---
title: "multiAssayQC"
author: "Tim Triche, Jr."
date: "July 17th, 2015"
vignette: >
  %\VignetteIndexEntry{multiAssayQC}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
output: BiocStyle::html_document
---

# Overview

This vignette simply loads a few enormous multi-assay datasets for inspection.  
Hopefully, one of the outcomes of this year's conference will be more elegant 
approaches to doing this, so that similar datasets in the future will be lazily
evaluated for data relevant to a given query.

# AML, combined in a different fashion (from Level 1 arrays & Level 3 mutations)

```{r, eval=FALSE}
library(ozymandias)
data(LAML_multi)
plotX(LAML_multi) ## DNAmethylation-major, as it were

## Identify TP53-deleted patients who were not initially flagged as TP53-mutant

rm(LAML_multi)
gc(,T) ## free up some RAM
```

# AOCS (Australian chemoresistant ovarian cancer datasets, from ICGC)

```{r, message=FALSE}
data(AOCS_450k) ## DNA methylation with SNPs and copy number
data(AOCS_miRNA_RNAseq) ## as the title suggests, various sizes of RNA

## match samples & runs via some trickery...

rm(AOCS_450k)
rm(AOCS_miRNA_RNAseq)
gc(,T) ## free up some RAM
```

# GBM (for pathway plotting of contrasts using mutations)

This was retrieved using RTCGAToolbox, which works great for the analysis.

```{r}
data(gbm) ## DNA methylation with SNPs and copy number

## Somewhat unprincipled pathway analysis within Artemis

rm(gbm)
gc(,T) ## free up the RAM
```

There will likely be more changes to this document prior to the workshop.