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 <- loose.rock::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 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.14-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [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       
## 
## attached base packages:
##  [1] grid      parallel  stats4    stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] VennDiagram_1.6.20          reshape2_1.4.4             
##  [3] forcats_0.5.1               glmSparseNet_1.12.0        
##  [5] glmnet_4.1-2                Matrix_1.3-4               
##  [7] TCGAutils_1.14.0            curatedTCGAData_1.15.1     
##  [9] MultiAssayExperiment_1.20.0 SummarizedExperiment_1.24.0
## [11] Biobase_2.54.0              GenomicRanges_1.46.0       
## [13] GenomeInfoDb_1.30.0         IRanges_2.28.0             
## [15] S4Vectors_0.32.0            BiocGenerics_0.40.0        
## [17] MatrixGenerics_1.6.0        matrixStats_0.61.0         
## [19] futile.logger_1.4.3         loose.rock_1.2.0           
## [21] survival_3.2-13             ggplot2_3.3.5              
## [23] dplyr_1.0.7                 BiocStyle_2.22.0           
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1                  backports_1.2.1              
##   [3] AnnotationHub_3.2.0           BiocFileCache_2.2.0          
##   [5] plyr_1.8.6                    splines_4.1.1                
##   [7] sparsebn_0.1.2                BiocParallel_1.28.0          
##   [9] digest_0.6.28                 foreach_1.5.1                
##  [11] htmltools_0.5.2               magick_2.7.3                 
##  [13] fansi_0.5.0                   magrittr_2.0.1               
##  [15] memoise_2.0.0                 openxlsx_4.2.4               
##  [17] tzdb_0.1.2                    Biostrings_2.62.0            
##  [19] readr_2.0.2                   prettyunits_1.1.1            
##  [21] colorspace_2.0-2              blob_1.2.2                   
##  [23] rvest_1.0.2                   rappdirs_0.3.3               
##  [25] haven_2.4.3                   xfun_0.27                    
##  [27] crayon_1.4.1                  RCurl_1.98-1.5               
##  [29] jsonlite_1.7.2                zoo_1.8-9                    
##  [31] iterators_1.0.13              glue_1.4.2                   
##  [33] survminer_0.4.9               GenomicDataCommons_1.18.0    
##  [35] gtable_0.3.0                  zlibbioc_1.40.0              
##  [37] XVector_0.34.0                DelayedArray_0.20.0          
##  [39] car_3.0-11                    ccdrAlgorithm_0.0.5          
##  [41] shape_1.4.6                   abind_1.4-5                  
##  [43] discretecdAlgorithm_0.0.7     scales_1.1.1                 
##  [45] futile.options_1.0.1          DBI_1.1.1                    
##  [47] rstatix_0.7.0                 Rcpp_1.0.7                   
##  [49] xtable_1.8-4                  progress_1.2.2               
##  [51] foreign_0.8-81                bit_4.0.4                    
##  [53] km.ci_0.5-2                   httr_1.4.2                   
##  [55] ellipsis_0.3.2                pkgconfig_2.0.3              
##  [57] XML_3.99-0.8                  farver_2.1.0                 
##  [59] sass_0.4.0                    dbplyr_2.1.1                 
##  [61] utf8_1.2.2                    tidyselect_1.1.1             
##  [63] labeling_0.4.2                rlang_0.4.12                 
##  [65] later_1.3.0                   AnnotationDbi_1.56.0         
##  [67] cellranger_1.1.0              munsell_0.5.0                
##  [69] BiocVersion_3.14.0            tools_4.1.1                  
##  [71] cachem_1.0.6                  cli_3.0.1                    
##  [73] generics_0.1.1                RSQLite_2.2.8                
##  [75] ExperimentHub_2.2.0           broom_0.7.9                  
##  [77] evaluate_0.14                 stringr_1.4.0                
##  [79] fastmap_1.1.0                 yaml_2.2.1                   
##  [81] knitr_1.36                    bit64_4.0.5                  
##  [83] zip_2.2.0                     survMisc_0.5.5               
##  [85] purrr_0.3.4                   KEGGREST_1.34.0              
##  [87] mime_0.12                     formatR_1.11                 
##  [89] xml2_1.3.2                    biomaRt_2.50.0               
##  [91] compiler_4.1.1                filelock_1.0.2               
##  [93] curl_4.3.2                    png_0.1-7                    
##  [95] interactiveDisplayBase_1.32.0 ggsignif_0.6.3               
##  [97] tibble_3.1.5                  bslib_0.3.1                  
##  [99] stringi_1.7.5                 highr_0.9                    
## [101] sparsebnUtils_0.0.8           GenomicFeatures_1.46.0       
## [103] lattice_0.20-45               KMsurv_0.1-5                 
## [105] vctrs_0.3.8                   pillar_1.6.4                 
## [107] lifecycle_1.0.1               BiocManager_1.30.16          
## [109] jquerylib_0.1.4               data.table_1.14.2            
## [111] bitops_1.0-7                  httpuv_1.6.3                 
## [113] rtracklayer_1.54.0            R6_2.5.1                     
## [115] BiocIO_1.4.0                  bookdown_0.24                
## [117] promises_1.2.0.1              gridExtra_2.3                
## [119] rio_0.5.27                    codetools_0.2-18             
## [121] lambda.r_1.2.4                assertthat_0.2.1             
## [123] rjson_0.2.20                  withr_2.4.2                  
## [125] GenomicAlignments_1.30.0      Rsamtools_2.10.0             
## [127] GenomeInfoDbData_1.2.7        hms_1.1.1                    
## [129] tidyr_1.1.4                   rmarkdown_2.11               
## [131] carData_3.0-4                 ggpubr_0.4.0                 
## [133] pROC_1.18.0                   shiny_1.7.1                  
## [135] restfulr_0.0.13