Assembling an RMA Quantification Matrix for the CCLE Data
========================================================

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
cancer cell lines profiled as part of the
Cancer Cell Line Encylcopedia [(CCLE)](#ccle12)
on Affymetrix U133+2 arrays.

### 1.2 Methods

We acquired a tarball of the 917 gzipped CEL
files used from the Gene Expression Omnibus (GEO) page
for GSE36133,
[http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE36133](http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE36133),
on Sep 14, 2012.
(Warning - this file is over 4G, so it may not download
properly to a 32-bit machine.)

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 ccleExpression
to the RData file "ccleExpression.RData".

## 2 Libraries

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


```r

library(affy)
library(hgu133plus2cdf)
```


## 3 Loading Clinical Information

Next, we load our previously assembled clinical information.


```r

load(file.path("RDataObjects", "ccleClinical.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

pathToCCLEData <- file.path("RawData", "CCLE", "CEL_Files")
```



## 5 Quantifying The CEL Files

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


```r

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


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


```r

d1 <- date()
ccleExpression <- justRMA(filenames = celFilePaths, compress = TRUE)
ccleExpression <- exprs(ccleExpression)
d2 <- date()
```



```r

c(d1, d2)
```

```
## [1] "Thu Jun 13 07:53:42 2013" "Thu Jun 13 08:08:35 2013"
```

```r

dim(ccleExpression)
```

```
## [1] 54675   917
```

```r
ccleExpression[1:3, 1:3]
```

```
##           GSM886835.CEL.gz GSM886836.CEL.gz GSM886837.CEL.gz
## 1007_s_at            8.400            7.699           10.638
## 1053_at             10.062            9.331           10.577
## 117_at               4.257            3.966            3.905
```


The justRMA computation takes about 40 minutes on my MacBook Pro;
the sheer volume of the data makes this challenging.

## 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

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

```
##           GEO.ID sourceName            primarySite    histology
## 1321N1 GSM886835      ECACC central_nervous_system       glioma
## 143B   GSM886836       ATCC                   bone osteosarcoma
## 22Rv1  GSM886837       ATCC               prostate    carcinoma
##            subtype
## 1321N1 astrocytoma
## 143B              
## 22Rv1
```

```r
colnames(ccleExpression)[1:3]
```

```
## [1] "GSM886835.CEL.gz" "GSM886836.CEL.gz" "GSM886837.CEL.gz"
```

```r
colnames(ccleExpression) <- tempNames
ccleExpression[1:3, 1:3]
```

```
##           1321N1  143B  22Rv1
## 1007_s_at  8.400 7.699 10.638
## 1053_at   10.062 9.331 10.577
## 117_at     4.257 3.966  3.905
```


## 7 Saving RData

Now we save the relevant information to an RData object.


```r

save(ccleExpression, file = file.path("RDataObjects", "ccleExpression.RData"))
```



## 8 Appendix

### 8.1 File Location


```r

getwd()
```

```
## [1] "/workspace/kabagg/RDPaper/Webpage/ResidualDisease"
```


### 8.2 SessionInfo


```r

sessionInfo()
```

```
## R version 2.15.1 (2012-06-22)
## Platform: x86_64-unknown-linux-gnu (64-bit)
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=C                 LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
## [1] hgu133plus2cdf_2.10.0 AnnotationDbi_1.22.5  affy_1.34.0          
## [4] Biobase_2.16.0        BiocGenerics_0.6.0    markdown_0.5.3       
## [7] knitr_0.9            
## 
## loaded via a namespace (and not attached):
##  [1] affyio_1.24.0         BiocInstaller_1.4.9   DBI_0.2-6            
##  [4] digest_0.6.3          evaluate_0.4.3        formatR_0.7          
##  [7] IRanges_1.18.0        preprocessCore_1.18.0 RSQLite_0.11.3       
## [10] stats4_2.15.1         stringr_0.6.2         tools_2.15.1         
## [13] zlibbioc_1.2.0
```


## 9 References

> <p id="ccle12"> [1] Barretina J, Caponigro G, Stransky N, Venkatesan K et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. <em>Nature</em>, <b>483(7391)</b>:603-7, 2012. PMID: 22460905.</p>