Xiaohua Douglas Zhang1. 1. Biometrics Research, Merck Research Laboratories, West Point, PA 19486, USA. Xiaohua_zhang@merck.com
Abstract
AIM: To develop a new analytical method to address the issues of traditional contrast analysis for comparing gene effects in RNAi and expression-profiling research. METHODS & RESULTS: I propose a new method consisting of contrast variable, standardized mean of contrast (SMC) and c(+)-probability analysis for comparing gene effects in multiple conditions. Compared with traditional contrast analysis, this new method has the following major advantages: it directly addresses the primary question of interest, namely the assessment of the strength of comparison; SMC and c(+)-probability capture data variability and are robust to sample size. The simulation and application studies show that traditional contrast analysis produces misleading results and erroneous conclusions whereas the new method produces reasonable results and sensible conclusions. CONCLUSIONS: The new method may have a broad utility in comparing gene effects in multiple conditions including selecting hits in RNAi research and identifying differential expression in microarray experiments.
AIM: To develop a new analytical method to address the issues of traditional contrast analysis for comparing gene effects in RNAi and expression-profiling research. METHODS & RESULTS: I propose a new method consisting of contrast variable, standardized mean of contrast (SMC) and c(+)-probability analysis for comparing gene effects in multiple conditions. Compared with traditional contrast analysis, this new method has the following major advantages: it directly addresses the primary question of interest, namely the assessment of the strength of comparison; SMC and c(+)-probability capture data variability and are robust to sample size. The simulation and application studies show that traditional contrast analysis produces misleading results and erroneous conclusions whereas the new method produces reasonable results and sensible conclusions. CONCLUSIONS: The new method may have a broad utility in comparing gene effects in multiple conditions including selecting hits in RNAi research and identifying differential expression in microarray experiments.
Authors: Xiaohua Douglas Zhang; Francesca Santini; Raul Lacson; Shane D Marine; Qian Wu; Luca Benetti; Ruojing Yang; Alex McCampbell; Joel P Berger; Dawn M Toolan; Erica M Stec; Daniel J Holder; Keith A Soper; Joseph F Heyse; Marc Ferrer Journal: Bioinformatics Date: 2011-08-16 Impact factor: 6.937
Authors: Julian A Gingold; Ed S Coakley; Jie Su; Dung-Fang Lee; Zerlina Lau; Hongwei Zhou; Dan P Felsenfeld; Christoph Schaniel; Ihor R Lemischka Journal: BMC Bioinformatics Date: 2015-07-22 Impact factor: 3.169
Authors: Aditi Nadkarni; John A Burns; Alberto Gandolfi; Moinuddin A Chowdhury; Laura Cartularo; Christian Berens; Nicholas E Geacintov; David A Scicchitano Journal: J Biol Chem Date: 2015-11-11 Impact factor: 5.157
Authors: Yuli Wang; Matthew DiSalvo; Dulan B Gunasekara; Johanna Dutton; Angela Proctor; Michael S Lebhar; Ian A Williamson; Jennifer Speer; Riley L Howard; Nicole M Smiddy; Scott J Bultman; Christopher E Sims; Scott T Magness; Nancy L Allbritton Journal: Cell Mol Gastroenterol Hepatol Date: 2017-03-06