ArticleNucleic acids research2025
Detecting signatures underlying the composition of biological data.
Article in Nucleic acids research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Gut microbial signatures in poultry under low-protein diets and lysine restriction: implications for precision nutrition.Veterinary and animal science · 2026Review
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6 authors.
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Abstract
Biological compositional data is inherently multidimensional and therefore difficult to visualize and interpret. To allow for the automatic decomposition of large compositional data and to capture gradients in co-occurring features, called signatures, we developed a new software package 'cvaNMF'. Our benchmarks on synthetic data show the effectiveness of cross-validation and our novel signature-similarity method to identify a suitable decomposition using non-negative matrix factorization (NMF). This software provides a complete set of tools to identify and visualize biologically informative signatures which we demonstrate in a wide range of microbial and cellular datasets: 'Enterosignatures' detected in gut metagenomes differentiated human hosts with diverse diseases; five 'terrasignatures' from rhizosphere metagenomes differentiated root- or soil-associated microbiomes, while being refined enough to infer geographic distances between plants. Large-scale data from >13 000 metagenomes representing 25 biomes were decomposed into environmental and host-associated microbiomes based on five newly discovered signatures. Finally, analysis of the cell composition of non-small cell lung cancer samples allowed separation of cancerous and inflamed tissues based on four cell-type signatures.
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