Example for Survival Data – Prostate Adenocarcinoma
2025-10-07
Contents
1 Instalation
if (!require("BiocManager")) {
install.packages("BiocManager")
}
BiocManager::install("glmSparseNet")2 Required Packages
library(dplyr)
library(ggplot2)
library(survival)
library(futile.logger)
library(curatedTCGAData)
library(TCGAutils)
library(MultiAssayExperiment)
#
library(glmSparseNet)
#
# Some general options for futile.logger the debugging package
flog.layout(layout.format("[~l] ~m"))
options(
"glmSparseNet.show_message" = FALSE,
"glmSparseNet.base_dir" = withr::local_tempdir()
)
# Setting ggplot2 default theme as minimal
theme_set(ggplot2::theme_minimal())3 Load data
The data is loaded from an online curated dataset downloaded from TCGA using
curatedTCGAData bioconductor package and processed.
To accelerate the process we use a very reduced dataset down to around 100 variables only (genes), which is stored as a data object in this package. However, the procedure to obtain the data manually is described in the following chunk.
prad <- curatedTCGAData(
diseaseCode = "PRAD", assays = "RNASeq2GeneNorm",
version = "1.1.38", dry.run = FALSE
)Build the survival data from the clinical columns.
- Selects only primary solid tumour samples
- Merge survival times for patients, which have different columns in case they are alive or dead.
- Build two matrix objects that fit the data
xdataandydata
# keep only solid tumour (code: 01)
pradPrimarySolidTumor <- TCGAutils::TCGAsplitAssays(prad, "01")
xdataRaw <- t(assay(pradPrimarySolidTumor[[1]]))
# Get survival information
ydataRaw <- colData(pradPrimarySolidTumor) |>
as.data.frame() |>
# Find max time between all days (ignoring missings)
dplyr::rowwise() |>
dplyr::mutate(
time = max(days_to_last_followup, days_to_death, na.rm = TRUE)
) |>
# Keep only survival variables and codes
dplyr::select(patientID, status = vital_status, time) |>
# Discard individuals with survival time less or equal to 0
dplyr::filter(!is.na(time) & time > 0) |>
as.data.frame()
# Set index as the patientID
rownames(ydataRaw) <- ydataRaw$patientID
# keep only features that have standard deviation > 0
xdataRaw <- xdataRaw[
TCGAbarcode(rownames(xdataRaw)) %in% rownames(ydataRaw),
]
xdataRaw <- xdataRaw[, apply(xdataRaw, 2, sd) != 0] |>
scale()
# Order ydata the same as assay
ydataRaw <- ydataRaw[TCGAbarcode(rownames(xdataRaw)), ]
set.seed(params$seed)
smallSubset <- c(
geneNames(c(
"ENSG00000103091", "ENSG00000064787",
"ENSG00000119915", "ENSG00000120158",
"ENSG00000114491", "ENSG00000204176",
"ENSG00000138399"
))$external_gene_name,
sample(colnames(xdataRaw), 100)
) |>
unique() |>
sort()
xdata <- xdataRaw[, smallSubset[smallSubset %in% colnames(xdataRaw)]]
ydata <- ydataRaw |> dplyr::select(time, status)4 Fit models
Fit model model penalizing by the hubs using the cross-validation function by
cv.glmHub.
set.seed(params$seed)
fitted <- cv.glmHub(xdata, Surv(ydata$time, ydata$status),
family = "cox",
nlambda = 1000,
network = "correlation",
options = networkOptions(
cutoff = .6,
minDegree = .2
)
)5 Results of Cross Validation
Shows the results of 100 different parameters used to find the optimal value
in 10-fold cross-validation. The two vertical dotted lines represent the best
model and a model with less variables selected (genes), but within a standard
error distance from the best.
plot(fitted)
5.1 Coefficients of selected model from Cross-Validation
Taking the best model described by lambda.min
coefsCV <- Filter(function(.x) .x != 0, coef(fitted, s = "lambda.min")[, 1])
data.frame(
ensembl.id = names(coefsCV),
gene.name = geneNames(names(coefsCV))$external_gene_name,
coefficient = coefsCV,
stringsAsFactors = FALSE
) |>
arrange(gene.name) |>
knitr::kable()| ensembl.id | gene.name | coefficient | |
|---|---|---|---|
| AKAP9 | AKAP9 | AKAP9 | 0.2627933 |
| ALPK2 | ALPK2 | ALPK2 | -0.0738446 |
| ATP5G2 | ATP5G2 | ATP5G2 | -0.2585744 |
| C22orf32 | C22orf32 | C22orf32 | -0.2126287 |
| CSNK2A1P | CSNK2A1P | CSNK2A1P | -1.4902722 |
| MYST3 | MYST3 | MYST3 | -1.6228029 |
| NBPF10 | NBPF10 | NBPF10 | 0.4525259 |
| PFN1 | PFN1 | PFN1 | 0.4196058 |
| SCGB2A2 | SCGB2A2 | SCGB2A2 | 0.0745791 |
| SLC25A1 | SLC25A1 | SLC25A1 | -0.8526360 |
| STX4 | STX4 | STX4 | -0.1695387 |
| SYP | SYP | SYP | 0.2530633 |
| TMEM141 | TMEM141 | TMEM141 | -0.8307072 |
| UMPS | UMPS | UMPS | 0.2252967 |
| ZBTB26 | ZBTB26 | ZBTB26 | 0.3699276 |
5.2 Survival curves and Log rank test
separate2GroupsCox(as.vector(coefsCV),
xdata[, names(coefsCV)],
ydata,
plotTitle = "Full dataset", legendOutside = FALSE
)## $pvalue
## [1] 0.001155155
##
## $plot
##
## $km
## Call: survfit(formula = survival::Surv(time, status) ~ group, data = prognosticIndexDf)
##
## n events median 0.95LCL 0.95UCL
## Low risk - 1 249 0 NA NA NA
## High risk - 1 248 10 3502 3467 NA6 Session Info
sessionInfo()## R version 4.5.1 Patched (2025-08-23 r88802)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## 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_4.1-10 VennDiagram_1.7.3
## [3] reshape2_1.4.4 forcats_1.0.1
## [5] Matrix_1.7-4 glmSparseNet_1.27.0
## [7] TCGAutils_1.29.5 curatedTCGAData_1.31.2
## [9] MultiAssayExperiment_1.35.9 SummarizedExperiment_1.39.2
## [11] Biobase_2.69.1 GenomicRanges_1.61.5
## [13] Seqinfo_0.99.2 IRanges_2.43.5
## [15] S4Vectors_0.47.4 BiocGenerics_0.55.1
## [17] generics_0.1.4 MatrixGenerics_1.21.0
## [19] matrixStats_1.5.0 futile.logger_1.4.3
## [21] survival_3.8-3 ggplot2_4.0.0
## [23] dplyr_1.1.4 BiocStyle_2.37.1
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