Mining differential top-k co-expression patterns from time course comparative gene expression datasets

Abstract

Background: Frequent pattern mining analysis applied on microarray dataset appears to be a promising strategy for identifying relationships between gene expression levels. Unfortunately, too many itemsets (co-expressed genes) are identified by this analysis method since it does not consider the importance of each gene within biological processes to a cellular response and does not take into account temporal properties under biological treatment-control matched conditions in a microarray dataset.

Results: We propose a method termed TIIM (Top-k Impactful Itemsets Miner), which only requires specifying a user-defined number k to explore the top k itemsets with the most significantly differentially co-expressed genes between 2 conditions in a time course. To give genes different weights, a table with impact degrees for each gene was constructed based on the number of neighboring genes that are differently expressed in the dataset within gene regulatory networks. Finally, the resulting top-k impactful itemsets were manually evaluated using previous literature and analyzed by a Gene Ontology enrichment method.

Conclusions: In this study, the proposed method was evaluated in 2 publicly available time course microarray datasets with 2 different experimental conditions. Both datasets identified potential itemsets with co-expressed genes evaluated from the literature and showed higher accuracies compared to the 2 corresponding control methods: i) performing TIIM without considering the gene expression differentiation between 2 different experimental conditions and impact degrees, and ii) performing TIIM with a constant impact degree for each gene. Our proposed method found that several new gene regulations involved in these itemsets were useful for biologists and provided further insights into the mechanisms underpinning biological processes. The Java source code and other related materials used in this study are available at "http://websystem.csie.ncku.edu.tw/TIIM_Program.rar".

Figure 1

A flowchart of TIIM for discovering differential top-k impactful itemsets.

Figure 2

Example of transforming gene expression data into the transaction data format.

Figure 3

Example of integrating transformed gene item values over repeated samples.

Figure 4

Example of identifying differential gene items.

Figure 5

Generation of an impact degree table.

Figure 6

GO enrichment analysis of wild-type and F72A/R73A mutant Vpr protein for the human dataset. GO₁: GO:0006915 ~ apoptosis; GO₂: GO:0043066 ~ negative regulation of apoptosis; GO₃: GO:0042127 ~ regulation of cell proliferation; GO₄: GO:0008284 ~ positive regulation of cell proliferation; GO₅: GO:0007050 ~ cell cycle arrest; GO₆: GO:0007346 ~ regulation of mitotic cell cycle; GO₇: GO:0051726 ~ regulation of cell cycle.

Figure 7

GO enrichment analysis of wild-type and R80A mutant Vpr protein for the human dataset. GO₁: GO:0043066 ~ negative regulation of apoptosis; GO₂: GO:0006915 ~ apoptosis; GO₃: GO:0042127 ~ regulation of cell proliferation; GO₄: GO:0008285 ~ negative regulation of cell proliferation; GO₅: GO:0008284 ~ positive regulation of cell proliferation; GO₆: GO:0007050 ~ cell cycle arrest; GO₇: GO:0045786 ~ negative regulation of cell cycle; GO₈: GO:0007346 ~ regulation of mitotic cell cycle; GO₉: GO:0051726 ~ regulation of cell cycle.