# Example for Survival Data – Prostate Adenocarcinoma

Marta Lopes and André Veríssimo

#### 2020-04-28

# Contents

# 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", 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 | 0.2616307
ALPK2 | ALPK2 | -0.0714527
ATP5G2 | ATP5G2 | -0.2575987
C22orf32 | C22orf32 | -0.2119992
CSNK2A1P | CSNK2A1P | -1.4875518
MYST3 | MYST3 | -1.6177076
NBPF10 | NBPF10 | 0.4507147
PFN1 | PFN1 | 0.4161846
SCGB2A2 | SCGB2A2 | 0.0749064
SLC25A1 | SLC25A1 | -0.8484827
STX4 | STX4 | -0.1690185
SYP | SYP | 0.2425939
TMEM141 | TMEM141 | -0.8273147
UMPS | UMPS | 0.2214068
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