Difference between revisions of "Microarray diagnostics"
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==Background Correction== | ==Background Correction== | ||
<pre> | <pre> | ||
| − | # Number of missing values | + | #Number of missing values |
apply( backgroundCorrect(RG, method="subtract"), 2, sum(is.na)) | apply( backgroundCorrect(RG, method="subtract"), 2, sum(is.na)) | ||
</pre> | </pre> | ||
Revision as of 01:35, 6 September 2006
Summary statistics
Raw data should be on the 216 scale, with data ranges of (0, 65,535).
Examples using apply over arrays in an RGList.
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))
