R/cvvPlot.R In ABarray: Microarray QA and statistical data analysis for Applied Biosystems Genome Survey Microrarray (AB1700) gene expression data.

```#- \$Id: cvvPlot.R,v 1.1.1.1 2006/06/06 22:06:37 sunya Exp \$

#- Plot the CV result as a function of mean signal intensity.

"cvvPlot" <-
function(data, name = "") {
sd <- apply(data, 1, function(x) {sd(x, na.rm = TRUE)})
mean <- rowMeans(data, na.rm = TRUE)
cvValue <- sd / abs(mean)

#- Let's create cv plot here
title = paste("CV plot ", name, sep = "")

y.max <- max(cvValue, na.rm = TRUE)

#- Calculate position for text information
txt <- c(paste("Mean  = ", format(mean(cvValue, na.rm = TRUE) * 100, digits = 2), "%", sep = ""),
paste("Median= ", format(median(cvValue, na.rm = TRUE) * 100, digits = 2), "%", sep=""),
paste("Max   = ", format(max(cvValue, na.rm = TRUE) * 100, digits = 4), "%", sep = ""))

#- Check to see if mean is in log scale
if(mean(mean, na.rm = TRUE) > 100) {
mean <- log2(mean)
}

plot(mean, cvValue, col = "blue", pch = ".", main = title,
xlab = "mean signal intensity (log2)", ylab = "CV")
abline(v = 10, col = "green")

#- Draw a line at 25%, 50%, and 75% quantile cv
cv.q <- quantile(cvValue, na.rm = TRUE)
abline(h = cv.q[3], col = "red")
abline(h = cv.q[4], col = "purple")

legend("topright", txt)
legend("right", lwd = 2, col = c("red", "purple"), legend = c("50%tile", "75%tile"))
}

############################################################
#- \$Log: cvvPlot.R,v \$
#- Revision 1.1.1.1  2006/06/06 22:06:37  sunya
#- ABarray project converted from ab1700 project
#-
#- Revision 1.3  2006/03/14 19:48:30  sunya
#- Changed icp (internal control probe) QC plots.
#- Added function for icp -> icpPlot
#- ANOVA analysis now performs probe filtering, but no FDR is calculated.
#- hclusterPlot now calculate correlation coefficient for probes, previously
#- it used Euclidian distance. The distance between arrays is still Euclidean.
#-
```

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ABarray documentation built on May 2, 2018, 3:42 a.m.