Residual Disease Paper ======================================================== Comparing RD Results ------------------------------------------------------- by Shelley Herbrich ```{r options, echo=FALSE} opts_chunk$set(tidy=TRUE, message=FALSE, warning=FALSE,fig.path='figure/RDValidation-', cache.path='cache/RDValidation-') ``` ## 1 Executive Summary ### 1.1 Introduction Using the true residual disease (RD) status for the validation cohort, we are interested to check our predictions using FABP4 and ADH1B. ### 1.2 Data and Methods We work with the results dataset, *PCRResults*. For both target genes, we define our subset of patients with enriched proportion of residual disease as those with the top 25% of expression (this corresponds to the top 35 samples). ### 1.3 Results We plot the sorted log2 FABP4 and ADH1B values based on our quantification method. We also plot ADH1B against FABP4. ## 2 Loading Libraries and Quantification Data We load the PCR results, containing our quantification summaries and true RD status. ```{r libraries} library(qpcR) library(gdata) ``` ```{r loadResults} load(file.path("RDataObjects","PCRResults.RData")) load(file.path("RDataObjects","rawPCRData.RData")) ``` ```{r defineRD} sampleID <- PCRResults$Sample.Name rd <- PCRResults$RDStatus names(rd) <- sampleID ``` ## 3 Flagging RD Using FABP4 First, we graphically examine our cutoff of the top 25th percentile based on levels of FABP4. ```{r plotSortedFABP4} plot(rev(PCRResults$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") ``` 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 originalResults} table(rd[1:35])/sum(table(rd[1:35])) table(rd[36:139])/sum(table(rd[36:139])) fisher.test(matrix(c(30,5,54,50),ncol=2),alternative="greater") ``` 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 plotSortedADH1B} 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") ``` Using ADH1B alone, we are also able to define a subgroup with an enriched proportion (86%) of residual disease. ```{r originalResultsADH1B} table(rd[rev(orderADH1B)[1:35]])/sum(table(rd[rev(orderADH1B)[1:35]])) table(rd[rev(orderADH1B)[36:139]])/sum(table(rd[rev(orderADH1B)[36:139]])) fisher.test(matrix(c(30,5,54,50),ncol=2),alternative="greater") ``` Again, we see the difference in proportion of residual disease is significantly higher for those with elevated ADH1B. ```{r overlap} byBoth <- intersect(PCRResults$Sample.Name[1:35], PCRResults$Sample.Name[rev(orderADH1B)[1:35]]) rd[byBoth] ``` Of the 35 samples flagged by either marker, 23 were flagged by both (22 RD, 1 no RD). ```{r noRDSample} rawPCRData[which(rawPCRData$Sample.Name=="W20"),1:5] ``` Here, we note that for the single sample with RD two wells for both ADH1B and FABP4 were removed due to poor PCR quality leaving only a single replicate to quantify each target gene. ## 5 Flagging RD Using Both ADH1B and FABP4 ```{r both} plot(PCRResults$FABP4, PCRResults$ADH1B, ylab="ADH1B",xlab="FABP4",pch=21,bg=c("grey","red")[factor(rd)],main="") abline(a=-39.5,b=-1) sum(-39.5-PCRResults$FABP4