Identification of temporal association rules from time-series microarray data set: temporal association rules

One of the most challenging problems in mining gene expression data is to identify how the expression of any particular gene affects the expression of other genes. To elucidate the relationships between genes, an association rule mining (ARM) method has been applied to microarray gene expression data. A conventional ARM method, however, has a limit on extracting temporal dependencies between genes, though the temporal information is indispensable to discover underlying regulation mechanisms in biological pathways. In this paper, therefore, we propose a novel method, referred to as temporal association rule mining (TARM), which can extract temporal dependencies among related genes. A temporal association rule has the form [gene A ↑, gene B↓] → (7 min)[gene C], which represents that high expression level of gene A and significant repression of gene B followed by significant expression of gene C after 7 minutes. The proposed TARM method is tested with Saccharomyces cerevisiae cell cycle time-series microarray gene expression data set. In the parameter fitting phase of TARM, the best parameter set [threshold = ±0.8, support cutoff = 3 transactions, confidence cutoff = 90%], which extracted the most number of correct associations in KEGG cell cycle pathway, has been chosen for rule mining phase. Furthermore, comparing the precision scores of TARM (0.38) and Bayesian network (0.16), TARM method showed better accuracy. With the best parameter set, numbers of temporal association rules with five transcriptional time delays (0, 7, 14, 21, 28 minutes) are extracted from gene expression data of 799 genes which are pre-identified cell cycle relevant genes, while comparably small number of rules are extracted from random shuffled gene expression data of 799 genes. From the extracted temporal association rules, associated genes which play same role of biological processes within short transcriptional time delay and some temporal dependencies between genes with specific biological processes are identified.