---
title: "Using `RTCGA` package to estimate a frequency of MDM2 duplications based on CNV data"
author: "Przemyslaw Biecek"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Using RTCGA to estimate ratio of MDM2 duplications}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, echo=FALSE}
library(knitr)
opts_chunk$set(comment="", message=FALSE, warning = FALSE, tidy.opts=list(keep.blank.line=TRUE, width.cutoff=150),options(width=150), cache=TRUE, eval = FALSE)
```

# RTCGA.cnv package

You need RTCGA.cnv package to use CNV scores.

```{r}
source("https://bioconductor.org/biocLite.R")
biocLite("RTCGA.cnv")
```


# MDM2

To get scores for all cancers for selected gene or region one should use
the get.region.cnv.score() function.

For example, MDM2 is located on chromosome 12 positions 69240000-69200000.

```{r}
get.region.cnv.score <- function(chr="12", start=69240000, stop=69200000) {
  list_cnv <- data(package="RTCGA.cnv")
  datasets <- list_cnv$results[,"Item"]
  
  filtered <- lapply(datasets, function(dataname) {
    tmp <- get(dataname)
    tmp <- tmp[tmp$Chromosome == chr,]
    tmp <- tmp[pmin(tmp$Start, tmp$End) <= pmax(stop, start) & pmax(tmp$Start, tmp$End) >= pmin(stop, start),]
    data.frame(tmp, cohort=dataname)
  })
  
  do.call(rbind, filtered)
}
MDM2.scores <- get.region.cnv.score(chr="12", start=69240000, stop=69200000)

# only one per patient
MDM2.scores$Sample <- substr(MDM2.scores$Sample, 1, 12)
MDM2.scores <- MDM2.scores[!duplicated(MDM2.scores$Sample),]
```

Let's see where there are more than 3 copies of MDM2

```{r}
cutoff <- log(3)/log(2)-1
MDM2cuted <- cut(MDM2.scores$Segment_Mean, c(0, cutoff, Inf), labels = c("<= 3", "> 3"))
```

And now we can calculate number of cases with <= or >3 copies od MDM2 in each cancer type.

```{r}
table(MDM2.scores$cohort, MDM2cuted)
```
