Assessing Gene Expression Measurements

摘要

Gene array studies enable assessment of expression patterns of thousands of genes over time and un-rnder multiple conditions. The analysis of these patterns requires detecting whether observed diu000berences inrnexpression levels are signifcant or not. To perform the analysis, we must frst normalize the data. Normal-rnization is the term used to describe the process of removing diu000berences of measurements caused by certainrnnon-reproducibility.rnWe analyzed our data by non-parametric and parametric methods and used diu000berent goodness of ftrncriteria in order to fnd the normalized data. One of the method is a parametric likelihood framework forrnmicroarray data analysis where we introduced a Bayesian component; it is supposed that for each gene therernis a time-dependent probability of diu000berent expression levels under diu000berent conditions. In this model, thernprobability of diu000berent expression levels for individual genes can be estimated.rnWe included missing data in the model, using appropriate techniques. In order to validate the paramet-rnric likelihood model and the parameters of the model in the actual data set we used diu000berent estimationrntechniques. Furthermore, we used bootstrapping to estimate the variances of the estimated parameters.rnBased on the parametric likelihood model, we developed clusters of gene expression levels using principalrncomponent analysis, k-mean clustering and variance clustering techniques.