Residual Disease Paper
========================================================
Quantifying Public PCR Data
--------------------------------------------------------

by Shelley Herbrich






## 1 Executive Summary

### 1.1 Introduction

In this report, we present the script used to generate the final PCR quantification summaries for ADH1B and FABP4.

### 1.2 Data & Methods

We use the "rawPCRData.RData" file  which contains the well-specific source, plate, randomized sample identifier, target gene, baseline-corrected fluorescence measurements for 40 cycles, and baseline coordiantes. For each sample, we have 2 to 3 technical replicates for both ADH1B and FABP4 as well as the control, 18S. Some wells were manually filtered for poor quality.  

We chose to use a "window-of-linearity" method, as introduced by Ramakers et al (2003), to quantify the PCR results from the baseline-corrected fluorescence measurements obtained from the vendor software. This method fits a linear model to the cycle number versus log fluorescence within a sliding window of defined size within a fixed border. The optimal model is that with the maximum log-linear range. Based on this model, the intercept corresponds to the initial template fluorescence and the slope is an estimate of the PCR efficiency. This algorithm is implemented in **sliwin** (part of the [qpcr](http://www.dr-spiess.de/) package). 

We require that the model not be fit within the baseline by forcing the lower bound of the border to be greater than the "take-off point" of exponential growth. Because the 18S fluorescence takes off almost immediately, we allow for a smaller window size to fit the optimal linear model.  

### 1.3 Results

We generate the ADH1B and FABP4 PCR values that correspond to the "PCRResults.RData" object.

## 2 Options and Libraries

We load the libraries we will use in this report.


```r
library(qpcR)
```


## 3 Loading Data

Next, we load the deidentified raw PCR data.


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


## 4 Quantifying PCR Data

We quantify the PCR measurements for FABP4 and ADH1B using the method described above and display the results.


```r
sampleID <- unique(rawPCRData$Sample.Name)
PCRquantifications <- data.frame(Source = rep("", length(sampleID)), Plate = rep("", 
    length(sampleID)), Sample.Name = sampleID, FABP4 = rep(NA, length(sampleID)), 
    ADH1B = rep(NA, length(sampleID)), stringsAsFactors = FALSE)

for (i1 in 1:length(sampleID)) {
    tmp <- rawPCRData[which(rawPCRData$Sample.Name == sampleID[i1]), ]
    tempNo <- array(NA, length(tmp$Well))
    for (i2 in 1:length(tmp$Well)) {
        subDRn <- cbind(1:40, t(tmp[i2, grep("Cycle", colnames(tmp))]))
        m <- pcrfit(subDRn, cyc = 1, fluo = 2, model = l3)
        to <- tryCatch(takeoff(m)$top, error = function(e) NA)
        border <- c(0, 0)
        if (to < tmp[i2, "Baseline.End"] & !is.na(to)) 
            border <- c(tmp[i2, "Baseline.End"] - to, 0)
        if (tmp$Target.Name[i2] == "18S") {
            sw <- tryCatch(sliwin(m, border = border, wsize = 4:6, plot = FALSE), 
                error = function(e) NULL)
        } else {
            sw <- tryCatch(sliwin(m, border = border, plot = FALSE), error = function(e) NULL)
        }
        if (!is.null(sw)) 
            tempNo[i2] <- unlist(sw["init"])
    }
    targetMeans <- sapply(split(tempNo, tmp$Target.Name), mean, na.rm = TRUE)
    PCRquantifications[i1, "Source"] <- as.character(tmp$Source[1])
    PCRquantifications[i1, "Plate"] <- tmp$Plate[1]
    PCRquantifications[i1, "FABP4"] <- log2(targetMeans["FABP4"]) - log2(targetMeans["18S"])
    PCRquantifications[i1, "ADH1B"] <- log2(targetMeans["ADH1B"]) - log2(targetMeans["18S"])
}
PCRquantifications <- PCRquantifications[order(PCRquantifications$FABP4, decreasing = TRUE), 
    ]
```



```r
head(PCRquantifications)
```

```
##         Source    Plate Sample.Name  FABP4  ADH1B
## 25  Washington  Plate.4         W20 -13.64 -16.71
## 61       MDACC  Plate.9         M33 -13.71 -20.97
## 26  Washington  Plate.4         W46 -13.99 -14.05
## 95       MDACC Plate.13         M80 -14.81 -15.26
## 139      MDACC Plate.29         M71 -15.18 -15.74
## 72       MDACC Plate.10         M61 -16.16 -15.27
```



```r
plot(PCRquantifications$FABP4, PCRquantifications$ADH1B, xlab = "FABP4", ylab = "ADH1B")
points(PCRquantifications$FABP4[which(PCRquantifications$Source == "MDACC")], 
    PCRquantifications$ADH1B[which(PCRquantifications$Source == "MDACC")], pch = "*", 
    col = "red")
legend("topleft", c("MDACC", "Washington"), pch = c(8, 1), col = c("red", "black"), 
    bty = "n", cex = 0.8)
abline(v = -20.05)
abline(a = -39.5, b = -1, lty = 2)
```

![plot of chunk plotPCR](figure/plotPCR.png) 


## 5 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] qpcR_1.3-7.1      robustbase_0.9-10 rgl_0.93.963      minpack.lm_1.1-8 
## [5] MASS_7.3-29       knitr_1.5        
## 
## loaded via a namespace (and not attached):
## [1] evaluate_0.5.1 formatR_0.9    stringr_0.6.2  tools_3.0.2
```