---
title: "Example for Survival Data -- Skin Melanoma"
author: "Eunice Carrasquinha and André Veríssimo"
date: "`r Sys.Date()`"
output: 
  BiocStyle::html_document:
    number_sections: yes
    toc: true
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{Example for Survival Data -- Skin Melanoma}
  %\VignetteEncoding{UTF-8}
params:
  seed: !r 8432
--- 

## Instalation

```{r, eval=FALSE}
if (!require('BiocManager'))
  install.packages('BiocManager')
BiocManager::install('glmSparseNet')
```

# Required Packages

```{r packages, message=FALSE, warning=FALSE}
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())
```

# 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.

```{r curated_data, include=FALSE}
# chunk not included as it produces to many unnecessary messages
skcm <- curatedTCGAData(diseaseCode = 'SKCM', assays = 'RNASeq2GeneNorm', FALSE, cache = tempdir())
```

```{r curated_data_non_eval, eval=FALSE}
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`

```{r data.show, warning=FALSE, error=FALSE}
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)
```

# Fit models

Fit model model penalizing by the hubs using the cross-validation function by 
`cv.glmHub`.

```{r fit}
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))
```

# 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.

```{r results}
plot(fitted)
```

## Coefficients of selected model from Cross-Validation

Taking the best model described by `lambda.min`

```{r show_coefs}
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()
```

## Hallmarks of Cancer

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

## Survival curves and Log rank test

```{r}
separate2GroupsCox(as.vector(coefs.v), 
                   xdata[, names(coefs.v)], 
                   ydata, 
                   plot.title = 'Full dataset', legend.outside = FALSE)
```

