UC Riverside

Microarray data analysis involves low-level and high-level analysis.

The low-level analysis focuses on how to get accurate and precise

gene expression data. The analysis built on gene expression data is

the high-level analysis such as differential gene expression

analysis, SFP detection, eQTL analysis and so on. This thesis

focuses on applications in both low-level and high-level analysis.

In the low-level analysis, the proposed L-GCRMA method combines the

advantage of the GCRMA model and the Langmuir model to get a more

accurate and precise gene expression data, especially at high

concentration. The simulation study and spike-in data analysis

demonstrates the advantage of proposed L-GCRMA model. In the

high-level analysis, a well developed SEM algorithm is successfully

applied to eQTL analysis and trait-gene association analysis.