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 RC (2024-04-16 r86468)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## 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       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## 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_1.23.0        
##  [7] TCGAutils_1.25.0            curatedTCGAData_1.25.4     
##  [9] MultiAssayExperiment_1.31.0 SummarizedExperiment_1.35.0
## [11] Biobase_2.65.0              GenomicRanges_1.57.0       
## [13] GenomeInfoDb_1.41.0         IRanges_2.39.0             
## [15] S4Vectors_0.43.0            BiocGenerics_0.51.0        
## [17] MatrixGenerics_1.17.0       matrixStats_1.3.0          
## [19] futile.logger_1.4.3         survival_3.6-4             
## [21] ggplot2_3.5.1               dplyr_1.1.4                
## [23] BiocStyle_2.33.0           
## 
## 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_1.57.0    farver_2.1.1             
##   [7] rmarkdown_2.26            BiocIO_1.15.0            
##   [9] zlibbioc_1.51.0           vctrs_0.6.5              
##  [11] memoise_2.0.1             Rsamtools_2.21.0         
##  [13] RCurl_1.98-1.14           rstatix_0.7.2            
##  [15] tinytex_0.50              progress_1.2.3           
##  [17] htmltools_0.5.8.1         S4Arrays_1.5.0           
##  [19] BiocBaseUtils_1.7.0       AnnotationHub_3.13.0     
##  [21] lambda.r_1.2.4            curl_5.2.1               
##  [23] broom_1.0.5               pROC_1.18.5              
##  [25] SparseArray_1.5.0         sass_0.4.9               
##  [27] bslib_0.7.0               plyr_1.8.9               
##  [29] httr2_1.0.1               zoo_1.8-12               
##  [31] futile.options_1.0.1      cachem_1.0.8             
##  [33] GenomicAlignments_1.41.0  mime_0.12                
##  [35] lifecycle_1.0.4           iterators_1.0.14         
##  [37] pkgconfig_2.0.3           R6_2.5.1                 
##  [39] fastmap_1.1.1             GenomeInfoDbData_1.2.12  
##  [41] digest_0.6.35             colorspace_2.1-0         
##  [43] AnnotationDbi_1.67.0      ps_1.7.6                 
##  [45] ExperimentHub_2.13.0      RSQLite_2.3.6            
##  [47] ggpubr_0.6.0              labeling_0.4.3           
##  [49] filelock_1.0.3            km.ci_0.5-6              
##  [51] fansi_1.0.6               httr_1.4.7               
##  [53] abind_1.4-5               compiler_4.4.0           
##  [55] bit64_4.0.5               withr_3.0.0              
##  [57] backports_1.4.1           BiocParallel_1.39.0      
##  [59] carData_3.0-5             DBI_1.2.2                
##  [61] highr_0.10                ggsignif_0.6.4           
##  [63] biomaRt_2.61.0            rappdirs_0.3.3           
##  [65] DelayedArray_0.31.0       rjson_0.2.21             
##  [67] tools_4.4.0               chromote_0.2.0           
##  [69] glue_1.7.0                restfulr_0.0.15          
##  [71] promises_1.3.0            checkmate_2.3.1          
##  [73] generics_0.1.3            gtable_0.3.5             
##  [75] KMsurv_0.1-5              tzdb_0.4.0               
##  [77] tidyr_1.3.1               survminer_0.4.9          
##  [79] websocket_1.4.1           data.table_1.15.4        
##  [81] hms_1.1.3                 car_3.1-2                
##  [83] xml2_1.3.6                utf8_1.2.4               
##  [85] XVector_0.45.0            BiocVersion_3.20.0       
##  [87] foreach_1.5.2             pillar_1.9.0             
##  [89] stringr_1.5.1             later_1.3.2              
##  [91] splines_4.4.0             BiocFileCache_2.13.0     
##  [93] lattice_0.22-6            rtracklayer_1.65.0       
##  [95] bit_4.0.5                 tidyselect_1.2.1         
##  [97] Biostrings_2.73.0         knitr_1.46               
##  [99] gridExtra_2.3             bookdown_0.39            
## [101] xfun_0.43                 stringi_1.8.3            
## [103] UCSC.utils_1.1.0          yaml_2.3.8               
## [105] evaluate_0.23             codetools_0.2-20         
## [107] tibble_3.2.1              BiocManager_1.30.22      
## [109] cli_3.6.2                 xtable_1.8-4             
## [111] munsell_0.5.1             processx_3.8.4           
## [113] jquerylib_0.1.4           survMisc_0.5.6           
## [115] Rcpp_1.0.12               GenomicDataCommons_1.29.0
## [117] dbplyr_2.5.0              png_0.1-8                
## [119] XML_3.99-0.16.1           readr_2.1.5              
## [121] blob_1.2.4                prettyunits_1.2.0        
## [123] bitops_1.0-7              scales_1.3.0             
## [125] purrr_1.0.2               crayon_1.5.2             
## [127] rlang_1.1.3               KEGGREST_1.45.0          
## [129] rvest_1.0.4               formatR_1.14