Assembling Clinical Information for the Bonome Ovarian Data
===========================================================

by Keith A. Baggerly

## 1 Executive Summary

### 1.1 Introduction

We want to produce an RData file with the
clinical information for the
ovarian cancer samples
profiled by [Bonome et al.](#bonome08)
on U133A arrays.

### 1.2 Methods

We acquired clinical annotation from
the Gene Expression Omnibus (GEO) pages descending
from the main page GSE26712,
[http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE26712](http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE26712),
on Sep 12, 2012.
This includes the GEO GSM id for each sample, the sample
ID, Surgery Outcome (DOD, AWD, NED), and Survival in Years.

A csv file of this annotation is stored in RawData
as bonomeClinical.csv.

We load the clinical information into a data frame, and
construct an R "Surv"" object for overall survival.

### 1.3 Results

We save bonomeClinical and
bonomeOSYrs
to the RData file "bonomeClinical.RData".

## 2 Libraries

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

```{r libraries}

library(survival)

```

## 3 Loading the Data

Here we simply load the table of clinical information.


```{r loadBonomeClinical}

bonomeClinical <-
    read.table(file.path("RawData","Bonome","Clinical","bonomeClinical.csv"),
               header=TRUE, sep=",")
dim(bonomeClinical)
bonomeClinical[1:3,]

rownames(bonomeClinical) <-
    as.character(bonomeClinical[,"SampleID"])

```

## 4 Defining Overall Survival

Next, we define an R "Surv"" object for overall survival (OS)
We begin by looking at
the recorded values for patient status.

```{r examineStatus}

table(bonomeClinical[,"Status"])

```

Here, AWD = Alive with Disease, DOD = Dead of Disease,
and NED = Alive with no Evidence of Disease.

Next, we define an indicator vectors for OS.

```{r defineOS}

bonomeOSStatus <- rep(NA,nrow(bonomeClinical))
bonomeOSStatus[bonomeClinical[,"Status"]=="AWD"] <- "Censored"
bonomeOSStatus[bonomeClinical[,"Status"]=="DOD"] <- "Uncensored"
bonomeOSStatus[bonomeClinical[,"Status"]=="NED"] <- "Censored"
table(bonomeOSStatus)

```

Now we create the Surv object.

```{r createSurv}

bonomeOSYrs <- Surv(bonomeClinical[,"SurvivalYears"],
                    bonomeOSStatus=="Uncensored")
rownames(bonomeOSYrs) <- rownames(bonomeClinical)

```

## 5 Saving RData

Now we save the relevant information to an RData object.

```{r saveBonomeClinical}

save(bonomeClinical,
     bonomeOSYrs,
     file=file.path("RDataObjects","bonomeClinical.RData"))

```


## 6 Appendix

### 6.1 File Location

```{r getLocation}

getwd()

```

### 6.2 SessionInfo

```{r sessionInfo}

sessionInfo();

```

## 7 References

> <p id="bonome08">[1] 
  Bonome T, Levine DA, Shih J, Randonovich M, Pise-Masison CA,
  Bogomolniy F, Ozbun L, Brady J, Barrett JC, Boyd J, Birrer MJ.
  A gene signature predicting for survival in suboptimally debulked
  patients with ovarian cancer.
  <em>Cancer Res</em>, <b>68(13)</b>:5478-86, 2008.</p>