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
.Last.value <- flog.layout(layout.format('[~l] ~m'))
.Last.value <- 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)

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

1.2.1 Kaplan-Meier survival results

## Call: survfit(formula = survival::Surv(time, status) ~ group, data = prognostic.index.df)
## 
##            n events median 0.95LCL 0.95UCL
## Low risk  13      4     NA     638      NA
## High risk 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%)

res.age.40.60 <- 
  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 = prognostic.index.df)
## 
##            n events median 0.95LCL 0.95UCL
## Low risk  11      3     NA     563      NA
## High risk 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.

res.age.60.40 <- separate2GroupsCox(
  chosen.btas = c(age = 1, 0), 
  xdata, 
  ydata, 
  probs = c(.6, .4),
  stop.when.overlap = FALSE
)
## Warning in separate2GroupsCox(chosen.btas = c(age = 1, 0), xdata, ydata, : 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 stop.when.overlap 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 = prognostic.index.df)
## 
##            n events median 0.95LCL 0.95UCL
## Low risk  16      5     NA     638      NA
## High risk 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 4.3.2 Patched (2023-11-01 r85457)
## Platform: x86_64-apple-darwin20 (64-bit)
## Running under: macOS Monterey 12.7.1
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
## 
## 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] VennDiagram_1.7.3           reshape2_1.4.4             
##  [3] forcats_1.0.0               glmSparseNet_1.20.1        
##  [5] glmnet_4.1-8                Matrix_1.6-5               
##  [7] TCGAutils_1.22.2            curatedTCGAData_1.24.0     
##  [9] MultiAssayExperiment_1.28.0 SummarizedExperiment_1.32.0
## [11] Biobase_2.62.0              GenomicRanges_1.54.1       
## [13] GenomeInfoDb_1.38.5         IRanges_2.36.0             
## [15] S4Vectors_0.40.2            BiocGenerics_0.48.1        
## [17] MatrixGenerics_1.14.0       matrixStats_1.2.0          
## [19] futile.logger_1.4.3         survival_3.5-7             
## [21] ggplot2_3.4.4               dplyr_1.1.4                
## [23] BiocStyle_2.30.0           
## 
## loaded via a namespace (and not attached):
##   [1] jsonlite_1.8.8                shape_1.4.6                  
##   [3] magrittr_2.0.3                magick_2.8.2                 
##   [5] GenomicFeatures_1.54.3        farver_2.1.1                 
##   [7] rmarkdown_2.25                BiocIO_1.12.0                
##   [9] zlibbioc_1.48.0               vctrs_0.6.5                  
##  [11] memoise_2.0.1                 Rsamtools_2.18.0             
##  [13] RCurl_1.98-1.14               rstatix_0.7.2                
##  [15] BiocBaseUtils_1.4.0           htmltools_0.5.7              
##  [17] S4Arrays_1.2.0                progress_1.2.3               
##  [19] AnnotationHub_3.10.0          lambda.r_1.2.4               
##  [21] curl_5.2.0                    broom_1.0.5                  
##  [23] SparseArray_1.2.3             sass_0.4.8                   
##  [25] bslib_0.6.1                   plyr_1.8.9                   
##  [27] zoo_1.8-12                    futile.options_1.0.1         
##  [29] cachem_1.0.8                  GenomicAlignments_1.38.2     
##  [31] mime_0.12                     lifecycle_1.0.4              
##  [33] iterators_1.0.14              pkgconfig_2.0.3              
##  [35] R6_2.5.1                      fastmap_1.1.1                
##  [37] GenomeInfoDbData_1.2.11       shiny_1.8.0                  
##  [39] digest_0.6.34                 colorspace_2.1-0             
##  [41] AnnotationDbi_1.64.1          ExperimentHub_2.10.0         
##  [43] RSQLite_2.3.5                 ggpubr_0.6.0                 
##  [45] filelock_1.0.3                labeling_0.4.3               
##  [47] km.ci_0.5-6                   fansi_1.0.6                  
##  [49] httr_1.4.7                    abind_1.4-5                  
##  [51] compiler_4.3.2                bit64_4.0.5                  
##  [53] withr_3.0.0                   backports_1.4.1              
##  [55] BiocParallel_1.36.0           carData_3.0-5                
##  [57] DBI_1.2.1                     highr_0.10                   
##  [59] ggsignif_0.6.4                biomaRt_2.58.2               
##  [61] rappdirs_0.3.3                DelayedArray_0.28.0          
##  [63] rjson_0.2.21                  tools_4.3.2                  
##  [65] interactiveDisplayBase_1.40.0 httpuv_1.6.14                
##  [67] glue_1.7.0                    restfulr_0.0.15              
##  [69] promises_1.2.1                generics_0.1.3               
##  [71] gtable_0.3.4                  KMsurv_0.1-5                 
##  [73] tzdb_0.4.0                    tidyr_1.3.1                  
##  [75] survminer_0.4.9               data.table_1.15.0            
##  [77] hms_1.1.3                     car_3.1-2                    
##  [79] xml2_1.3.6                    utf8_1.2.4                   
##  [81] XVector_0.42.0                BiocVersion_3.18.1           
##  [83] foreach_1.5.2                 pillar_1.9.0                 
##  [85] stringr_1.5.1                 later_1.3.2                  
##  [87] splines_4.3.2                 BiocFileCache_2.10.1         
##  [89] lattice_0.22-5                rtracklayer_1.62.0           
##  [91] bit_4.0.5                     tidyselect_1.2.0             
##  [93] Biostrings_2.70.2             knitr_1.45                   
##  [95] gridExtra_2.3                 bookdown_0.37                
##  [97] xfun_0.41                     stringi_1.8.3                
##  [99] yaml_2.3.8                    evaluate_0.23                
## [101] codetools_0.2-19              tibble_3.2.1                 
## [103] BiocManager_1.30.22           cli_3.6.2                    
## [105] xtable_1.8-4                  munsell_0.5.0                
## [107] jquerylib_0.1.4               survMisc_0.5.6               
## [109] Rcpp_1.0.12                   GenomicDataCommons_1.26.0    
## [111] dbplyr_2.4.0                  png_0.1-8                    
## [113] XML_3.99-0.16.1               ellipsis_0.3.2               
## [115] readr_2.1.5                   blob_1.2.4                   
## [117] prettyunits_1.2.0             bitops_1.0-7                 
## [119] scales_1.3.0                  purrr_1.0.2                  
## [121] crayon_1.5.2                  rlang_1.1.3                  
## [123] KEGGREST_1.42.0               rvest_1.0.3                  
## [125] formatR_1.14