Assembling an RMA Quantification Matrix for the Tothill Ovarian Data
====================================================================

by Keith A. Baggerly

## 1 Executive Summary

### 1.1 Introduction

We want to produce an RData file with a matrix of RMA
expression values for the ovarian cancer samples
profiled by [Tothill et al](#tothill08) with
Affymetrix U133+2 arrays.

### 1.2 Methods

We acquired a tarball of the 285 gzipped CEL
files used from the Gene Expression Omnibus (GEO) page
for GSE9891,
[http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE9891](http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE9891),
on September 13, 2012.

We used justRMA to compute RMA fits, and used
our previously assembled clinical information
to map the GEO GSM ids to remap the column (sample)
names.

### 1.3 Results

We save tothillExpression
to the RData file "tothillExpression.RData".

## 2 Libraries

We first load the libraries we will use
in this report.

```{r libraries, message=FALSE}

library(affy)
library(hgu133plus2cdf)

```

## 3 Loading Clinical Information

Next, we load our previously assembled clinical information.

```{r loadTothillClinical}

load(file.path("RDataObjects","tothillClinical.RData"))

```

## 4 Specifying the Raw Data Location

Here, we specify the location of the data we acquired
from GEO on our local system. You will need to acquire
these files and adjust this path before running
this report yourself.

```{r pathToTothillData}

pathToTothillData <-
    file.path("RawData","Tothill","CEL_Files")

```


## 5 Quantifying The CEL Files

First, we specify the CEL file paths in a character
vector for passing to justRMA.

```{r celFilePaths}

celFileNames <- dir(pathToTothillData,pattern="^GSM")
celFilePaths <- file.path(pathToTothillData,celFileNames)

```

Now we use justRMA to summarize expression at the probeset level.

```{r fitRMA, message=FALSE}

d1 <- date()
tothillExpression <- justRMA(filenames=celFilePaths,compress=TRUE)
tothillExpression <- exprs(tothillExpression)
d2 <- date()
c(d1,d2)

dim(tothillExpression)
tothillExpression[1:3,1:3]

```

The justRMA computation takes about 4 minutes on my MacBook Pro.

## 6 Mapping CEL Names to Sample IDs

We now use the clinical information to replace the
GEO GSM ids with the sample ids in the column names.

```{r remapColumnNames}

tempClinRows <- match(substr(colnames(tothillExpression),1,9),
                      as.character(tothillClinical[,"GEO.ID"]))
tempNames <- rownames(tothillClinical)[tempClinRows]
tothillClinical[tempNames[1:3],]
colnames(tothillExpression)[1:3]
colnames(tothillExpression) <- tempNames
tothillExpression[1:3,1:3]

```

## 7 Saving RData

Now we save the relevant information to an RData object.

```{r saveTothillExpression}

save(tothillExpression,
     file=file.path("RDataObjects","tothillExpression.RData"))

```


## 8 Appendix

### 8.1 File Location

```{r getLocation}

getwd()

```

### 8.2 SessionInfo

```{r sessionInfo}

sessionInfo();

```

## 9 References

> <p id="tothill08">
  [1] Tothill RW, Tinker AV, George J, Brown R, Fox SB, Lade S,
  Johnson DS, Trivett MK, Etemadmoghadam D, Locandro B, Traficante N,
  Fereday S, Hung JA, Chiew YE, Haviv I;
  Australian Ovarian Cancer Study Group, Gertig D, DeFazio A, Bowtell DD.
  Novel molecular subtypes of serous and endometrioid ovarian cancer
  linked to clinical outcome.
  <em>Clin Cancer Res</em>, <b>14(16)<b>:5198-208, 2008. <p>
