplot2_3.5.1               dplyr_1.1.4                
## [23] BiocStyle_2.32.0           
## 
## loaded via a namespace (and not attached):
##   [1] jsonlite_1.8.8            shape_1.4.6.1            
##   [3] magrittr_2.0.3            magick_2.8.3             
##   [5] GenomicFeatures_1.56.0    farver_2.1.1             
##   [7] rmarkdown_2.26            BiocIO_1.14.0            
##   [9] zlibbioc_1.50.0           vctrs_0.6.5              
##  [11] memoise_2.0.1             Rsamtools_2.20.0         
##  [13] RCurl_1.98-1.14           rstatix_0.7.2            
##  [15] tinytex_0.50              progress_1.2.3           
##  [17] htmltools_0.5.8.1         S4Arrays_1.4.0           
##  [19] BiocBaseUtils_1.6.0       AnnotationHub_3.12.0     
##  [21] lambda.r_1.2.4            curl_5.2.1               
##  [23] broom_1.0.5               pROC_1.18.5              
##  [25] SparseArray_1.4.0         sass_0.4.9               
##  [27] bslib_0.7.0               plyr_1.8.9               
##  [29] httr2_1.0.1               zoo_1.8-12               
##  [31] futile.options_1.0.1      cachem_1.0.8             
##  [33] GenomicAlignments_1.40.0  mime_0.12                
##  [35] lifecycle_1.0.4           iterators_1.0.14         
##  [37] pkgconfig_2.0.3           R6_2.5.1                 
##  [39] fastmap_1.1.1             GenomeInfoDbData_1.2.12  
##  [41] digest_0.6.35             colorspace_2.1-0         
##  [43] AnnotationDbi_1.66.0      ps_1.7.6                 
##  [45] ExperimentHub_2.12.0      RSQLite_2.3.6            
##  [47] ggpubr_0.6.0              labeling_0.4.3           
##  [49] filelock_1.0.3            km.ci_0.5-6              
##  [51] fansi_1.0.6               httr_1.4.7               
##  [53] abind_1.4-5               compiler_4.4.0           
##  [55] bit64_4.0.5               withr_3.0.0              
##  [57] backports_1.4.1           BiocParallel_1.38.0      
##  [59] carData_3.0-5             DBI_1.2.2                
##  [61] highr_0.10                ggsignif_0.6.4           
##  [63] biomaRt_2.60.0            rappdirs_0.3.3           
##  [65] DelayedArray_0.30.0       rjson_0.2.21             
##  [67] tools_4.4.0               chromote_0.2.0           
##  [69] glue_1.7.0                restfulr_0.0.15          
##  [71] promises_1.3.0            checkmate_2.3.1          
##  [73] generics_0.1.3            gtable_0.3.5             
##  [75] KMsurv_0.1-5              tzdb_0.4.0               
##  [77] tidyr_1.3.1               survminer_0.4.9          
##  [79] websocket_1.4.1           data.table_1.15.4        
##  [81] hms_1.1.3                 car_3.1-2                
##  [83] xml2_1.3.6                utf8_1.2.4               
##  [85] XVector_0.44.0            BiocVersion_3.19.1       
##  [87] foreach_1.5.2             pillar_1.9.0             
##  [89] stringr_1.5.1             later_1.3.2              
##  [91] splines_4.4.0             BiocFileCache_2.12.0     
##  [93] lattice_0.22-6            rtracklayer_1.64.0       
##  [95] bit_4.0.5                 tidyselect_1.2.1         
##  [97] Biostrings_2.72.0         knitr_1.46               
##  [99] gridExtra_2.3             bookdown_0.39            
## [101] xfun_0.43                 stringi_1.8.3            
## [103] UCSC.utils_1.0.0          yaml_2.3.8               
## [105] evaluate_0.23             codetools_0.2-20         
## [107] tibble_3.2.1              BiocManager_1.30.22      
## [109] cli_3.6.2                 xtable_1.8-4             
## [111] munsell_0.5.1             processx_3.8.4           
## [113] jquerylib_0.1.4           survMisc_0.5.6           
## [115] Rcpp_1.0.12               GenomicDataCommons_1.28.0
## [117] dbplyr_2.5.0              png_0.1-8                
## [119] XML_3.99-0.16.1           readr_2.1.5              
## [121] blob_1.2.4                prettyunits_1.2.0        
## [123] bitops_1.0-7              scales_1.3.0             
## [125] purrr_1.0.2               crayon_1.5.2             
## [127] rlang_1.1.3               KEGGREST_1.44.0          
## [129] rvest_1.0.4               formatR_1.14