Finding large average submatrices in high dimensional data
By Andrey A. Shabalin, V. Weigman, Charles M. Perou, Andrew B. Nobel. 2009
183 citations recorded by the source corpus.
The search for sample-variable associations is an important problem in the exploratory analysis of high dimensional data. Biclustering methods search for sample-variable associations in the form of distinguished submatrices of the data matrix. (The rows and columns of a submatrix need not be contiguous.) In this paper we propose and evaluate a statistically motivated biclustering procedure (LAS) that finds large average submatrices within a given real-valued data matrix. The procedure operates in an iterative-residual fashion, and is driven by a Bonferroni-based significance score that effectively trades off between submatrix size and average value. We examine the performance and potential utility of LAS, and compare it with a number of existing methods, through an extensive three-part validation study using two gene expression datasets. The validation study examines quantitative properties of biclusters, biological and clinical assessments using auxiliary information, and classification of disease subtypes using bicluster membership. In addition, we carry out a simulation study to assess the effectiveness and noise sensitivity of the LAS search procedure. These results suggest that LAS is an effective exploratory tool for the discovery of biologically relevant structures in high dimensional data. Software is available at this https URL
Subfields: Computer Vision and Pattern Recognition, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/ad27d13339dcad0e29524ed1fcee39f3e22d53fb
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In Molecular Biology
Citations in the corpus
- Detection of a sparse submatrix of a high-dimensional noisy matrix (2011, background): “[10], who were also interested in finding large average submatrices.”
- Parsing the interferon transcriptional network and its disease associations (2016, unknown): “To visually clarify regulator/target relationships we applied the LAS biclustering algorithm (Shabalin et al., 2015) and identified a solution with five regulatory modules (C1 through C5).”