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())
(group cutoff is median calculated relative risk)
## 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
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.
## 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
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.
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
## 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
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.
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 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: Etc/UTC
## 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_1.7-1 glmSparseNet_1.25.0
## [7] TCGAutils_1.25.1 curatedTCGAData_1.27.1
## [9] MultiAssayExperiment_1.31.5 SummarizedExperiment_1.35.5
## [11] Biobase_2.67.0 GenomicRanges_1.57.2
## [13] GenomeInfoDb_1.41.2 IRanges_2.39.2
## [15] S4Vectors_0.43.2 BiocGenerics_0.53.0
## [17] MatrixGenerics_1.17.1 matrixStats_1.4.1
## [19] futile.logger_1.4.3 survival_3.7-0
## [21] ggplot2_3.5.1 dplyr_1.1.4
## [23] BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] sys_3.4.3 jsonlite_1.8.9
## [3] shape_1.4.6.1 magrittr_2.0.3
## [5] GenomicFeatures_1.57.1 farver_2.1.2
## [7] rmarkdown_2.28 BiocIO_1.17.0
## [9] zlibbioc_1.51.2 vctrs_0.6.5
## [11] memoise_2.0.1 Rsamtools_2.21.2
## [13] RCurl_1.98-1.16 rstatix_0.7.2
## [15] htmltools_0.5.8.1 S4Arrays_1.5.11
## [17] BiocBaseUtils_1.9.0 progress_1.2.3
## [19] AnnotationHub_3.15.0 lambda.r_1.2.4
## [21] curl_5.2.3 broom_1.0.7
## [23] Formula_1.2-5 pROC_1.18.5
## [25] SparseArray_1.5.45 sass_0.4.9
## [27] bslib_0.8.0 plyr_1.8.9
## [29] httr2_1.0.5 zoo_1.8-12
## [31] futile.options_1.0.1 cachem_1.1.0
## [33] buildtools_1.0.0 GenomicAlignments_1.41.0
## [35] mime_0.12 lifecycle_1.0.4
## [37] iterators_1.0.14 pkgconfig_2.0.3
## [39] R6_2.5.1 fastmap_1.2.0
## [41] GenomeInfoDbData_1.2.13 digest_0.6.37
## [43] colorspace_2.1-1 AnnotationDbi_1.69.0
## [45] ExperimentHub_2.13.1 RSQLite_2.3.7
## [47] ggpubr_0.6.0 filelock_1.0.3
## [49] labeling_0.4.3 km.ci_0.5-6
## [51] fansi_1.0.6 httr_1.4.7
## [53] abind_1.4-8 compiler_4.4.1
## [55] bit64_4.5.2 withr_3.0.2
## [57] backports_1.5.0 BiocParallel_1.41.0
## [59] carData_3.0-5 DBI_1.2.3
## [61] highr_0.11 ggsignif_0.6.4
## [63] biomaRt_2.63.0 rappdirs_0.3.3
## [65] DelayedArray_0.33.1 rjson_0.2.23
## [67] tools_4.4.1 glue_1.8.0
## [69] restfulr_0.0.15 checkmate_2.3.2
## [71] generics_0.1.3 gtable_0.3.6
## [73] KMsurv_0.1-5 tzdb_0.4.0
## [75] tidyr_1.3.1 survminer_0.4.9
## [77] data.table_1.16.2 hms_1.1.3
## [79] car_3.1-3 xml2_1.3.6
## [81] utf8_1.2.4 XVector_0.45.0
## [83] BiocVersion_3.21.1 foreach_1.5.2
## [85] pillar_1.9.0 stringr_1.5.1
## [87] splines_4.4.1 BiocFileCache_2.15.0
## [89] lattice_0.22-6 rtracklayer_1.65.0
## [91] bit_4.5.0 tidyselect_1.2.1
## [93] maketools_1.3.1 Biostrings_2.75.0
## [95] knitr_1.48 gridExtra_2.3
## [97] xfun_0.48 stringi_1.8.4
## [99] UCSC.utils_1.1.0 yaml_2.3.10
## [101] evaluate_1.0.1 codetools_0.2-20
## [103] tibble_3.2.1 BiocManager_1.30.25
## [105] cli_3.6.3 xtable_1.8-4
## [107] munsell_0.5.1 jquerylib_0.1.4
## [109] survMisc_0.5.6 Rcpp_1.0.13
## [111] GenomicDataCommons_1.29.7 dbplyr_2.5.0
## [113] png_0.1-8 XML_3.99-0.17
## [115] readr_2.1.5 blob_1.2.4
## [117] prettyunits_1.2.0 bitops_1.0-9
## [119] scales_1.3.0 purrr_1.0.2
## [121] crayon_1.5.3 rlang_1.1.4
## [123] KEGGREST_1.45.1 rvest_1.0.4
## [125] formatR_1.14