A run-based procedure to identify time-lagged gene clusters in microarray experiments

The analysis of gene-expression data obtained from microarray experiments can be useful to identify regulatory relationship between genes. Genes with a common functional role have similar expression patterns across different microarray experiments. These similar expression patterns are perhaps due to co-regulation of genes in the same functional group. Most of the existing methods available for the identification of the regulatory relationships are either made for comparing two genes at a time or methods are not computationally efficient in the identification of the regulatory relationships. The procedures adopted by these methods do not use complete information contained in the data set. In this paper, we propose a statistical procedure, which will use the information contained in the data set to cluster genes that show similar patterns. The proposed procedure compares several genes at a time instead of pair-wise comparisons as done in some of the other procedures. The proposed procedure provides gene clusters based on time-lagged data sets with more details. The proposed method provides a numerical value that would facilitate in comparing different sets of data obtained from different expressions. It also provides the identification of the gene involved and the time point at which the observation is made so that proper medicine can be developed for the gene-specific and time-specific disease.

We applied the proposed procedure on the Spellman data set (Mol. Biol. Cell 1998; 9(12): 3273-3297) and compared our procedure with some of the other existing procedures. We found that our procedure is more computationally efficient than Ji and Tan (Bioinformatics 2005; 21:509-516), event method and edge detection procedures. The proposed procedure also provides more details about the clusters than Ji and Tan (Bioinformatics 2005; 21:509-516), event method and edge detection procedures. The proposed procedure is really simple to apply as compared with other available procedures in the literature including Ji and Tan (Bioinformatics 2005; 21:509-516), event method and edge detection procedures.