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 4.4.2 (2024-10-31)
## 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_1.7-2 glmSparseNet_1.25.0
## [7] TCGAutils_1.27.6 curatedTCGAData_1.28.1
## [9] MultiAssayExperiment_1.33.8 SummarizedExperiment_1.37.0
## [11] Biobase_2.67.0 GenomicRanges_1.59.1
## [13] GenomeInfoDb_1.43.4 IRanges_2.41.2
## [15] S4Vectors_0.45.2 BiocGenerics_0.53.5
## [17] generics_0.1.3 MatrixGenerics_1.19.1
## [19] matrixStats_1.5.0 futile.logger_1.4.3
## [21] survival_3.8-3 ggplot2_3.5.1
## [23] dplyr_1.1.4 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_1.59.1 farver_2.1.2
## [7] rmarkdown_2.29 BiocIO_1.17.1
## [9] vctrs_0.6.5 memoise_2.0.1
## [11] Rsamtools_2.23.1 RCurl_1.98-1.16
## [13] rstatix_0.7.2 htmltools_0.5.8.1
## [15] S4Arrays_1.7.1 BiocBaseUtils_1.9.0
## [17] progress_1.2.3 AnnotationHub_3.15.0
## [19] lambda.r_1.2.4 curl_6.2.0
## [21] broom_1.0.7 Formula_1.2-5
## [23] SparseArray_1.7.4 pROC_1.18.5
## [25] sass_0.4.9 bslib_0.8.0
## [27] plyr_1.8.9 httr2_1.1.0
## [29] zoo_1.8-12 futile.options_1.0.1
## [31] cachem_1.1.0 buildtools_1.0.0
## [33] GenomicAlignments_1.43.0 mime_0.12
## [35] lifecycle_1.0.4 iterators_1.0.14
## [37] pkgconfig_2.0.3 R6_2.5.1
## [39] fastmap_1.2.0 GenomeInfoDbData_1.2.13
## [41] digest_0.6.37 colorspace_2.1-1
## [43] AnnotationDbi_1.69.0 ExperimentHub_2.15.0
## [45] RSQLite_2.3.9 ggpubr_0.6.0
## [47] filelock_1.0.3 labeling_0.4.3
## [49] km.ci_0.5-6 httr_1.4.7
## [51] abind_1.4-8 compiler_4.4.2
## [53] bit64_4.6.0-1 withr_3.0.2
## [55] backports_1.5.0 BiocParallel_1.41.0
## [57] carData_3.0-5 DBI_1.2.3
## [59] ggsignif_0.6.4 biomaRt_2.63.0
## [61] rappdirs_0.3.3 DelayedArray_0.33.4
## [63] rjson_0.2.23 tools_4.4.2
## [65] glue_1.8.0 restfulr_0.0.15
## [67] checkmate_2.3.2 gtable_0.3.6
## [69] KMsurv_0.1-5 tzdb_0.4.0
## [71] tidyr_1.3.1 survminer_0.5.0
## [73] data.table_1.16.4 hms_1.1.3
## [75] car_3.1-3 xml2_1.3.6
## [77] XVector_0.47.2 BiocVersion_3.21.1
## [79] foreach_1.5.2 pillar_1.10.1
## [81] stringr_1.5.1 splines_4.4.2
## [83] BiocFileCache_2.15.1 lattice_0.22-6
## [85] rtracklayer_1.67.0 bit_4.5.0.1
## [87] tidyselect_1.2.1 maketools_1.3.1
## [89] Biostrings_2.75.3 knitr_1.49
## [91] gridExtra_2.3 xfun_0.50
## [93] stringi_1.8.4 UCSC.utils_1.3.1
## [95] yaml_2.3.10 evaluate_1.0.3
## [97] codetools_0.2-20 tibble_3.2.1
## [99] BiocManager_1.30.25 cli_3.6.3
## [101] xtable_1.8-4 munsell_0.5.1
## [103] jquerylib_0.1.4 survMisc_0.5.6
## [105] Rcpp_1.0.14 GenomicDataCommons_1.31.0
## [107] dbplyr_2.5.0 png_0.1-8
## [109] XML_3.99-0.18 readr_2.1.5
## [111] blob_1.2.4 prettyunits_1.2.0
## [113] bitops_1.0-9 scales_1.3.0
## [115] purrr_1.0.2 crayon_1.5.3
## [117] rlang_1.1.5 KEGGREST_1.47.0
## [119] rvest_1.0.4 formatR_1.14