1 Instalation

if (!require("BiocManager"))
  install.packages("BiocManager")
BiocManager::install("glmSparseNet")

2 Required Packages

library(dplyr)
library(ggplot2)
library(survival)
library(loose.rock)
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 <- loose.rock::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 xdata and ydata

# keep only solid tumour (code: 01)
prad.primary.solid.tumor <- TCGAutils::splitAssays(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)
  rowwise %>%
  mutate(time = max(days_to_last_followup, days_to_death, na.rm = TRUE)) %>%
  # Keep only survival variables and codes
  select(patientID, status = vital_status, time) %>% 
  # Discard individuals with survival time less or equal to 0
  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 %>% 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 Hallmarks of Cancer

geneNames(names(coefs.v)) %>% { hallmarks(.$external_gene_name)$heatmap }

5.3 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      NA

Example for Survival Data – Prostate Adenocarcinoma

2021-05-19

Contents