Empirical evaluation of data transformations and ranking statistics for microarray analysis.
There are many options in handling microarray data that can affect study conclusions, sometimes drastically. Working with a two-color platform, this study uses ten spike-in microarray experiments to evaluate the relative effectiveness of some of these options for the experimental goal of detecting differential expression. We consider two data transformations, background subtraction and intensity normalization, as well as six different statistics for detecting differentially expressed genes. Findings support the use of an intensity-based normalization procedure and also indicate that local background subtraction can be detrimental for effectively detecting differential expression. We also verify that robust statistics outperform t-statistics in identifying differentially expressed genes when there are few replicates. Finally, we find that choice of image analysis software can also substantially influence experimental conclusions.
Department of Biostatistics, University of Washington, F-600 Health Sciences Building 1705 NE Pacific Street, Box 357232, Seattle, WA 98195, USA.
Li-Xuan
LX
Qin
Kathleen F
KF
Kerr
D000818:Animals; D003627:Data Interpretation, Statistical; D036262:Empirical Research; D020869:Gene Expression Profiling / Q000379:methods*; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012984:Software