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 4.3.0 RC (2023-04-13 r84266)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Monterey 12.6.1
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/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_1.18.0        
##  [5] glmnet_4.1-7                Matrix_1.5-4               
##  [7] TCGAutils_1.20.0            curatedTCGAData_1.21.2     
##  [9] MultiAssayExperiment_1.26.0 SummarizedExperiment_1.30.1
## [11] Biobase_2.60.0              GenomicRanges_1.52.0       
## [13] GenomeInfoDb_1.36.0         IRanges_2.34.0             
## [15] S4Vectors_0.38.1            BiocGenerics_0.46.0        
## [17] MatrixGenerics_1.12.0       matrixStats_0.63.0         
## [19] futile.logger_1.4.3         survival_3.5-5             
## [21] ggplot2_3.4.2               dplyr_1.1.1                
## [23] BiocStyle_2.28.0           
## 
## loaded via a namespace (and not attached):
##   [1] jsonlite_1.8.4                shape_1.4.6                  
##   [3] magrittr_2.0.3                magick_2.7.4                 
##   [5] GenomicFeatures_1.52.0        farver_2.1.1                 
##   [7] rmarkdown_2.21                BiocIO_1.10.0                
##   [9] zlibbioc_1.46.0               vctrs_0.6.1                  
##  [11] memoise_2.0.1                 Rsamtools_2.16.0             
##  [13] RCurl_1.98-1.12               rstatix_0.7.2                
##  [15] htmltools_0.5.5               S4Arrays_1.0.1               
##  [17] progress_1.2.2                AnnotationHub_3.8.0          
##  [19] lambda.r_1.2.4                curl_5.0.0                   
##  [21] broom_1.0.4                   pROC_1.18.0                  
##  [23] sass_0.4.5                    bslib_0.4.2                  
##  [25] plyr_1.8.8                    zoo_1.8-12                   
##  [27] futile.options_1.0.1          cachem_1.0.7                 
##  [29] GenomicAlignments_1.36.0      mime_0.12                    
##  [31] lifecycle_1.0.3               iterators_1.0.14             
##  [33] pkgconfig_2.0.3               R6_2.5.1                     
##  [35] fastmap_1.1.1                 GenomeInfoDbData_1.2.10      
##  [37] shiny_1.7.4                   digest_0.6.31                
##  [39] colorspace_2.1-0              AnnotationDbi_1.62.1         
##  [41] ExperimentHub_2.8.0           RSQLite_2.3.1                
##  [43] ggpubr_0.6.0                  filelock_1.0.2               
##  [45] labeling_0.4.2                km.ci_0.5-6                  
##  [47] fansi_1.0.4                   abind_1.4-5                  
##  [49] httr_1.4.5                    compiler_4.3.0               
##  [51] bit64_4.0.5                   withr_2.5.0                  
##  [53] backports_1.4.1               BiocParallel_1.34.1          
##  [55] carData_3.0-5                 DBI_1.1.3                    
##  [57] highr_0.10                    ggsignif_0.6.4               
##  [59] biomaRt_2.56.0                rappdirs_0.3.3               
##  [61] DelayedArray_0.26.2           rjson_0.2.21                 
##  [63] tools_4.3.0                   interactiveDisplayBase_1.38.0
##  [65] httpuv_1.6.9                  glue_1.6.2                   
##  [67] restfulr_0.0.15               promises_1.2.0.1             
##  [69] generics_0.1.3                gtable_0.3.3                 
##  [71] KMsurv_0.1-5                  tzdb_0.3.0                   
##  [73] tidyr_1.3.0                   survminer_0.4.9              
##  [75] data.table_1.14.8             hms_1.1.3                    
##  [77] car_3.1-2                     xml2_1.3.3                   
##  [79] utf8_1.2.3                    XVector_0.40.0               
##  [81] BiocVersion_3.17.1            foreach_1.5.2                
##  [83] pillar_1.9.0                  stringr_1.5.0                
##  [85] later_1.3.0                   splines_4.3.0                
##  [87] BiocFileCache_2.8.0           lattice_0.21-8               
##  [89] rtracklayer_1.60.0            bit_4.0.5                    
##  [91] tidyselect_1.2.0              Biostrings_2.68.0            
##  [93] knitr_1.42                    gridExtra_2.3                
##  [95] bookdown_0.33                 xfun_0.38                    
##  [97] stringi_1.7.12                yaml_2.3.7                   
##  [99] evaluate_0.20                 codetools_0.2-19             
## [101] tibble_3.2.1                  BiocManager_1.30.20          
## [103] cli_3.6.1                     xtable_1.8-4                 
## [105] munsell_0.5.0                 jquerylib_0.1.4              
## [107] survMisc_0.5.6                Rcpp_1.0.10                  
## [109] GenomicDataCommons_1.24.0     dbplyr_2.3.2                 
## [111] png_0.1-8                     XML_3.99-0.14                
## [113] ellipsis_0.3.2                readr_2.1.4                  
## [115] blob_1.2.4                    prettyunits_1.1.1            
## [117] bitops_1.0-7                  scales_1.2.1                 
## [119] purrr_1.0.1                   crayon_1.5.2                 
## [121] rlang_1.1.0                   KEGGREST_1.40.0              
## [123] rvest_1.0.3                   formatR_1.14