if (!require("BiocManager"))
install.packages("BiocManager")
BiocManager::install("glmSparseNet")
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())
data('cancer', package = 'survival')
xdata <- survival::ovarian[,c('age', 'resid.ds')]
ydata <- data.frame(
time = survival::ovarian$futime,
status = survival::ovarian$fustat
)
(group cutoff is median calculated relative risk)
res.age <- separate2GroupsCox(c(age = 1, 0), xdata, ydata)
## 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
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.
res.age.40.60 <-
separate2GroupsCox(c(age = 1, 0),
xdata,
ydata,
probs = c(.4, .6)
)
## 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
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.
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
## 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
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.
sessionInfo()
## R version 4.2.0 (2022-04-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so
##
## 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
##
## 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_0.5.1 glmSparseNet_1.14.1
## [5] glmnet_4.1-4 Matrix_1.4-1
## [7] TCGAutils_1.16.0 curatedTCGAData_1.18.0
## [9] MultiAssayExperiment_1.22.0 SummarizedExperiment_1.26.1
## [11] Biobase_2.56.0 GenomicRanges_1.48.0
## [13] GenomeInfoDb_1.32.2 IRanges_2.30.0
## [15] S4Vectors_0.34.0 BiocGenerics_0.42.0
## [17] MatrixGenerics_1.8.0 matrixStats_0.62.0
## [19] futile.logger_1.4.3 survival_3.3-1
## [21] ggplot2_3.3.6 dplyr_1.0.9
## [23] BiocStyle_2.24.0
##
## loaded via a namespace (and not attached):
## [1] backports_1.4.1 AnnotationHub_3.4.0
## [3] BiocFileCache_2.4.0 plyr_1.8.7
## [5] splines_4.2.0 BiocParallel_1.30.2
## [7] digest_0.6.29 foreach_1.5.2
## [9] htmltools_0.5.2 magick_2.7.3
## [11] fansi_1.0.3 magrittr_2.0.3
## [13] memoise_2.0.1 tzdb_0.3.0
## [15] Biostrings_2.64.0 readr_2.1.2
## [17] prettyunits_1.1.1 colorspace_2.0-3
## [19] blob_1.2.3 rvest_1.0.2
## [21] rappdirs_0.3.3 xfun_0.31
## [23] crayon_1.5.1 RCurl_1.98-1.6
## [25] jsonlite_1.8.0 zoo_1.8-10
## [27] iterators_1.0.14 glue_1.6.2
## [29] survminer_0.4.9 GenomicDataCommons_1.20.1
## [31] gtable_0.3.0 zlibbioc_1.42.0
## [33] XVector_0.36.0 DelayedArray_0.22.0
## [35] car_3.0-13 shape_1.4.6
## [37] abind_1.4-5 scales_1.2.0
## [39] futile.options_1.0.1 DBI_1.1.2
## [41] rstatix_0.7.0 Rcpp_1.0.8.3
## [43] xtable_1.8-4 progress_1.2.2
## [45] bit_4.0.4 km.ci_0.5-6
## [47] httr_1.4.3 ellipsis_0.3.2
## [49] pkgconfig_2.0.3 XML_3.99-0.9
## [51] farver_2.1.0 sass_0.4.1
## [53] dbplyr_2.1.1 utf8_1.2.2
## [55] tidyselect_1.1.2 labeling_0.4.2
## [57] rlang_1.0.2 later_1.3.0
## [59] AnnotationDbi_1.58.0 munsell_0.5.0
## [61] BiocVersion_3.15.2 tools_4.2.0
## [63] cachem_1.0.6 cli_3.3.0
## [65] generics_0.1.2 RSQLite_2.2.14
## [67] ExperimentHub_2.4.0 broom_0.8.0
## [69] evaluate_0.15 stringr_1.4.0
## [71] fastmap_1.1.0 yaml_2.3.5
## [73] knitr_1.39 bit64_4.0.5
## [75] survMisc_0.5.6 purrr_0.3.4
## [77] KEGGREST_1.36.0 mime_0.12
## [79] formatR_1.12 xml2_1.3.3
## [81] biomaRt_2.52.0 compiler_4.2.0
## [83] filelock_1.0.2 curl_4.3.2
## [85] png_0.1-7 interactiveDisplayBase_1.34.0
## [87] ggsignif_0.6.3 tibble_3.1.7
## [89] bslib_0.3.1 stringi_1.7.6
## [91] highr_0.9 GenomicFeatures_1.48.1
## [93] lattice_0.20-45 KMsurv_0.1-5
## [95] vctrs_0.4.1 pillar_1.7.0
## [97] lifecycle_1.0.1 BiocManager_1.30.17
## [99] jquerylib_0.1.4 data.table_1.14.2
## [101] bitops_1.0-7 httpuv_1.6.5
## [103] rtracklayer_1.56.0 R6_2.5.1
## [105] BiocIO_1.6.0 bookdown_0.26
## [107] promises_1.2.0.1 gridExtra_2.3
## [109] codetools_0.2-18 lambda.r_1.2.4
## [111] assertthat_0.2.1 rjson_0.2.21
## [113] withr_2.5.0 GenomicAlignments_1.32.0
## [115] Rsamtools_2.12.0 GenomeInfoDbData_1.2.8
## [117] hms_1.1.1 tidyr_1.2.0
## [119] rmarkdown_2.14 carData_3.0-5
## [121] ggpubr_0.4.0 pROC_1.18.0
## [123] shiny_1.7.1 restfulr_0.0.13