Biclustering algorithms for biological data analysis: a survey
Abstract
A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results from the application of standard clustering methods to genes are limited. This limitation is imposed by the existence of a number of experimental conditions where the activity of genes is uncorrelated. A similar limitation exists when clustering of conditions is performed. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions of the data matrix has been proposed. The goal is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this paper, we refer to this class of algorithms as biclustering. Biclustering is also referred in the literature as coclustering and direct clustering, among others names, and has also been used in fields such as information retrieval and data mining. In this comprehensive survey, we analyze a large number of existing approaches to biclustering, and classify them in accordance with the type of biclusters they can find, the patterns of biclusters that are discovered, the methods used to perform the search, the approaches used to evaluate the solution, and the target applications.
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The TCC feature space can produce cell clustering similar to gene expression space while avoiding quantification and a statistical model for read generation.
“While this feature space is not as biologically interpretable as gene expression space, it can produce122 a similar cell clustering while sidestepping the time-consuming quantification task and avoiding the need to define a statistical model for read generation.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [122]
Transcript-compatibility counts provide a high-dimensional representation of cells with features other than gene expression.
“By counting the number of reads a cell has in each of the classes (transcript-compatibility counts, TCC), one obtains a high-dimensional representation of the cells with features other than explicitly quantified gene expression122.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [122]
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- Authors
- Madeira, S. C., & Oliveira, A. L.
- Affiliations
- Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento (PT)
- Instituto Superior Técnico (PT)
- Venue
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Published
- 2004-01
- Volume
- 1
- Issue
- 1
- Pages
- 24-45
- ISSN
- 1545-5963 (ISSN-L)
- OpenAlex
- W2144544802
- Cited by
- 2,049
- References
- 60
- Citations by year
- 2026: 21 · 2025: 29 · 2024: 49 · 2023: 47 · 2022: 53 · 2021: 79 · 2020: 50 · 2019: 87 · 2018: 106 · 2017: 107 · 2016: 101 · 2015: 133 · 2014: 129 · 2013: 111 · 2012: 145
- FWCI
- 26.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Computational Biology and Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- biclustering, gene expression data, microarray data, gene clustering, biological data analysis
- MeSH terms
- Algorithms, Humans, Saccharomyces cerevisiae, Models, Statistical, Gene Expression, Cluster Analysis, Computational Biology, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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