DISA tool: Discriminative and informative subspace assessment with categorical and numerical outcomes
Pattern discovery and subspace clustering play a central role in the biological domain, supporting for instance putative regulatory module discovery from omics data for both descriptive and predictive ends. In the presence of target variables (e.g. phenotypes), regulatory patterns should further sat...
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| Veröffentlicht in: | PloS one 2022-10, Vol.17 (10), p.e0276253-e0276253 |
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| description | Pattern discovery and subspace clustering play a central role in the biological domain, supporting for instance putative regulatory module discovery from omics data for both descriptive and predictive ends. In the presence of target variables (e.g. phenotypes), regulatory patterns should further satisfy delineate discriminative power properties, well-established in the presence of categorical outcomes, yet largely disregarded for numerical outcomes, such as risk profiles and quantitative phenotypes. DISA (Discriminative and Informative Subspace Assessment), a Python software package, is proposed to evaluate patterns in the presence of numerical outcomes using well-established measures together with a novel principle able to statistically assess the correlation gain of the subspace against the overall space. Results confirm the possibility to soundly extend discriminative criteria towards numerical outcomes without the drawbacks well-associated with discretization procedures. Results from four case studies confirm the validity and relevance of the proposed methods, further unveiling critical directions for research on biotechnology and biomedicine. Availability: DISA is freely available at |
| doi_str_mv | 10.1371/journal.pone.0276253 |
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In the presence of target variables (e.g. phenotypes), regulatory patterns should further satisfy delineate discriminative power properties, well-established in the presence of categorical outcomes, yet largely disregarded for numerical outcomes, such as risk profiles and quantitative phenotypes. DISA (Discriminative and Informative Subspace Assessment), a Python software package, is proposed to evaluate patterns in the presence of numerical outcomes using well-established measures together with a novel principle able to statistically assess the correlation gain of the subspace against the overall space. Results confirm the possibility to soundly extend discriminative criteria towards numerical outcomes without the drawbacks well-associated with discretization procedures. Results from four case studies confirm the validity and relevance of the proposed methods, further unveiling critical directions for research on biotechnology and biomedicine. 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Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Alexandre, Leonardo</au><au>Costa, Rafael S</au><au>Henriques, Rui</au><au>V E, Sathishkumar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>DISA tool: Discriminative and informative subspace assessment with categorical and numerical outcomes</atitle><jtitle>PloS one</jtitle><date>2022-10-19</date><risdate>2022</risdate><volume>17</volume><issue>10</issue><spage>e0276253</spage><epage>e0276253</epage><pages>e0276253-e0276253</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Pattern discovery and subspace clustering play a central role in the biological domain, supporting for instance putative regulatory module discovery from omics data for both descriptive and predictive ends. In the presence of target variables (e.g. phenotypes), regulatory patterns should further satisfy delineate discriminative power properties, well-established in the presence of categorical outcomes, yet largely disregarded for numerical outcomes, such as risk profiles and quantitative phenotypes. DISA (Discriminative and Informative Subspace Assessment), a Python software package, is proposed to evaluate patterns in the presence of numerical outcomes using well-established measures together with a novel principle able to statistically assess the correlation gain of the subspace against the overall space. Results confirm the possibility to soundly extend discriminative criteria towards numerical outcomes without the drawbacks well-associated with discretization procedures. Results from four case studies confirm the validity and relevance of the proposed methods, further unveiling critical directions for research on biotechnology and biomedicine. 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| subjects | Analysis Availability Biology and Life Sciences Biotechnology Breast cancer Categorical variables Clustering Computer and Information Sciences Engineering and Technology Expected values Gene expression Medical diagnosis Medicine and Health Sciences Methods Numerical analysis Phenotypes Physical Sciences Public software Research and Analysis Methods Risk assessment Statistical significance Subspaces Variables |
| title | DISA tool: Discriminative and informative subspace assessment with categorical and numerical outcomes |
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