ArticleProceedings of the National Academy of Sciences of the United States of America2016
Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Uncovering smooth structures in single-cell data with neighbor embeddings guided by predictability-computability-stability.Patterns (New York, N.Y.) · 2026Article
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- Spatial Decomposition of Longitudinal RNFL Maps Reveals Distinct Modes of Glaucomatous Progression With Structure-Function and Genetic Signatures.Investigative ophthalmology & visual science · 2026Article
- Genoarchitecture and input-output organization of the mouse basal ganglia and thalamic parafascicular nucleus.Nature neuroscience · 2026Article
- Data-driven fine-grained region discovery in the mouse brain with transformers.Nature communications · 2025Article
- A simplified MyProstateScore2.0 for high-grade prostate cancer.Cancer biomarkers : section A of Disease markers · 2025Article
- Unsupervised pattern identification in spatial gene expression atlas reveals mouse brain regions beyond established ontology.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- The critical dynamics of hippocampal seizures.Nature communications · 2024Article
- Practical considerations for machine learning-enabled discoveries in spatial transcriptomics.GEN biotechnology · 2024Article
- Automatic design of gene regulatory mechanisms for spatial pattern formation.NPJ systems biology and applications · 2024Article
- MIGGRI: A multi-instance graph neural network model for inferring gene regulatory networks for Drosophila from spatial expression images.PLoS computational biology · 2023Article
- Identifying promoter sequence architectures via a chunking-based algorithm using non-negative matrix factorisation.PLoS computational biology · 2023Article
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- Common multi-day rhythms in smartphone behavior.NPJ digital medicine · 2023Article
- Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data.Methods in molecular biology (Clifton, N.J.) · 2023Review
- Chronic intracranial EEG recordings and interictal spike rate reveal multiscale temporal modulations in seizure states.Brain communications · 2023Article
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- Reproducibility in Matrix and Tensor Decompositions: Focus on Model Match, Interpretability, and Uniqueness.IEEE signal processing magazine · 2022Article
- RSNET: inferring gene regulatory networks by a redundancy silencing and network enhancement technique.BMC bioinformatics · 2022Article
- Multiple mechanisms shape the relationship between pathway and duration of focal seizures.Brain communications · 2022Article
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6 authors.
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Abstract
Spatial gene expression patterns enable the detection of local covariability and are extremely useful for identifying local gene interactions during normal development. The abundance of spatial expression data in recent years has led to the modeling and analysis of regulatory networks. The inherent complexity of such data makes it a challenge to extract biological information. We developed staNMF, a method that combines a scalable implementation of nonnegative matrix factorization (NMF) with a new stability-driven model selection criterion. When applied to a set ofDrosophilaearly embryonic spatial gene expression images, one of the largest datasets of its kind, staNMF identified 21 principal patterns (PP). Providing a compact yet biologically interpretable representation ofDrosophilaexpression patterns, PP are comparable to a fate map generated experimentally by laser ablation and show exceptional promise as a data-driven alternative to manual annotations. Our analysis mapped genes to cell-fate programs and assigned putative biological roles to uncharacterized genes. Finally, we used the PP to generate local transcription factor regulatory networks. Spatially local correlation networks were constructed for six PP that span along the embryonic anterior-posterior axis. Using a two-tail 5% cutoff on correlation, we reproduced 10 of the 11 links in the well-studied gap gene network. The performance of PP with theDrosophiladata suggests that staNMF provides informative decompositions and constitutes a useful computational lens through which to extract biological insight from complex and often noisy gene expression data.
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