Assessing the size of gene or RNAi effects in multifactor high-throughput experiments.
OBJECTIVE
To expand the recently proposed contrast variable and associated concepts to assess the size of gene effects or siRNA effects in multifactor high-throughput experiments, as well as to serve the need to consider both mean and standardized mean of contrast (SMC).


RESULTS
The recently proposed concepts of contrast variable and SMC are expanded in the context of multifactor analysis of variance. Based on this expansion, SMC is explored as a tool for analyzing multifactor high-throughput data, a novel plot termed a dual-flashlight plot is proposed, and the incompatibility of false-discovery rates across experiments is demonstrated. The applications show that the results reached using expanded SMC and the dual-flashlight plot are more reasonable than those reached using p-value-based or false-discovery rate-based volcano plot for assessing differential expression, genetic dominance and linear/quadratic time-course changes.


CONCLUSIONS
Compared with traditional contrast analysis, the expanded contrast variable and SMC may serve as an alternative that can address the real need of assessing the size of gene or siRNA effects in multifactor high-throughput experiments.
Zhang
Xiaohua Douglas
XD
Biometrics Research, Merck Research Laboratories, West Point, PA 19486, USA. xiaohua_zhang@merck.com
D000465:Algorithms; D000704:Analysis of Variance; D003627:Data Interpretation, Statistical; D055808:Drug Discovery; D004353:Drug Evaluation, Preclinical; D005189:False Positive Reactions; D005260:Female; D005786:Gene Expression Regulation / Q000502:physiology; D005796:Genes / Q000235:genetics*; D006801:Humans; D008297:Male; D020411:Oligonucleotide Array Sequence Analysis; D011336:Probability; D034622:RNA Interference; D011960:Receptors, Estrogen / Q000235:genetics; D015203:Reproducibility of Results
D011960:Receptors, Estrogen