A note on oligonucleotide expression values not being normally distributed.
Novel techniques for analyzing microarray data are constantly being developed. Though many of the methods contribute to biological discoveries, inability to properly evaluate the novel techniques limits their ability to advance science. Because the underlying distribution of microarray data is unknown, novel methods are typically tested against the assumed normal distribution. However, microarray data are not, in fact, normally distributed, and assuming so can have misleading consequences. Using an Affymetrix technical replicate spike-in data set, we show that oligonucleotide expression values are not normally distributed for any of the standard methods for calculating expression values. The resulting data tend to have a large proportion of skew and heavy tailed genes. Additionally, we show that standard methods can give unexpected and misleading results when the data are not well approximated by the normal distribution. Robust methods are therefore recommended when analyzing microarray data. Additionally, new techniques should be evaluated with skewed and/or heavy-tailed data distributions.
Hardin
Johanna
J
Department of Mathematics, Pomona College, 610 North College Avenue, Claremont, CA 91711, USA. jo.hardin@pomona.edu
Wilson
Jason
J
D001699:Biometry; D003627:Data Interpretation, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*