Identification of Regulatory Modules in Time Series Gene Expression Data Using a Linear Time Biclustering Algorithm
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- TL;DR
- We introduce a linear-time biclustering algorithm designed to identify regulatory modules in time series gene expression data.
- Problem
- While most biclustering formulations are computationally NP-hard, analyzing time series expression data allows us to restrict the problem to identifying maximal biclusters with contiguous columns, making it tractable.
- Method
- The proposed CCC-Biclustering algorithm finds all maximal contiguous column coherent biclusters in time linear to the size of the expression matrix, relying on a discretized matrix and suffix tree-based string processing techniques. Additionally, the approach includes a statistical significance ranking method and a filter for redundant, highly overlapping biclusters.
- Results
- Experiments on synthetic and real data demonstrate the method's effectiveness and its relevance for discovering regulatory modules.
- Contributions
- Not specified in the abstract.
- Limitations
- Not specified in the abstract.
- Takeaways
- Analysis of Saccharomyces cerevisiae transcriptomic patterns during heat stress shows the methodology extracts relevant information compatible with documented biological knowledge, highlighting its utility for studying other environmental stresses and regulatory modules.
- Applications
- Not specified in the abstract.
- Topics
- Computational biology, bioinformatics, time series gene expression data, biclustering algorithms, regulatory modules
- For industry
- Not specified in the abstract.
- Why it matters
- Not specified in the abstract.
Abstract
Although most biclustering formulations are NP-hard, in time series expression data analysis, it is reasonable to restrict the problem to the identification of maximal biclusters with contiguous columns, which correspond to coherent expression patterns shared by a group of genes in consecutive time points. This restriction leads to a tractable problem. We propose an algorithm that finds and reports all maximal contiguous column coherent biclusters in time linear in the size of the expression matrix. The linear time complexity of CCC-Biclustering relies on the use of a discretized matrix and efficient string processing techniques based on suffix trees. We also propose a method for ranking biclusters based on their statistical significance and a methodology for filtering highly overlapping and, therefore, redundant biclusters. We report results in synthetic and real data showing the effectiveness of the approach and its relevance in the discovery of regulatory modules. Results obtained using the transcriptomic expression patterns occurring in Saccharomyces cerevisiae in response to heat stress show not only the ability of the proposed methodology to extract relevant information compatible with documented biological knowledge but also the utility of using this algorithm in the study of other environmental stresses and of regulatory modules in general.