Contents

0.1 Instalation

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

1 Required Packages

library(futile.logger)
library(ggplot2)
library(glmSparseNet)
library(survival)

# Some general options for futile.logger the debugging package
flog.layout(layout.format("[~l] ~m"))
options("glmSparseNet.show_message" = FALSE)
# Setting ggplot2 default theme as minimal
theme_set(ggplot2::theme_minimal())

1.1 Prepare data

data("cancer", package = "survival")
xdata <- survival::ovarian[, c("age", "resid.ds")]
ydata <- data.frame(
    time = survival::ovarian$futime,
    status = survival::ovarian$fustat
)

1.2 Separate using age as co-variate

(group cutoff is median calculated relative risk)

resAge <- separate2GroupsCox(c(age = 1, 0), xdata, ydata)

1.2.1 Kaplan-Meier survival results

## Call: survfit(formula = survival::Surv(time, status) ~ group, data = prognosticIndexDf)
## 
##                n events median 0.95LCL 0.95UCL
## Low risk - 1  13      4     NA     638      NA
## High risk - 1 13      8    464     268      NA

1.2.2 Plot

A individual is attributed to low-risk group if its calculated relative risk (using Cox Proportional model) is below or equal the median risk.

The opposite for the high-risk groups, populated with individuals above the median relative-risk.

1.3 Separate using age as co-variate (group cutoff is 40% - 60%)

resAge4060 <-
    separate2GroupsCox(c(age = 1, 0),
        xdata,
        ydata,
        probs = c(.4, .6)
    )

1.3.1 Kaplan-Meier survival results

## Call: survfit(formula = survival::Surv(time, status) ~ group, data = prognosticIndexDf)
## 
##                n events median 0.95LCL 0.95UCL
## Low risk - 1  11      3     NA     563      NA
## High risk - 1 10      7    359     156      NA

1.3.2 Plot

A individual is attributed to low-risk group if its calculated relative risk (using Cox Proportional model) is below the median risk.

The opposite for the high-risk groups, populated with individuals above the median relative-risk.

1.4 Separate using age as co-variate (group cutoff is 60% - 40%)

This is a special case where you want to use a cutoff that includes some sample on both high and low risks groups.

resAge6040 <- separate2GroupsCox(
    chosenBetas = c(age = 1, 0),
    xdata,
    ydata,
    probs = c(.6, .4),
    stopWhenOverlap = FALSE
)
## Warning in buildPrognosticIndexDataFrame(ydata, probs, stopWhenOverlap, : The cutoff values given to the function allow for some over samples in both groups, with:
##   high risk size (15) + low risk size (16) not equal to xdata/ydata rows (31 != 26)
## 
## We are continuing with execution as parameter `stopWhenOverlap` is FALSE.
##   note: This adds duplicate samples to ydata and xdata xdata

1.4.1 Kaplan-Meier survival results

## Kaplan-Meier results
## Call: survfit(formula = survival::Surv(time, status) ~ group, data = prognosticIndexDf)
## 
##                n events median 0.95LCL 0.95UCL
## Low risk - 1  16      5     NA     638      NA
## High risk - 1 15      9    475     353      NA

1.4.2 Plot

A individual is attributed to low-risk group if its calculated relative risk (using Cox Proportional model) is below the median risk.

The opposite for the high-risk groups, populated with individuals above the median relative-risk.

2 Session Info

sessionInfo()
## R version 4.5.0 Patched (2025-04-21 r88169)
## Platform: aarch64-apple-darwin20
## Running under: macOS Ventura 13.7.1
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
## 
## 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] 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_1.7-3                glmSparseNet_1.27.0        
##  [7] TCGAutils_1.29.0            curatedTCGAData_1.31.0     
##  [9] MultiAssayExperiment_1.35.1 SummarizedExperiment_1.39.0
## [11] Biobase_2.69.0              GenomicRanges_1.61.0       
## [13] GenomeInfoDb_1.45.0         IRanges_2.43.0             
## [15] S4Vectors_0.47.0            BiocGenerics_0.55.0        
## [17] generics_0.1.3              MatrixGenerics_1.21.0      
## [19] matrixStats_1.5.0           futile.logger_1.4.3        
## [21] survival_3.8-3              ggplot2_3.5.2              
## [23] dplyr_1.1.4                 BiocStyle_2.37.0           
## 
## loaded via a namespace (and not attached):
##   [1] RColorBrewer_1.1-3        jsonlite_2.0.0           
##   [3] shape_1.4.6.1             magrittr_2.0.3           
##   [5] magick_2.8.6              GenomicFeatures_1.61.0   
##   [7] farver_2.1.2              rmarkdown_2.29           
##   [9] BiocIO_1.19.0             vctrs_0.6.5              
##  [11] memoise_2.0.1             Rsamtools_2.25.0         
##  [13] RCurl_1.98-1.17           rstatix_0.7.2            
##  [15] tinytex_0.57              htmltools_0.5.8.1        
##  [17] S4Arrays_1.9.0            BiocBaseUtils_1.11.0     
##  [19] progress_1.2.3            AnnotationHub_3.99.0     
##  [21] lambda.r_1.2.4            curl_6.2.2               
##  [23] broom_1.0.8               Formula_1.2-5            
##  [25] pROC_1.18.5               SparseArray_1.9.0        
##  [27] sass_0.4.10               bslib_0.9.0              
##  [29] plyr_1.8.9                httr2_1.1.2              
##  [31] zoo_1.8-14                futile.options_1.0.1     
##  [33] cachem_1.1.0              GenomicAlignments_1.45.0 
##  [35] lifecycle_1.0.4           iterators_1.0.14         
##  [37] pkgconfig_2.0.3           R6_2.6.1                 
##  [39] fastmap_1.2.0             GenomeInfoDbData_1.2.14  
##  [41] digest_0.6.37             AnnotationDbi_1.71.0     
##  [43] ps_1.9.1                  ExperimentHub_2.99.0     
##  [45] RSQLite_2.3.9             ggpubr_0.6.0             
##  [47] labeling_0.4.3            filelock_1.0.3           
##  [49] km.ci_0.5-6               httr_1.4.7               
##  [51] abind_1.4-8               compiler_4.5.0           
##  [53] bit64_4.6.0-1             withr_3.0.2              
##  [55] backports_1.5.0           BiocParallel_1.43.0      
##  [57] carData_3.0-5             DBI_1.2.3                
##  [59] ggsignif_0.6.4            biomaRt_2.65.0           
##  [61] rappdirs_0.3.3            DelayedArray_0.35.1      
##  [63] rjson_0.2.23              tools_4.5.0              
##  [65] chromote_0.5.1            glue_1.8.0               
##  [67] restfulr_0.0.15           promises_1.3.2           
##  [69] checkmate_2.3.2           gtable_0.3.6             
##  [71] KMsurv_0.1-5              tzdb_0.5.0               
##  [73] tidyr_1.3.1               survminer_0.5.0          
##  [75] websocket_1.4.4           data.table_1.17.0        
##  [77] hms_1.1.3                 car_3.1-3                
##  [79] xml2_1.3.8                XVector_0.49.0           
##  [81] BiocVersion_3.22.0        foreach_1.5.2            
##  [83] pillar_1.10.2             stringr_1.5.1            
##  [85] later_1.4.2               splines_4.5.0            
##  [87] BiocFileCache_2.99.0      lattice_0.22-7           
##  [89] rtracklayer_1.69.0        bit_4.6.0                
##  [91] tidyselect_1.2.1          Biostrings_2.77.0        
##  [93] knitr_1.50                gridExtra_2.3            
##  [95] bookdown_0.43             xfun_0.52                
##  [97] stringi_1.8.7             UCSC.utils_1.5.0         
##  [99] yaml_2.3.10               evaluate_1.0.3           
## [101] codetools_0.2-20          tibble_3.2.1             
## [103] BiocManager_1.30.25       cli_3.6.5                
## [105] xtable_1.8-4              processx_3.8.6           
## [107] jquerylib_0.1.4           survMisc_0.5.6           
## [109] dichromat_2.0-0.1         Rcpp_1.0.14              
## [111] GenomicDataCommons_1.33.0 dbplyr_2.5.0             
## [113] png_0.1-8                 XML_3.99-0.18            
## [115] readr_2.1.5               blob_1.2.4               
## [117] prettyunits_1.2.0         bitops_1.0-9             
## [119] scales_1.4.0              purrr_1.0.4              
## [121] crayon_1.5.3              rlang_1.1.6              
## [123] KEGGREST_1.49.0           rvest_1.0.4              
## [125] formatR_1.14