Summary
Objectives:
When estimating the expression of genes based on the scanned images from microarrays
various algorithms are applied in a so-called low-level analysis which can calculate
expression values with an arbitrary number of digits beyond the decimal point. However,
too many digits (decimal places) are usually not justified because they do not represent
the precision of the measured expression. Thus, there is pseudo-precision and, as
a result, there are no tied values.
Methods:
We suggest avoiding, or omitting, the pseudo-precision: ties can remain, or be created
by rounding the computed expression values. Then, average ranks can be used in order
to apply nonparametric tests when ties occur. We use two actual data sets and the
Wilcoxon rank sum test.
Results:
We demonstrate that rounding gives a more efficient test, i.e. the average p-value
is decreased and the number of p-values smaller than 0.05 is increased.
Conclusions:
The random noise of pseudo-precision can reduce the efficiency of statistical tests
applied to detect differentially expressed genes. This result is, obviously, relevant
in many other areas of our digitalized world.
Keywords
Accuracy - low-level analysis - microarray - nonparametric test - precision - ties
