A novel Mixture Model Method for identification of differentially expressed genes from DNA microarray data

Background: The main goal in analyzing microarray data is to determine the genes that are differentially expressed across two types of tissue samples or samples obtained under two experimental conditions. Mixture model method (MMM hereafter) is a nonparametric statistical method often used for microarray processing applications, but is known to over-fit the data if the number of replicates is small. In addition, the results of the MMM may not be repeatable when dealing with a small number of replicates. In this paper, we propose a new version of MMM to ensure the repeatability of the results in different runs, and reduce the sensitivity of the results on the parameters. Results: The proposed technique is applied to the two different data sets: Leukaemia data set and a data set that examines the effects of low phosphate diet on regular and Hyp mice. In each study, the proposed algorithm successfully selects genes closely related to the disease state that are verified by biological info...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Kayvan Najarian, Maryam Zaheri, Ali Ajdari Rad, Siamak Najarian, Javad Dargahi
• Keywords: BMCBI 2004, Leukaemia Data, Microarray Data, Microarray Processing Applications