A Time Series Analysis of Microarray Data

As the capture and analysis of single-time-point microarray expression data becomes routine, investigators are turning to time-series expression data to investigate complex gene regulation schemes and metabolic pathways. These investigations are facilitated by algorithms that can extract and cluster related behaviors from the full population of time-series behaviors observed. Although traditional clustering techniques have shown to be effective for certain types of expression analysis, they do not take the biological nature of the process into account, and therefore are clearly not optimized for this purpose. Moreover, the current approaches provide internal comparisons for the experiments utilized for clustering, but cross-comparisons between clustered results are qualitative and subjective. We present a combination of current and novel methods for the analysis of time series gene expression data. We focus on an actual study we have performed for Haemophilus influenzae which is a maj...

• Type: Conference
• Year: 2004
• Where: BIBE
• Authors: Selnur Erdal, Ozgur Ozturk, David L. Armbruster, Hakan Ferhatosmanoglu, William C. Ray
• Keywords: BIBE 2004, Bioinformatics, Expression Data, Gene Expression Data, Time-series Expression Data