---
title: "Example for Classification Data -- Breast Invasive Carcinoma"
author: "Marta Lopes and André Veríssimo"
date: "`r Sys.Date()`"
output: 
  BiocStyle::html_document:
    number_sections: yes
    toc: true
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{Example for Classification -- Breast Invasive Carcinoma}
  %\VignetteEncoding{UTF-8}
params:
  seed: !r 29221
--- 

## 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,include=TRUE,results="hide",message=FALSE,warning=FALSE}
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.show, warning=FALSE, error=FALSE}
brca <- TCGAutils::TCGAsplitAssays(brca, c('01','11'))
xdata.raw <- t(cbind(assay(brca[[1]]), assay(brca[[2]])))

# Get matches between survival and assay data
class.v        <- TCGAbiospec(rownames(xdata.raw))$sample_definition %>% factor
names(class.v) <- rownames(xdata.raw)

# keep features with standard deviation > 0
xdata.raw <- xdata.raw %>% 
  { (apply(., 2, sd) != 0) } %>% 
  { xdata.raw[, .] } %>%
  scale

set.seed(params$seed)
small.subset <- c('CD5', 'CSF2RB', 'HSF1', 'IRGC', 'LRRC37A6P', 'NEUROG2', 
                  'NLRC4', 'PDE11A', 'PIK3CB', 'QARS', 'RPGRIP1L', 'SDC1', 
                  'TMEM31', 'YME1L1', 'ZBTB11', 
                  sample(colnames(xdata.raw), 100))

xdata <- xdata.raw[, small.subset[small.subset %in% colnames(xdata.raw)]]
ydata <- class.v
```

# Fit models

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

```{r fit.show}
fitted <- cv.glmHub(xdata, ydata, 
                    family  = 'binomial',
                    network = 'correlation', 
                    nlambda = 1000,
                    network.options = networkOptions(cutoff = .6, 
                                                     min.degree = .2))
```

# Results of Cross Validation

Shows the results of `1000` 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 }
```

## Accuracy

```{r accuracy, echo=FALSE}
resp <- predict(fitted, s = 'lambda.min', newx = xdata, type = 'class')
flog.info('Misclassified (%d)', sum(ydata != resp))
flog.info('  * False primary solid tumour: %d', 
          sum(resp != ydata & resp == 'Primary Solid Tumor'))
flog.info('  * False normal              : %d', 
          sum(resp != ydata & resp == 'Solid Tissue Normal'))
```

Histogram of predicted response

```{r predict, echo=FALSE, warning=FALSE}
response <- predict(fitted, s = 'lambda.min', newx = xdata, type = 'response')
qplot(response, bins = 100)
```

ROC curve

```{r roc, echo=FALSE}
roc_obj <- pROC::roc(ydata, as.vector(response))

data.frame(TPR = roc_obj$sensitivities, FPR = 1 - roc_obj$specificities) %>%
  ggplot() +geom_line(aes(FPR,TPR), color = 2, size = 1, alpha = 0.7)+
      labs(title= sprintf("ROC curve (AUC = %f)", pROC::auc(roc_obj)), 
           x = "False Positive Rate (1-Specificity)", 
           y = "True Positive Rate (Sensitivity)")
```

# Session Info

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

