# Example for Survival Data – Skin Melanoma

Eunice Carrasquinha and André Veríssimo

#### 2020-04-28

# Contents

## 0.1 Instalation

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

# 1 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())

# 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', FALSE, cache = tempdir())

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 `xdata` and `ydata`

    skcm.metastatic <- TCGAutils::splitAssays(skcm, '06')
    xdata.raw <- t(assay(skcm.metastatic[[1]]))

    # Get survival information
    ydata.raw <- colData(skcm.metastatic) %>% 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

    # Get survival information
    ydata.raw <- colData(skcm) %>% 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('FOXL2', 'KLHL5', 'PCYT2', 'SLC6A10P', 'STRAP', 'TMEM33',
                      'WT1-AS', sample(colnames(xdata.raw), 100))

    xdata <- xdata.raw[, small.subset[small.subset %in% colnames(xdata.raw)]]
    ydata <- ydata.raw %>% 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  = balanced.cv.folds(!!ydata$status)$output,
                        network = 'correlation',
                        network.options = networkOptions(min.degree = .2,
                                                         cutoff = .6))

# 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`

    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
--- | --- | ---
PCYT2 | AMICA1 | 0.0646641
AMICA1 | C4orf49 | -0.2758400
C4orf49 | PCYT2 | -0.0059089

## 4.2 Hallmarks of Cancer

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

## 4.3 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] 0.0001269853
    ##
    ## $plot

    ##
    ## $km
    ## Call: survfit(formula = survival::Surv(time, status) ~ group, data = prognostic.index.df)
    ##
    ##             n events median 0.95LCL 0.95UCL
    ## Low risk  180     79   4000    2927    6164
    ## High risk 179    114   2005    1524    2829