Ranked Tiling

Van, Thanh Le; Leeuwen, Matthijs van; Nijssen, Siegfried; Fierro, Ana Carolina; Marchal, Kathleen; Raedt, Luc De

doi:10.1007/978-3-662-44851-9_7

Cited by 12 publications

(1 citation statement)

References 16 publications

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“…They include a mixed integer linear programming (MILP) and two constraint programming (CP) approaches. The first CP method combined with large neighborhood search is an improved version of the work proposed in [13]. Gene expression analysis results were already reported in this earlier work but the proposed approach looks for fully disjoint biclusters.…”

Section: Introductionmentioning

confidence: 99%

Identifying gene-specific subgroups: an alternative to biclustering

2019

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BackgroundTranscriptome analysis aims at gaining insight into cellular processes through discovering gene expression patterns across various experimental conditions. Biclustering is a standard approach to discover genes subsets with similar expression across subgroups of samples to be identified. The result is a set of biclusters, each forming a specific submatrix of rows (e.g. genes) and columns (e.g. samples). Relevant biclusters can, however, be missed when, due to the presence of a few outliers, they lack the assumed homogeneity of expression values among a few gene/sample combinations. The Max-Sum SubMatrix problem addresses this issue by looking at highly expressed subsets of genes and of samples, without enforcing such homogeneity.ResultsWe present here the K-CPGC algorithm to identify K relevant submatrices. Our main contribution is to show that this approach outperforms biclustering algorithms to identify several gene subsets representative of specific subgroups of samples. Experiments are conducted on 35 gene expression datasets from human tissues and yeast samples. We report comparative results with those obtained by several biclustering algorithms, including CCA, xMOTIFs, ISA, QUBIC, Plaid and Spectral. Gene enrichment analysis demonstrates the benefits of the proposed approach to identify more statistically significant gene subsets. The most significant Gene Ontology terms identified with K-CPGC are shown consistent with the controlled conditions of each dataset. This analysis supports the biological relevance of the identified gene subsets. An additional contribution is the statistical validation protocol proposed here to assess the relative performances of biclustering algorithms and of the proposed method. It relies on a Friedman test and the Hochberg’s sequential procedure to report critical differences of ranks among all algorithms.ConclusionsWe propose here the K-CPGC method, a computationally efficient algorithm to identify K max-sum submatrices in a large gene expression matrix. Comparisons show that it identifies more significantly enriched subsets of genes and specific subgroups of samples which are easily interpretable by biologists. Experiments also show its ability to identify more reliable GO terms. These results illustrate the benefits of the proposed approach in terms of interpretability and of biological enrichment quality. Open implementation of this algorithm is available as an R package.

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Section: Introductionmentioning

confidence: 99%