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• W2118333177 abstract "Effective visualization of biological data is often critical for subsequent analysis. The popular clustergram/dendrogram visualization rearranges rows and columns of a data matrix so as to highlight clusters of similar responses, but assumes each row or column belongs to only one cluster and cannot associate each row or column with multiple clusters. Such multi-way associations occur frequently, e.g., when a gene plays multiple biological roles. We describe the ’factorgram’ visualization, which rearranges the data into an expanded view, associating each row (or column) with multiple clusters of rows (or columns) and elucidating potentially new biological relationships. Factorgrams for mouse gene expression and yeast synthetic-lethal gene-interaction datasets detect a larger number of statistically-significant clusters than clustergrams, plus a larger number of clusters enriched for gene ontology annotations. Experimentally-verified associations previously identified by manual rearrangement of rows and columns not grouped together by clustergrams, are readily identified by the factorgram." @default.
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• W2118333177 date "2006-01-01" @default.
• W2118333177 modified "2023-09-26" @default.
• W2118333177 title "Factorgrams: A tool for visualizing multi-way associations in biological data" @default.
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