The high-level similarity of some disparate gene expression measures.
Probe-level data from Affymetrix GeneChips can be summarized in many ways to produce probe-set level gene expression measures (GEMs). Disturbingly, the different approaches not only generate quite different measures but they could also yield very different analysis results. Here, we explore the question of how much the analysis results really do differ, first at the gene level, then at the biological process level. We demonstrate that, even though the gene level results may not necessarily match each other particularly well, as long as there is reasonably strong differentiation between the groups in the data, the various GEMs do in fact produce results that are similar to one another at the biological process level. Not only that the results are biologically relevant. As the extent of differentiation drops, the degree of concurrence weakens, although the biological relevance of findings at the biological process level may yet remain.
Raghavan
Nandini
N
Nonclinical Biostatistics, Johnson & Johnson Pharmaceutical Research & Development LLC, Raritan, NJ 08869, USA.
De Bondt
An M I M
AM
Talloen
Willem
W
Moechars
Dieder
D
Göhlmann
Hinrich W H
HW
Amaratunga
Dhammika
D
D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity