---
title: "curatedMSIData: resources for microsatellite instability aspects of TCGA samples."
author: "Vincent J. Carey, stvjc at channing.harvard.edu"
date: "`r format(Sys.time(), '%B %d, %Y')`"
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{curatedMSIData overview}
  %\VignetteEncoding{UTF-8}
output:
  BiocStyle::html_document:
    highlight: pygments
    number_sections: yes
    theme: united
    toc: yes
---

# Introduction

Microsatellite instability (MSI) is a feature of tumor genomes that
has long been studied.  A basic source of interest in MSI is
the observation that tumors exhibiting MSI are often
less aggressive.  One theory is that these tumors produce
neoantigens that are more likely to stimulate immune
responses that reduce risk of metastasis.

This package provides data structures based on tables 
published with recent
studies of MSI in TCGA samples.

# [Cortes-Ciriano et al. 2017](http://dx.doi.org/10.1038/ncomms15180)

Three supplemental tables have been transformed to SummarizedExperiment
instances.  These are devoted to gene-centric enumeration of MSI
events in CDS (`molpo_CDS`), 3'UTR (`molpo_3utr`), 
and 5'UTR (`molpo_5utr`).

```{r lkd}
suppressPackageStartupMessages({
library(BiocOncoTK)
library(ggplot2)
})
molpo_CDS
```

With the following code we approximately reproduce Figure 2a of
the paper.  We sorted by 'locus-specific' MSI recurrence, then
grouped by gene symbol.  CASP5 occurs twice in Figure 2a; the
figure below sums all the events tabulated for CASP5.  How
to dissect the annotation of MSI enumeration for this gene
is not immediately clear.  The same situation arises with
gene MAK16 for 3'UTR.  

```{r make2a}
library(SummarizedExperiment)
sev = apply(assay(molpo_CDS),1,sum)
litass = assay(molpo_CDS[names(sort(sev,decreasing=TRUE)[1:50]),])
dd = data.frame(t(litass), tumor=molpo_CDS$tumor_type)
library(dplyr)
library(magrittr)
names(dd) = gsub("_.*", "", names(dd))
library(reshape2)
ddm = melt(dd)
names(ddm)[2] = "gene"
names(ddm)[3] = "msi_cds"
if (.Platform$OS.type != "windows") {
ggplot(ddm %>% filter(msi_cds>0), aes(x=gene,fill=tumor)) + geom_bar() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))
}
```

Figure 3a of the paper illustrates variation within and between tumor types
in total numbers of MSI events detected in whole genome sequences.
The gestalt of this figure can be obtained through the following
code:

```{r make3a}
lit = molpo_WGS[1,molpo_WGS$tumor_type %in% c("UCEC", "BRCA", "KIRP")]
myd = data.frame(totmsi=t(assay(lit[1,])), tumor=lit$tumor_type)
ss = split(myd, myd$tumor)
ss[[1]]$x = 1:nrow(ss[[1]])
ss[[2]]$x = 1:nrow(ss[[2]])
ss[[3]]$x = 1:nrow(ss[[3]])
ssd = do.call(rbind,ss)
if (.Platform$OS.type != "windows") {
ggplot(ssd, aes(y=Total_number_MSI_events+1,x=x)) + geom_point() +
   facet_grid(.~tumor, scales="free_x")+ scale_y_log10()
}
```

# [Ding et al. 2018](http://dx.doi.org/10.1038/ncomms15180)

The MSIsensor scores from the paper of Ding et al. are provided in
a simple data.frame.  The gestalt of their Figure 3C can
be obtained via:

```{r getms}
data(MSIsensor.10k)
data(patient_to_tumor_code)
names(MSIsensor.10k)[1] = "patient_barcode"
mm = merge(MSIsensor.10k, patient_to_tumor_code)
if (.Platform$OS.type != "windows") {
ggplot(mm, aes(y=MSIsensor.score+1, x=tumor_code)) + 
  geom_boxplot() + coord_flip() + scale_y_log10()
}
```

