title: A three component latent class model for robust semiparametric gene discovery.
abstract: We propose a robust model for discovering differentially expressed genes which directly incorporates biological significance, i.e., effect dimension. Using the so-called c-fold rule, we transform the expressions into a nominal observed random variable with three categories: below a fixed lower threshold, above a fixed upper threshold or within the two thresholds. Gene expression data is then transformed into a nominal variable with three levels possibly originated by three different distributions corresponding to under expressed, not differential, and over expressed genes. This leads to a statistical model for a 3-component mixture of trinomial distributions with suitable constraints on the parameter space. In order to obtain the MLE estimates, we show how to implement a constrained EM algorithm with a latent label for the corresponding component of each gene. Different strategies for a statistically significant gene discovery are discussed and compared. We illustrate the method on a little simulation study and a real dataset on multiple sclerosis.
authors.affiliation: Sapienza-Università di Roma, Rome, Italy. marco.alfo@uniroma1.it
authors.forename: Marco
authors.initials: M
authors.lastname: Alfo'
authors.forename: Alessio
authors.initials: A
authors.lastname: Farcomeni
authors.forename: Luca
authors.initials: L
authors.lastname: Tardella
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D057225:Data Mining / Q000706:statistics & numerical data; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D056726:Genetic Association Studies / Q000706:statistics & numerical data*; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D011336:Probability