Meta-analysis Statistical significance for genome wide studies Structural equation modeling
I am working on the meta-analysis of microarray studies done on the pathogenic Salmonella species. Dallas Joder, my student did his honors thesis investigating different techniques of meta-analysis to identify biological pathways relevant to this bacterial resilience and virulence expression. Dallas' thesis "Meta-Analysis of Salmonella enterica Microarray Data", won the James Madison University Phi Beta Kappa Best Honors Thesis Award for 2011.
One of the well known measures of significance for genome wide studies is the q value based on the false discovery rate. The q value is estimated from a list of p values based on permutation test for equality of means. Permutation test does not control Type I error rate to its nominal value when variances are unequal. I am working with Dr. Pradeep Singh of University of Southeast Missouri on this problem. In our work, we estimate q values based on the traditional t test with unequal variance and Baumgartner nonparametric test p-values in the presence of filtering conditions. Monte Carlo method is used to show that the estimated q value from both these tests controls q-value threshold rate better than the q value estimated from permutation test based p values.
Changes in social and environmental factors along with genetic and physical factors contribute to the increased prevalence of Type II Diabetes and pre-diabetic pathology in this country. I am collaborating with Dr. Terrie Rife of JMU to use structural equation modeling approach to identify the functional relationship between glucose intolerance, diet, nitric oxide synthase, stress, environment, and development of diabetes.
Statistical significance for genome wide studies Structural equation modeling
Structural equation modeling