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 r84269)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.2 LTS
##
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.17-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
## [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] 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
##  [9] MultiAssayExperiment_1.26.0 SummarizedExperiment_1.30.0
## [11] Biobase_2.60.0              GenomicRanges_1.52.0
## [13] GenomeInfoDb_1.36.0         IRanges_2.34.0
## [15] S4Vectors_0.38.0            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_1.1.2
## [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_0.6.2
##  [11] memoise_2.0.1                 Rsamtools_2.16.0
##  [13] RCurl_1.98-1.12               rstatix_0.7.2
##  [15] htmltools_0.5.5               progress_1.2.2
##  [17] AnnotationHub_3.8.0           lambda.r_1.2.4
##  [19] curl_5.0.0                    broom_1.0.4
##  [21] pROC_1.18.0                   sass_0.4.5
##  [23] bslib_0.4.2                   plyr_1.8.8
##  [25] zoo_1.8-12                    futile.options_1.0.1
##  [27] cachem_1.0.7                  GenomicAlignments_1.36.0
##  [29] mime_0.12                     lifecycle_1.0.3
##  [31] iterators_1.0.14              pkgconfig_2.0.3
##  [33] R6_2.5.1                      fastmap_1.1.1
##  [35] GenomeInfoDbData_1.2.10       shiny_1.7.4
##  [37] digest_0.6.31                 colorspace_2.1-0
##  [39] AnnotationDbi_1.62.0          ExperimentHub_2.8.0
##  [41] RSQLite_2.3.1                 ggpubr_0.6.0
##  [43] filelock_1.0.2                labeling_0.4.2
##  [45] km.ci_0.5-6                   fansi_1.0.4
##  [47] abind_1.4-5                   httr_1.4.5
##  [49] compiler_4.3.0                bit64_4.0.5
##  [51] withr_2.5.0                   backports_1.4.1
##  [53] BiocParallel_1.34.0           carData_3.0-5
##  [55] DBI_1.1.3                     highr_0.10
##  [57] ggsignif_0.6.4                biomaRt_2.56.0
##  [59] rappdirs_0.3.3                DelayedArray_0.26.0
##  [61] rjson_0.2.21                  tools_4.3.0
##  [63] interactiveDisplayBase_1.38.0 httpuv_1.6.9
##  [65] glue_1.6.2                    restfulr_0.0.15
##  [67] promises_1.2.0.1              generics_0.1.3
##  [69] gtable_0.3.3                  KMsurv_0.1-5
##  [71] tzdb_0.3.0                    tidyr_1.3.0
##  [73] survminer_0.4.9               data.table_1.14.8
##  [75] hms_1.1.3                     car_3.1-2
##  [77] xml2_1.3.3                    utf8_1.2.3
##  [79] XVector_0.40.0                BiocVersion_3.17.1
##  [81] foreach_1.5.2                 pillar_1.9.0
##  [83] stringr_1.5.0                 later_1.3.0
##  [85] splines_4.3.0                 BiocFileCache_2.8.0
##  [87] lattice_0.21-8                rtracklayer_1.60.0
##  [89] bit_4.0.5                     tidyselect_1.2.0
##  [91] Biostrings_2.68.0             knitr_1.42
##  [93] gridExtra_2.3                 bookdown_0.33
##  [95] xfun_0.39                     stringi_1.7.12
##  [97] yaml_2.3.7                    evaluate_0.20
##  [99] codetools_0.2-19              tibble_3.2.1
## [101] BiocManager_1.30.20           cli_3.6.1
## [103] xtable_1.8-4                  munsell_0.5.0
## [105] jquerylib_0.1.4               survMisc_0.5.6
## [107] Rcpp_1.0.10                   GenomicDataCommons_1.24.0
## [109] dbplyr_2.3.2                  png_0.1-8
## [111] XML_3.99-0.14                 ellipsis_0.3.2
## [123] formatR_1.14