Example for Survival Data – Skin Melanoma
2024-04-19
Contents
0.1 Instalation
if (!require("BiocManager")) {
install.packages("BiocManager")
}
BiocManager::install("glmSparseNet")1 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())2 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 107 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.
skcm <- curatedTCGAData(
diseaseCode = "SKCM", assays = "RNASeq2GeneNorm",
version = "1.1.38", dry.run = FALSE
)Build the survival data from the clinical columns.
- 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
skcmMetastatic <- TCGAutils::TCGAsplitAssays(skcm, "06")
xdataRaw <- t(assay(skcmMetastatic[[1]]))
# Get survival information
ydataRaw <- colData(skcmMetastatic) |>
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()
# Get survival information
ydataRaw <- colData(skcm) |>
as.data.frame() |>
# Find max time between all days (ignoring missings)
dplyr::filter(
!is.na(days_to_last_followup) | !is.na(days_to_death)
) |>
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(
"FOXL2", "KLHL5", "PCYT2", "SLC6A10P", "STRAP", "TMEM33",
"WT1-AS", sample(colnames(xdataRaw), 100)
)
xdata <- xdataRaw[, smallSubset[smallSubset %in% colnames(xdataRaw)]]
ydata <- ydataRaw |> dplyr::select(time, status)3 Fit models
Fit model model penalizing by the hubs using the cross-validation function by
cv.glmHub.
fitted <- cv.glmHub(
xdata,
Surv(ydata$time, ydata$status),
family = "cox",
foldid = glmSparseNet:::balancedCvFolds(ydata$status)$output,
network = "correlation",
options = networkOptions(
cutoff = .6,
minDegree = .2
)
)4 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)
4.1 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 | |
|---|---|---|---|
| AMICA1 | AMICA1 | AMICA1 | -0.2758400 |
| C4orf49 | C4orf49 | C4orf49 | -0.0059089 |
| PCYT2 | PCYT2 | PCYT2 | 0.0646641 |
4.2 Survival curves and Log rank test
separate2GroupsCox(as.vector(coefsCV),
xdata[, names(coefsCV)],
ydata,
plotTitle = "Full dataset", legendOutside = FALSE
)## $pvalue
## [1] 0.0001269853
##
## $plot
##
## $km
## Call: survfit(formula = survival::Surv(time, status) ~ group, data = prognosticIndexDf)
##
## n events median 0.95LCL 0.95UCL
## Low risk - 1 180 79 4000 2927 6164
## High risk - 1 179 114 2005 1524 28295 Session Info
sessionInfo()## R version 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-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
##
## other attached packages:
## [1] glmnet_4.1-8 VennDiagram_1.7.3
## [3] reshape2_1.4.4 forcats_1.0.0
## [5] Matrix_1.7-0 glmSparseNet_1.21.2
## [7] TCGAutils_1.23.4 curatedTCGAData_1.25.4
## [9] MultiAssayExperiment_1.29.1 SummarizedExperiment_1.33.3
## [11] Biobase_2.63.1 GenomicRanges_1.55.4
## [13] GenomeInfoDb_1.39.14 IRanges_2.37.1
## [15] S4Vectors_0.41.6 BiocGenerics_0.49.1
## [17] MatrixGenerics_1.15.1 matrixStats_1.3.0
## [19] futile.logger_1.4.3 survival_3.5-8
## [21] ggplot2_3.5.0 dplyr_1.1.4
## [23] BiocStyle_2.31.0
##
## loaded via a namespace (and not attached):
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