A unified approach for simultaneous gene clustering and differential expression identification.
Although both clustering and identification of differentially expressed genes are equally essential in most microarray studies, the two tasks are often conducted without regard to each other. This is clearly not the most efficient way of extracting information. The main aim of this article is to develop a coherent statistical method that can simultaneously cluster and detect differentially expressed genes. Through information sharing between the two tasks, the proposed approach gives more sensible clustering among genes and is more sensitive in identifying differentially expressed genes. The improvement over existing methods is illustrated in both our simulation results and a case study.
Yuan
Ming
M
School of Industrial and Systems Engineering, Georgia Institute of Technology, 765 Ferst Drive NW, Atlanta, Georgia 30332, USA. myuan@isye.gatech.edu
Kendziorski
Christina
C
D000818:Animals; D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D020738:Leptin / Q000235:genetics; D051379:Mice; D015233:Models, Statistical; D005810:Multigene Family; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data
D020738:Leptin