Residual Disease Paper
========================================================
Comparing RD Results
-------------------------------------------------------

by Shelley Herbrich





## 1 Executive Summary

### 1.1 Introduction

Using the true residual disease (RD) status for the validation cohort, we are interested to check our predictions using FABP4 and ADH1B. 

### 1.2 Data and Methods

We work with the results dataset, *PCRResults*.

For both target genes, we define our subset of patients with enriched proportion of residual disease as those with the top 25% of expression (this corresponds to the top 35 samples).

### 1.3 Results

We plot the sorted log2 FABP4 and ADH1B values based on our quantification method. We also plot ADH1B against FABP4.  


## 2 Loading Libraries and Quantification Data

We load the PCR results, containing our quantification summaries and true RD status.


```r
library(qpcR)
library(gdata)
```



```r
load(file.path("RDataObjects", "PCRResults.RData"))
load(file.path("RDataObjects", "rawPCRData.RData"))
```



```r
sampleID <- PCRResults$Sample.Name
rd <- PCRResults$RDStatus
names(rd) <- sampleID
```



## 3 Flagging RD Using FABP4
 
First, we graphically examine our cutoff of the top 25th percentile based on levels of FABP4. 


```r
plot(rev(PCRResults$FABP4), ylab = "Initial Amount (log2)", xlab = "", pch = 21, 
    bg = c("grey", "red")[rev(factor(rd))], main = "Sorted FABP4 Concentrations")
abline(h = -20.05, lty = 2)
mtext("25%", side = 4, at = -16.5, las = 2, line = 0.5, cex = 0.8)
legend("topleft", c("Yes", "No"), pch = 19, col = c("red", "grey"), bty = "n", 
    title = "RD Status")
```

![plot of chunk plotSortedFABP4](figure/RDValidation-plotSortedFABP4.png) 


We do see a subgroup with an enriched proportion of residual disease that is associated with high FABP4. In our cohort where the overall percentage of patients with residual disease is 60%, we are able to identify a subgroup with 86% residual disease. 


```r
table(rd[1:35])/sum(table(rd[1:35]))
```

```
## 
##     No    Yes 
## 0.1429 0.8571
```

```r
table(rd[36:139])/sum(table(rd[36:139]))
```

```
## 
##     No    Yes 
## 0.4808 0.5192
```

```r

fisher.test(matrix(c(30, 5, 54, 50), ncol = 2), alternative = "greater")
```

```
## 
## 	Fisher's Exact Test for Count Data
## 
## data:  matrix(c(30, 5, 54, 50), ncol = 2)
## p-value = 0.0002489
## alternative hypothesis: true odds ratio is greater than 1
## 95 percent confidence interval:
##  2.191   Inf
## sample estimates:
## odds ratio 
##      5.494
```


Based on a one-sided Fisher's Exact test, the difference in proportion of residual disease is significantly higher for those with elevated FABP4. 


## 4 Flagging RD Using ADH1B

Now, we look at the top 25th percentile based on ADH1B. 


```r
orderADH1B <- order(PCRResults$ADH1B)

plot(PCRResults$ADH1B[orderADH1B], ylab = "Initial Amount (log2)", xlab = "", 
    pch = 21, bg = c("grey", "red")[factor(rd[orderADH1B])], main = "Sorted ADH1B Concentrations")
abline(h = -19.15, lty = 2)
mtext("25%", side = 4, at = -16.5, las = 2, line = 0.5, cex = 0.8)
legend("topleft", c("Yes", "No"), pch = 19, col = c("red", "grey"), bty = "n", 
    title = "RD Status")
```

![plot of chunk plotSortedADH1B](figure/RDValidation-plotSortedADH1B.png) 


Using ADH1B alone, we are also able to define a subgroup with an enriched proportion (86%) of residual disease. 


```r
table(rd[rev(orderADH1B)[1:35]])/sum(table(rd[rev(orderADH1B)[1:35]]))
```

```
## 
##     No    Yes 
## 0.1429 0.8571
```

```r
table(rd[rev(orderADH1B)[36:139]])/sum(table(rd[rev(orderADH1B)[36:139]]))
```

```
## 
##     No    Yes 
## 0.4808 0.5192
```

```r

fisher.test(matrix(c(30, 5, 54, 50), ncol = 2), alternative = "greater")
```

```
## 
## 	Fisher's Exact Test for Count Data
## 
## data:  matrix(c(30, 5, 54, 50), ncol = 2)
## p-value = 0.0002489
## alternative hypothesis: true odds ratio is greater than 1
## 95 percent confidence interval:
##  2.191   Inf
## sample estimates:
## odds ratio 
##      5.494
```


Again, we see the difference in proportion of residual disease is significantly higher for those with elevated ADH1B. 


```r
byBoth <- intersect(PCRResults$Sample.Name[1:35], PCRResults$Sample.Name[rev(orderADH1B)[1:35]])
rd[byBoth]
```

```
##   W20   W46   M80   M71   M61   M22   W38   M64   M24   W34   M54    W4 
##  "No" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" 
##   W44   M52   W28   W55   M81   M32   W22   M76   M18   W16   M40 
## "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes"
```


Of the 35 samples flagged by either marker, 23 were flagged by both (22 RD, 1 no RD). 


```r
rawPCRData[which(rawPCRData$Sample.Name == "W20"), 1:5]
```

```
##         Source   Plate Well Sample.Name Target.Name
## 205 Washington Plate.4   C1         W20       ADH1B
## 208 Washington Plate.4   C4         W20       FABP4
## 211 Washington Plate.4   C7         W20         18S
## 212 Washington Plate.4   C8         W20         18S
## 213 Washington Plate.4   C9         W20         18S
```


Here, we note that for the single sample with RD two wells for both ADH1B and FABP4 were removed due to poor PCR quality leaving only a single replicate to quantify each target gene. 

## 5 Flagging RD Using Both ADH1B and FABP4


```r
plot(PCRResults$FABP4, PCRResults$ADH1B, ylab = "ADH1B", xlab = "FABP4", pch = 21, 
    bg = c("grey", "red")[factor(rd)], main = "")
abline(a = -39.5, b = -1)
```

![plot of chunk both](figure/RDValidation-both.png) 

```r
sum(-39.5 - PCRResults$FABP4 < PCRResults$ADH1B, na.rm = TRUE)
```

```
## [1] 35
```

```r
byBothSim <- PCRResults$Sample.Name[which(-39.5 - PCRResults$FABP4 < PCRResults$ADH1B)]
table(rd[byBothSim])
```

```
## 
##  No Yes 
##   4  31
```


By using both markers simultaneously, we improve our enriched subgroup to 89% residual disease. 

## Appendix


```r
getwd()
```

```
## [1] "/Users/slt/SLT WORKSPACE/EXEMPT/OVARIAN/Ovarian residual disease study 2012/RD manuscript/Web page for paper/Webpage"
```



```r
sessionInfo()
```

```
## R version 3.0.2 (2013-09-25)
## Platform: x86_64-apple-darwin10.8.0 (64-bit)
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] gdata_2.13.2      qpcR_1.3-7.1      robustbase_0.9-10 rgl_0.93.963     
## [5] minpack.lm_1.1-8  MASS_7.3-29       knitr_1.5        
## 
## loaded via a namespace (and not attached):
## [1] evaluate_0.5.1 formatR_0.9    gtools_3.1.0   stringr_0.6.2 
## [5] tools_3.0.2
```