Example for Survival Data – Prostate Adenocarcinoma
2024-02-05
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(glmSparseNet)
#
# 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())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)
prad.primary.solid.tumor <- TCGAutils::TCGAsplitAssays(prad, '01')
xdata.raw <- t(assay(prad.primary.solid.tumor[[1]]))
# Get survival information
ydata.raw <- colData(prad.primary.solid.tumor) %>% 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(ydata.raw) <- ydata.raw$patientID
# keep only features that have standard deviation > 0
xdata.raw <- xdata.raw[TCGAbarcode(rownames(xdata.raw)) %in%
rownames(ydata.raw),]
xdata.raw <- xdata.raw %>%
{ (apply(., 2, sd) != 0) } %>%
{ xdata.raw[, .] } %>%
scale
# Order ydata the same as assay
ydata.raw <- ydata.raw[TCGAbarcode(rownames(xdata.raw)), ]
set.seed(params$seed)
small.subset <- c(geneNames(c('ENSG00000103091', 'ENSG00000064787',
'ENSG00000119915', 'ENSG00000120158',
'ENSG00000114491', 'ENSG00000204176',
'ENSG00000138399'))$external_gene_name,
sample(colnames(xdata.raw), 100)) %>% unique %>% sort
xdata <- xdata.raw[, small.subset[small.subset %in% colnames(xdata.raw)]]
ydata <- ydata.raw %>% dplyr::select(time, status)4 Fit models
Fit model model penalizing by the hubs using the cross-validation function by
cv.glmHub.
set.seed(params$seed)
fitted <- cv.glmHub(xdata, Surv(ydata$time, ydata$status),
family = 'cox',
nlambda = 1000,
network = 'correlation',
network.options = networkOptions(cutoff = .6,
min.degree = .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
coefs.v <- coef(fitted, s = 'lambda.min')[,1] %>% { .[. != 0]}
coefs.v %>% {
data.frame(ensembl.id = names(.),
gene.name = geneNames(names(.))$external_gene_name,
coefficient = .,
stringsAsFactors = FALSE)
} %>%
arrange(gene.name) %>%
knitr::kable()| ensembl.id | gene.name | coefficient | |
|---|---|---|---|
| AKAP9 | AKAP9 | AKAP9 | 0.2616307 |
| ALPK2 | ALPK2 | ALPK2 | -0.0714527 |
| ATP5G2 | ATP5G2 | ATP5G2 | -0.2575987 |
| C22orf32 | C22orf32 | C22orf32 | -0.2119992 |
| CSNK2A1P | CSNK2A1P | CSNK2A1P | -1.4875518 |
| MYST3 | MYST3 | MYST3 | -1.6177076 |
| NBPF10 | NBPF10 | NBPF10 | 0.4507147 |
| PFN1 | PFN1 | PFN1 | 0.4161846 |
| SCGB2A2 | SCGB2A2 | SCGB2A2 | 0.0749064 |
| SLC25A1 | SLC25A1 | SLC25A1 | -0.8484827 |
| STX4 | STX4 | STX4 | -0.1690185 |
| SYP | SYP | SYP | 0.2425939 |
| TMEM141 | TMEM141 | TMEM141 | -0.8273147 |
| UMPS | UMPS | UMPS | 0.2214068 |
| ZBTB26 | ZBTB26 | ZBTB26 | 0.3696515 |
5.2 Survival curves and Log rank test
separate2GroupsCox(as.vector(coefs.v),
xdata[, names(coefs.v)],
ydata,
plot.title = 'Full dataset', legend.outside = FALSE)## $pvalue
## [1] 0.001155155
##
## $plot
##
## $km
## Call: survfit(formula = survival::Surv(time, status) ~ group, data = prognostic.index.df)
##
## n events median 0.95LCL 0.95UCL
## Low risk 249 0 NA NA NA
## High risk 248 10 3502 3467 NA6 Session Info
sessionInfo()## R version 4.3.2 Patched (2023-11-13 r85521)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.18-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
## [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
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## other attached packages:
## [1] VennDiagram_1.7.3 reshape2_1.4.4
## [3] forcats_1.0.0 glmSparseNet_1.20.1
## [5] glmnet_4.1-8 Matrix_1.6-5
## [7] TCGAutils_1.22.2 curatedTCGAData_1.24.0
## [9] MultiAssayExperiment_1.28.0 SummarizedExperiment_1.32.0
## [11] Biobase_2.62.0 GenomicRanges_1.54.1
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## [17] MatrixGenerics_1.14.0 matrixStats_1.2.0
## [19] futile.logger_1.4.3 survival_3.5-7
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