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 4.4.0 alpha (2024-03-27 r86216)
## Platform: aarch64-apple-darwin20
## Running under: macOS Ventura 13.6.5
## 
## 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] en_US.UTF-8/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_1.7-0                glmSparseNet_1.22.0        
##  [7] TCGAutils_1.24.0            curatedTCGAData_1.25.4     
##  [9] MultiAssayExperiment_1.30.0 SummarizedExperiment_1.34.0
## [11] Biobase_2.64.0              GenomicRanges_1.56.0       
## [13] GenomeInfoDb_1.40.0         IRanges_2.38.0             
## [15] S4Vectors_0.42.0            BiocGenerics_0.50.0        
## [17] MatrixGenerics_1.16.0       matrixStats_1.2.0          
## [19] futile.logger_1.4.3         survival_3.5-8             
## [21] ggplot2_3.5.0               dplyr_1.1.4                
## [23] BiocStyle_2.32.0           
## 
## loaded via a namespace (and not attached):
##   [1] jsonlite_1.8.8            shape_1.4.6.1            
##   [3] magrittr_2.0.3            magick_2.8.3             
##   [5] GenomicFeatures_1.56.0    farver_2.1.1             
##   [7] rmarkdown_2.26            BiocIO_1.14.0            
##   [9] zlibbioc_1.50.0           vctrs_0.6.5              
##  [11] memoise_2.0.1             Rsamtools_2.20.0         
##  [13] RCurl_1.98-1.14           rstatix_0.7.2            
##  [15] progress_1.2.3            htmltools_0.5.8          
##  [17] S4Arrays_1.4.0            BiocBaseUtils_1.6.0      
##  [19] AnnotationHub_3.12.0      lambda.r_1.2.4           
##  [21] curl_5.2.1                broom_1.0.5              
##  [23] pROC_1.18.5               SparseArray_1.4.0        
##  [25] sass_0.4.9                bslib_0.6.2              
##  [27] plyr_1.8.9                httr2_1.0.0              
##  [29] zoo_1.8-12                futile.options_1.0.1     
##  [31] cachem_1.0.8              GenomicAlignments_1.40.0 
##  [33] mime_0.12                 lifecycle_1.0.4          
##  [35] iterators_1.0.14          pkgconfig_2.0.3          
##  [37] R6_2.5.1                  fastmap_1.1.1            
##  [39] GenomeInfoDbData_1.2.12   digest_0.6.35            
##  [41] colorspace_2.1-0          AnnotationDbi_1.66.0     
##  [43] ps_1.7.6                  ExperimentHub_2.12.0     
##  [45] RSQLite_2.3.5             ggpubr_0.6.0             
##  [47] labeling_0.4.3            filelock_1.0.3           
##  [49] km.ci_0.5-6               fansi_1.0.6              
##  [51] httr_1.4.7                abind_1.4-5              
##  [53] compiler_4.4.0            bit64_4.0.5              
##  [55] withr_3.0.0               backports_1.4.1          
##  [57] BiocParallel_1.38.0       carData_3.0-5            
##  [59] DBI_1.2.2                 highr_0.10               
##  [61] ggsignif_0.6.4            biomaRt_2.60.0           
##  [63] rappdirs_0.3.3            DelayedArray_0.30.0      
##  [65] rjson_0.2.21              tools_4.4.0              
##  [67] chromote_0.2.0            glue_1.7.0               
##  [69] restfulr_0.0.15           promises_1.2.1           
##  [71] checkmate_2.3.1           generics_0.1.3           
##  [73] gtable_0.3.4              KMsurv_0.1-5             
##  [75] tzdb_0.4.0                tidyr_1.3.1              
##  [77] survminer_0.4.9           websocket_1.4.1          
##  [79] data.table_1.15.4         hms_1.1.3                
##  [81] car_3.1-2                 xml2_1.3.6               
##  [83] utf8_1.2.4                XVector_0.44.0           
##  [85] BiocVersion_3.19.1        foreach_1.5.2            
##  [87] pillar_1.9.0              stringr_1.5.1            
##  [89] later_1.3.2               splines_4.4.0            
##  [91] BiocFileCache_2.12.0      lattice_0.22-6           
##  [93] rtracklayer_1.64.0        bit_4.0.5                
##  [95] tidyselect_1.2.1          Biostrings_2.72.0        
##  [97] knitr_1.45                gridExtra_2.3            
##  [99] bookdown_0.38             xfun_0.43                
## [101] stringi_1.8.3             UCSC.utils_1.0.0         
## [103] yaml_2.3.8                evaluate_0.23            
## [105] codetools_0.2-19          tibble_3.2.1             
## [107] BiocManager_1.30.22       cli_3.6.2                
## [109] xtable_1.8-4              munsell_0.5.0            
## [111] processx_3.8.4            jquerylib_0.1.4          
## [113] survMisc_0.5.6            Rcpp_1.0.12              
## [115] GenomicDataCommons_1.28.0 dbplyr_2.5.0             
## [117] png_0.1-8                 XML_3.99-0.16.1          
## [119] readr_2.1.5               blob_1.2.4               
## [121] prettyunits_1.2.0         bitops_1.0-7             
## [123] scales_1.3.0              purrr_1.0.2              
## [125] crayon_1.5.2              rlang_1.1.3              
## [127] KEGGREST_1.44.0           rvest_1.0.4              
## [129] formatR_1.14