Detecting differentially expressed genes of heterogeneous and positively skewed data using half Johnson’s modified t-test

Description of Logit-t: Detecting Differentially Expressed Genes Using Probe-Level Data

Detecting Differentially Expressed Genes Using Calibrated Bayes Factors

A common interest in microarray data analysis is to identify genes having changes in expression values between different biological conditions. The existing methods include using two-sample t-statistics, modified t-statistics (SAM), Bayesian t-statistics (Cyber-T), semiparametric hierarchical Bayesian models, and nonparametric permutation tests. All these methods essentially compare two populat...

Detecting Differentially Expressed Genes in Microarrays Using Bayesian Model Selection

DNA microarrays open up a broad new horizon for investigators interested in studying the genetic determinants of disease. The high throughputnature of these arrays, where differential expression for thousands of genes can bemeasured simultaneously, creates an enormous wealth of information, but also poses a challenge for data analysis because of the large multiple testing problem involved. The ...

An Empirical Bayesian Method for Detecting Differentially Expressed Genes Using EST Data

Detection of differentially expressed genes from expressed sequence tags (ESTs) data has received much attention. An empirical Bayesian method is introduced in which gene expression patterns are estimated and used to define detection statistics. Significantly differentially expressed genes can be declared given detection statistics. Simulation is done to evaluate the performance of proposed met...

Comparison of Statistical Data Models for Identifying Differentially Expressed Genes Using a Generalized Likelihood Ratio Test

Currently, statistical techniques for analysis of microarray-generated data sets have deficiencies due to limited understanding of errors inherent in the data. A generalized likelihood ratio (GLR) test based on an error model has been recently proposed to identify differentially expressed genes from microarray experiments. However, the use of different error structures under the GLR test has no...