Study On Microarray Analysis

With the advance in recent 10 to 20 years, microarray has been widely developed in gene expression, functional genome, proteome and other research areas. Especially in recent years, it has been involved from the research fields to the clinical fields with the more and more important effect in clinical inspection, diagnosis, drug filtration etc. An issue of microarray experiment is to process large-scale of data with multi-channel, high through pass and automation.Microarray analysis is an important aspect of microarray technology. It has several processes including gddding, segmentation, data extraction and expression. Instead of a certain algorithm or method, it is a set of methods which cover the whole procedure of microarray experiment, but most of the current research works focus on certain analysis step. For the realization of high performance and automatic microarray analysis, the study of whole analysis procedure should be considered, not only solve the issues in each step, but also research on the relationship between them. With micorarray technology's wide implementation in clinical area, the breakthrough of microarray analysis becomes a desire.This thesis focuses on the methodology study of microarray analysis. A set of algorithms and methods which relate to each step of micorarray experiment were presented and validated by a great deal of experiments. The main contributions of this thesis are followings:An automatic gridding algorithm of biochip image based on projection is presented.For microarray image rotated correction, an automatic and power spectrum-based rotated correction algorithm is presented. For improving the performance, the optimization with multi-scale searching method is introduced.Based on the comparison of existed microarray image segmentation methods, the adaptive circle segmentation algorithm combined with the neighborhood searching algorithm is realized.The microarray data differential expression model is realized for clinical inspection. The key algorithms include: data filtration strategy based on coefficient variant, adjustable model for low level expression sample etc. This model is implemented for hepatitis C virus detection microarray. The analysis result showed a high consistence rate.Aiming at clinical implementation, a microarray analysis platform is researched and developed, including the development of laser confocal microarray scanner, microarray automatic analysis workstation, microarray database etc.A great deal of experiments were done to evaluate the performance of our algorithms and methods, including 80 samples of hepatitis C virus detection microarray, 256 samples of mutation detection microarray for hepatitis B virus the precore region, 63 samples of allergen detection microarray.Experiments and analysis show that the algorithms and methods presented in this thesis can be used for automatic microarray analysis. With an effective interaction between every analyzing step, gridding, segmentation and data expression can be processed in series. The result of clinical implementation also shows that the microarray analysis platform provides effective and feasible methods and technologies for microarray research, development and implementation.