---
title: "Example for Survival Data -- Breast Invasive Carcinoma"
author: "André Veríssimo"
date: "`r Sys.Date()`"
output: 
  BiocStyle::html_document:
    number_sections: yes
    toc: true
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{Example for Survival Data -- Breast Invasive Carcinoma}
  %\VignetteEncoding{UTF-8}
params:
  seed: !r 41
--- 

## 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(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 <- glmSparseNet:::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
brca <- curatedTCGAData(diseaseCode = "BRCA", assays = "RNASeq2GeneNorm",
                        version = "1.1.38", dry.run = FALSE)
```

```{r curated_data_non_eval, eval=FALSE}
brca <- curatedTCGAData(diseaseCode = "BRCA", assays = "RNASeq2GeneNorm",
                        version = "1.1.38", dry.run = FALSE)
```

```{r data, warning=FALSE, message=FALSE}
# keep only solid tumour (code: 01)
brca.primary.solid.tumor <- TCGAutils::TCGAsplitAssays(brca, '01')
xdata.raw <- t(assay(brca.primary.solid.tumor[[1]]))

# Get survival information
ydata.raw <- colData(brca.primary.solid.tumor) %>% as.data.frame %>% 
  # Keep only data relative to survival or samples
  dplyr::select(patientID, vital_status, 
         Days.to.date.of.Death, Days.to.Date.of.Last.Contact, 
         days_to_death,         days_to_last_followup,
         Vital.Status) %>% 
  # Convert days to integer
  dplyr::mutate(Days.to.date.of.Death = as.integer(Days.to.date.of.Death)) %>%
  dplyr::mutate(
    Days.to.Last.Contact  = as.integer(Days.to.Date.of.Last.Contact)
  ) %>%
  # Find max time between all days (ignoring missings)
  dplyr::rowwise() %>%
  dplyr::mutate(
    time = max(days_to_last_followup, Days.to.date.of.Death, 
               Days.to.Last.Contact, 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(ydata.raw) <- ydata.raw$patientID

# Get matches between survival and assay data
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)), ]

# Using only a subset of genes previously selected to keep this short example.
set.seed(params$seed)
small.subset <- c('CD5', 'CSF2RB', 'IRGC', 'NEUROG2', 'NLRC4', 'PDE11A',  
                  'PTEN', 'TP53', 'BRAF',
                  'PIK3CB', 'QARS', 'RFC3', 'RPGRIP1L', 'SDC1', 'TMEM31', 
                  'YME1L1', 'ZBTB11', sample(colnames(xdata.raw), 100)) %>% 
  unique

xdata <- xdata.raw[, small.subset[small.subset %in% colnames(xdata.raw)]]
ydata <- ydata.raw %>% dplyr::select(time, status)
```

# Fit models

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

```{r fit}
set.seed(params$seed)
fitted <- cv.glmHub(xdata, Surv(ydata$time, ydata$status),
                    family  = 'cox',
                    lambda = buildLambda(1),
                    network = 'correlation', 
                    network.options = networkOptions(cutoff = .6, 
                                                     min.degree = .2))
```

# 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(gene.name   = names(.),
             coefficient = .,
             stringsAsFactors = FALSE)
  } %>%
  arrange(gene.name) %>%
  knitr::kable()
```

## Hallmarks of Cancer

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

# Session Info

```{r sessionInfo}
sessionInfo()
```