Developing appropriate computational tools to distill biological insights from large-scale gene expression data has been an important part of systems biology. Considering that gene relationships may change or only exist in a subset of collected samples, biclustering that involves clustering both genes and samples has become increasingly important, especially when the samples are pooled from a wide range of experimental conditions.
In this paper, we introduce a new biclustering algorithm to find subsets of genomic expression features (EFs) (e.g., genes, isoforms, exon inclusion) that show strong “group interactions” under certain subsets of samples. Group interactions are defined by strong partial correlations, or equivalently, conditional dependencies between EFs after removing the influences of a set of other functionally related EFs. Our new biclustering method, named SCCA-BC, extends an existing method for group interaction inference, which is based on sparse canonical correlation analysis (SCCA) coupled with repeated random partitioning of the gene expression data set.
SCCA-BC gives sensible results on real data sets and outperforms most existing methods in simulations. Software is available at https://github.com/pimentel/scca-bc.
SCCA-BC seems to work in numerous conditions and the results seem promising for future extensions. SCCA-BC has the ability to find different types of bicluster patterns, and it is especially advantageous in identifying a bicluster whose elements share the same progressive and multivariate normal distribution with a dense covariance matrix.
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Author summary: In this paper, we introduce a novel biclustering algorithm to find subsets of genomic expression features (EFs) (e.g., genes, isoforms, exon inclusion) that show strong partial correlations (i.e., conditional dependencies between EFs after removing the influences of other EFs in the same set) under certain subsets of samples. We extend an existing method for inferring such conditional dependencies, which is based on sparse canonical correlation analysis (SCCA) coupled with repeated random partitioning and subsampling of the gene expression data set. We incorporate a binary vector such that it will assist the objective function on deciding exclusion or inclusion of a particular sample to the bicluster.We test our algorithm on both simulation and real datasets, and achieve promising results. In addition, our algorithm is shown to be relatively robust to initialization and small perturbation in hyper-parameters. The algorithm is available at https://github.com/ pimentel/scca-bc.
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Pimentel, H., Hu, Z. & Huang, H. Biclustering by sparse canonical correlation analysis. Quant Biol 6, 56–67 (2018). https://doi.org/10.1007/s40484-017-0127-0
- gene clusters