Analysis Of Gene Expression Patterns Based On Microarrays

Microarrays which contain oligonucleotide or cDNA probes are used to measure the expression levels of thousands of genes in a single hybridization experiment. Gene Expression Pattern Scanner (GEPS) is a web-based server to provide interactive pattern analysis of user-submitted microarray data for facilitating their further interpretation. Putative gene expression patterns such as correlated expression, similar expression and specific expression are determined globally and systematically using geometric comparison and correlation analysis methods. These patterns can be visualized via linear plots with quantitative measures. User-defined threshold value is allowed to customize the format of the pattern search results. For better understanding of gene expression, patterns derived from 329 205 non-redundant gene expression records from the GNF SymAltas and the Gene Expression Omnibus are also provided. GEPS is available at http://bioinf.xmu.edu.cn/software/geps/geps.php. Based on GEPS, we constructed Tissue-Specific Expression Database (TSED). Currently, TSED collects 4 public microarray datasets, covers 113 tissues and 3455 tissue-specific genes from human and mouse. TSED allow users to query by genes or tissues. TSED can be accessed at http://bioinf. xmu. edu. cn/databases/TSED/search. php. Another important task of microarray experiments is to identify genes that are differentially expressed or so-called biomarkers. Currently, such genes are usually detected by some popular statistical approaches: fold method, t test, F test, SAM, regularized t test, etc. But these methods are either accompanied by relatively high false positive rates or are bias indicators of the degree of differential expression. We present modified F test (MF) and modified t test (Mt) that the standard F value and t value are multiplied by their coefficients of variation respectively. Using two real microarray datasets for testing, through scattering plots and permutation methods for assessing the false positive rates and false discovery rates our proposed methods show better performance to some extent. Classification of the derived differentially expressed genes by their functions shows much biological relevance and significance.