Microarray diagnostics
Contents
Summary statistics
Raw data should be on the 216 scale, with data ranges of (0, 65,535). Statistics of interest include, min, max,range, summary,# NA's, # saturated for each slide, or for each block with slides. For each experiment, the number of Empty, spots or positive/negative controls may be of interest from the annotation information.
Statistical measures
Diagnostic measues such as five number summary, Wikipedia:Quartiles, including measures such as Wikipedia:Skewness, and Wikipedia:Kurtosis.
Examples using apply
# Ranges apply(RG$R, 2, range, na.rm=TRUE) apply(RG$G, 2, range, na.rm=TRUE) # Maximums apply(RG$R, 2, max, na.rm=TRUE) apply(RG$Rb, 2, max, na.rm=TRUE) apply(RG$G, 2, max, na.rm=TRUE) apply(RG$Gb, 2, max, na.rm=TRUE) # Examining backgrounds that are higher than foreground apply(RG$R < RG$Rb, 2, sum, na.rm=TRUE) apply(RG$G < RG$Gb, 2, sum, na.rm=TRUE)
There are several times where the data ranges, or the number of introduced missing values (NA's) can be investigated during background correction and normalization.
Background Correction
#Number of missing values apply( backgroundCorrect(RG, method="subtract"), 2, sum(is.na))
Comparing channels
Differences between the Red and Green channels can be examined by plotting the differences in summary statistics, for example the pseudocode below plots the counts for the Green channel versus the Red channel where the background is higher than the forground.
plot(apply(RG$R < RG$Rb, 2, sum, na.rm=TRUE), apply(RG$G < RG$Gb, 2, sum, na.rm=TRUE), type="n") text(apply(RG$R < RG$Rb, 2, sum, na.rm=TRUE), apply(RG$G < RG$Gb, 2, sum, na.rm=TRUE), seq(colnames(RG)))
