by Shelley Herbrich
Using the true residual disease (RD) status for the validation cohort, we are interested to check our predictions using FABP4 and ADH1B.
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).
We plot the sorted log2 FABP4 and ADH1B values based on our quantification method. We also plot ADH1B against FABP4.
We load the PCR results, containing our quantification summaries and true RD status.
library(qpcR)
library(gdata)
load(file.path("RDataObjects", "PCRResults.RData"))
load(file.path("RDataObjects", "rawPCRData.RData"))
sampleID <- PCRResults$Sample.Name
rd <- PCRResults$RDStatus
names(rd) <- sampleID
First, we graphically examine our cutoff of the top 25th percentile based on levels of FABP4.
plot(rev(PCRResults$FABP4), ylab = "Initial Amount (log2)", xlab = "", pch = 21,
bg = c("grey", "red")[rev(factor(rd))], main = "Sorted FABP4 Concentrations")
abline(h = -20.05, lty = 2)
mtext("25%", side = 4, at = -16.5, las = 2, line = 0.5, cex = 0.8)
legend("topleft", c("Yes", "No"), pch = 19, col = c("red", "grey"), bty = "n",
title = "RD Status")
We do see a subgroup with an enriched proportion of residual disease that is associated with high FABP4. In our cohort where the overall percentage of patients with residual disease is 60%, we are able to identify a subgroup with 86% residual disease.
table(rd[1:35])/sum(table(rd[1:35]))
##
## No Yes
## 0.1429 0.8571
table(rd[36:139])/sum(table(rd[36:139]))
##
## No Yes
## 0.4808 0.5192
fisher.test(matrix(c(30, 5, 54, 50), ncol = 2), alternative = "greater")
##
## Fisher's Exact Test for Count Data
##
## data: matrix(c(30, 5, 54, 50), ncol = 2)
## p-value = 0.0002489
## alternative hypothesis: true odds ratio is greater than 1
## 95 percent confidence interval:
## 2.191 Inf
## sample estimates:
## odds ratio
## 5.494
Based on a one-sided Fisher's Exact test, the difference in proportion of residual disease is significantly higher for those with elevated FABP4.
Now, we look at the top 25th percentile based on ADH1B.
orderADH1B <- order(PCRResults$ADH1B)
plot(PCRResults$ADH1B[orderADH1B], ylab = "Initial Amount (log2)", xlab = "",
pch = 21, bg = c("grey", "red")[factor(rd[orderADH1B])], main = "Sorted ADH1B Concentrations")
abline(h = -19.15, lty = 2)
mtext("25%", side = 4, at = -16.5, las = 2, line = 0.5, cex = 0.8)
legend("topleft", c("Yes", "No"), pch = 19, col = c("red", "grey"), bty = "n",
title = "RD Status")
Using ADH1B alone, we are also able to define a subgroup with an enriched proportion (86%) of residual disease.
table(rd[rev(orderADH1B)[1:35]])/sum(table(rd[rev(orderADH1B)[1:35]]))
##
## No Yes
## 0.1429 0.8571
table(rd[rev(orderADH1B)[36:139]])/sum(table(rd[rev(orderADH1B)[36:139]]))
##
## No Yes
## 0.4808 0.5192
fisher.test(matrix(c(30, 5, 54, 50), ncol = 2), alternative = "greater")
##
## Fisher's Exact Test for Count Data
##
## data: matrix(c(30, 5, 54, 50), ncol = 2)
## p-value = 0.0002489
## alternative hypothesis: true odds ratio is greater than 1
## 95 percent confidence interval:
## 2.191 Inf
## sample estimates:
## odds ratio
## 5.494
Again, we see the difference in proportion of residual disease is significantly higher for those with elevated ADH1B.
byBoth <- intersect(PCRResults$Sample.Name[1:35], PCRResults$Sample.Name[rev(orderADH1B)[1:35]])
rd[byBoth]
## W20 W46 M80 M71 M61 M22 W38 M64 M24 W34 M54 W4
## "No" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes"
## W44 M52 W28 W55 M81 M32 W22 M76 M18 W16 M40
## "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes"
Of the 35 samples flagged by either marker, 23 were flagged by both (22 RD, 1 no RD).
rawPCRData[which(rawPCRData$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.
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$FABP4 < PCRResults$ADH1B, na.rm = TRUE)
## [1] 35
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.
getwd()
## [1] "/Users/slt/SLT WORKSPACE/EXEMPT/OVARIAN/Ovarian residual disease study 2012/RD manuscript/Web page for paper/Webpage"
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