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 4.4.1 (2024-06-14)
## Platform: aarch64-apple-darwin20
## Running under: macOS Ventura 13.6.7
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
## 
## 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_1.7-1                glmSparseNet_1.24.0        
##  [7] TCGAutils_1.26.0            curatedTCGAData_1.27.1     
##  [9] MultiAssayExperiment_1.32.0 SummarizedExperiment_1.36.0
## [11] Biobase_2.66.0              GenomicRanges_1.58.0       
## [13] GenomeInfoDb_1.42.0         IRanges_2.40.0             
## [15] S4Vectors_0.44.0            BiocGenerics_0.52.0        
## [17] MatrixGenerics_1.18.0       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_2.34.0           
## 
## loaded via a namespace (and not attached):
##   [1] jsonlite_1.8.9            shape_1.4.6.1            
##   [3] magrittr_2.0.3            magick_2.8.5             
##   [5] GenomicFeatures_1.58.0    farver_2.1.2             
##   [7] rmarkdown_2.29            BiocIO_1.16.0            
##   [9] zlibbioc_1.52.0           vctrs_0.6.5              
##  [11] memoise_2.0.1             Rsamtools_2.22.0         
##  [13] RCurl_1.98-1.16           rstatix_0.7.2            
##  [15] tinytex_0.54              progress_1.2.3           
##  [17] htmltools_0.5.8.1         S4Arrays_1.6.0           
##  [19] BiocBaseUtils_1.8.0       AnnotationHub_3.14.0     
##  [21] lambda.r_1.2.4            curl_6.0.0               
##  [23] broom_1.0.7               Formula_1.2-5            
##  [25] pROC_1.18.5               SparseArray_1.6.0        
##  [27] sass_0.4.9                bslib_0.8.0              
##  [29] plyr_1.8.9                httr2_1.0.6              
##  [31] zoo_1.8-12                futile.options_1.0.1     
##  [33] cachem_1.1.0              GenomicAlignments_1.42.0 
##  [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_1.68.0     
##  [45] ps_1.8.1                  ExperimentHub_2.14.0     
##  [47] RSQLite_2.3.7             ggpubr_0.6.0             
##  [49] labeling_0.4.3            filelock_1.0.3           
##  [51] km.ci_0.5-6               fansi_1.0.6              
##  [53] httr_1.4.7                abind_1.4-8              
##  [55] compiler_4.4.1            bit64_4.5.2              
##  [57] withr_3.0.2               backports_1.5.0          
##  [59] BiocParallel_1.40.0       carData_3.0-5            
##  [61] DBI_1.2.3                 ggsignif_0.6.4           
##  [63] biomaRt_2.62.0            rappdirs_0.3.3           
##  [65] DelayedArray_0.32.0       rjson_0.2.23             
##  [67] tools_4.4.1               chromote_0.3.1           
##  [69] glue_1.8.0                restfulr_0.0.15          
##  [71] promises_1.3.0            checkmate_2.3.2          
##  [73] generics_0.1.3            gtable_0.3.6             
##  [75] KMsurv_0.1-5              tzdb_0.4.0               
##  [77] tidyr_1.3.1               survminer_0.5.0          
##  [79] websocket_1.4.2           data.table_1.16.2        
##  [81] hms_1.1.3                 car_3.1-3                
##  [83] xml2_1.3.6                utf8_1.2.4               
##  [85] XVector_0.46.0            BiocVersion_3.20.0       
##  [87] foreach_1.5.2             pillar_1.9.0             
##  [89] stringr_1.5.1             later_1.3.2              
##  [91] splines_4.4.1             BiocFileCache_2.14.0     
##  [93] lattice_0.22-6            rtracklayer_1.66.0       
##  [95] bit_4.5.0                 tidyselect_1.2.1         
##  [97] Biostrings_2.74.0         knitr_1.49               
##  [99] gridExtra_2.3             bookdown_0.41            
## [101] xfun_0.49                 stringi_1.8.4            
## [103] UCSC.utils_1.2.0          yaml_2.3.10              
## [105] evaluate_1.0.1            codetools_0.2-20         
## [107] tibble_3.2.1              BiocManager_1.30.25      
## [109] cli_3.6.3                 xtable_1.8-4             
## [111] munsell_0.5.1             processx_3.8.4           
## [113] jquerylib_0.1.4           survMisc_0.5.6           
## [115] Rcpp_1.0.13-1             GenomicDataCommons_1.30.0
## [117] dbplyr_2.5.0              png_0.1-8                
## [119] XML_3.99-0.17             readr_2.1.5              
## [121] blob_1.2.4                prettyunits_1.2.0        
## [123] bitops_1.0-9              scales_1.3.0             
## [125] purrr_1.0.2               crayon_1.5.3             
## [127] rlang_1.1.4               KEGGREST_1.46.0          
## [129] rvest_1.0.4               formatR_1.14