Combined static and dynamic analysis for determining the quality of time-series expression profiles

Expression profiling of time-series experiments is widely used to study biological systems. However, determining the quality of the resulting profiles remains a fundamental problem. Because of inadequate sampling rates, the effect of arrest-and-release methods and loss of synchronization, the measurements obtained from a series of time points may not accurately represent the underlying expression profiles. To solve this, we propose an approach that combines time-series and static (average) expression data analysis--for each gene, we determine whether its temporal expression profile can be reconciled with its static expression levels. We show that by combining synchronized and unsynchronized human cell cycle data, we can identify many cycling genes that are missed when using only time-series data. The algorithm also correctly distinguishes cycling genes from genes that specifically react to an environmental stimulus even if they share similar temporal expression profiles. Experimental validation of these results shows the utility of this analytical approach for determining the accuracy of gene expression patterns.     

Analysis of gene expression profile during 3T3-L1 preadipocyte differentiation

Cellular differentiation is a process in which a group of differentiation specific genes is programmatically induced. This gene expression program leads to changes in both cellular morphological and physiological phenotypes. Using an 18,376-member cDNA/EST microarray, we analyzed the difference in gene expression profiles between differentiated 3T3-L1 adipocyte and non-differentiated 3T3-L1 preadipocyte. From our study a large number of genes and ESTs were identified as differentially induced or suppressed. In this paper we describe the changes of gene expression profile during 3T3-L1 cell differentiation.