ArticleBioinformatics (Oxford, England)2023
FISHFactor: a probabilistic factor model for spatial transcriptomics data with subcellular resolution.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Scalable multi-group nonnegative spatial factorization for spatial genomics data with cell-type heterogeneity.bioRxiv : the preprint server for biology · 2026Article
- smoppix: unified nonparametric analysis of single-molecule spatial omics data using probabilistic indices.Genome biology · 2026Article
- A comprehensive review on RNA subcellular localization prediction.Briefings in functional genomics · 2026Review
- CellSP enables module discovery and visualization for subcellular spatial transcriptomics data.Communications biology · 2025Article
- Spatial omics enters the microscopic realm: opportunities and challenges.Trends in genetics : TIG · 2025Review
- Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings.Nature communications · 2025Article
- Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions.Bulletin of mathematical biology · 2024Article
- Bento: a toolkit for subcellular analysis of spatial transcriptomics data.Genome biology · 2024Article
- Redefining Mucosal Inflammation with Spatial Genomics.Journal of dental research · 2024Article
- Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases.Genome medicine · 2024Review
- Enablers and challenges of spatial omics, a melting pot of technologies.Molecular systems biology · 2023Review
- Article
- Spatial transcriptomics in development and disease.Molecular biomedicine · 2023Review
- Principles and challenges of modeling temporal and spatial omics data.Nature methods · 2023Review
- Article
- RNA trafficking and subcellular localization-a review of mechanisms, experimental and predictive methodologies.Briefings in bioinformatics · 2023Review
- Belayer: Modeling discrete and continuous spatial variation in gene expression from spatially resolved transcriptomics.Cell systems · 2022Article
- Spatial components of molecular tissue biology.Nature biotechnology · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationFactor analysis is a widely used tool for unsupervised dimensionality reduction of high-throughput datasets in molecular biology, with recently proposed extensions designed specifically for spatial transcriptomics data. However, these methods expect (count) matrices as data input and are therefore not directly applicable to single molecule resolution data, which are in the form of coordinate lists annotated with genes and provide insight into subcellular spatial expression patterns. To address this, we here propose FISHFactor, a probabilistic factor model that combines the benefits of spatial, non-negative factor analysis with a Poisson point process likelihood to explicitly model and account for the nature of single molecule resolution data. In addition, FISHFactor shares information across a potentially large number of cells in a common weight matrix, allowing consistent interpretation of factors across cells and yielding improved latent variable estimates.
resultsWe compare FISHFactor to existing methods that rely on aggregating information through spatial binning and cannot combine information from multiple cells and show that our method leads to more accurate results on simulated data. We show that our method is scalable and can be readily applied to large datasets. Finally, we demonstrate on a real dataset that FISHFactor is able to identify major subcellular expression patterns and spatial gene clusters in a data-driven manner. AVAILABILITY AND IMPLEMENTATION: The model implementation, data simulation and experiment scripts are available under https://www.github.com/bioFAM/FISHFactor.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.