ArticleGenome biology2022
Modeling zero inflation is not necessary for spatial transcriptomics.
Article in Genome biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- BatchSVG: identifying batch-biased genes in the application of spatially variable gene detection.Bioinformatics (Oxford, England) · 2026Article
- CPS: mapping physical coordinates to high-fidelity spatial transcriptomics via privileged multi-scale context distillation.Bioinformatics (Oxford, England) · 2026Article
- Differential expression analysis in single-cell and spatial RNA-seq without model assumptions.Cell reports methods · 2026Article
- Biased multi-view contrastive learning with attentive masking for spatial transcriptomic analysis.Briefings in bioinformatics · 2026Article
- UNLOCKING MULTI-SAMPLE DIFFERENTIAL EXPRESSION FOR SPATIAL TRANSCRIPTOMICS DATA WITH TESSERA.bioRxiv : the preprint server for biology · 2026Article
- Clustering single-cell multi-omics data via multi-subspace contrastive learning with structural smoothness.Briefings in bioinformatics · 2026Article
- A likelihood ratio test for detecting shifts in homeolog expression ratios in allopolyploids.The New phytologist · 2026Article
- Differential expression analysis in single cell and spatial RNASeq without model assumptions.bioRxiv : the preprint server for biology · 2025Article
- A perspective on developing foundation models for analyzing spatial transcriptomic data.Quantitative biology (Beijing, China) · 2025Article
- JOINT IDENTIFICATION OF SPATIALLY VARIABLE GENES VIA A NETWORK-ASSISTED BAYESIAN REGULARIZATION APPROACH.The annals of applied statistics · 2025Article
- A deep learning-based multiscale integration of spatial omics with tumor morphology.Nature communications · 2025Article
- Inference of cell-type composition and single-cell spatial maps from spatial transcriptomics data with SWOT.Communications biology · 2025Article
- Cell2Spatial is a computational framework that maps single cells to spatial transcriptomic spots to reconstruct tissue architecture.PLoS biology · 2025Article
- Mapping disease critical spatially variable gene programs by integrating spatial transcriptomics with human genetics.bioRxiv : the preprint server for biology · 2025Article
- Spatial omics technology potentially promotes the progress of tumor immunotherapy.British journal of cancer · 2025Review
- SIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data.Nature communications · 2025Article
- Batch correcting single-cell spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns.Genome biology · 2025Article
- Precise gene expression deconvolution in spatial transcriptomics with STged.Nucleic acids research · 2025Article
- Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics.Nature communications · 2025Article
- Deciphering normal and cancer stem cell niches by spatial transcriptomics: opportunities and challenges.Genes & development · 2025Review
Corrections and comments
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4 authors.
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Abstract
backgroundSpatial transcriptomics are a set of new technologies that profile gene expression on tissues with spatial localization information. With technological advances, recent spatial transcriptomics data are often in the form of sparse counts with an excessive amount of zero values.
resultsWe perform a comprehensive analysis on 20 spatial transcriptomics datasets collected from 11 distinct technologies to characterize the distributional properties of the expression count data and understand the statistical nature of the zero values. Across datasets, we show that a substantial fraction of genes displays overdispersion and/or zero inflation that cannot be accounted for by a Poisson model, with genes displaying overdispersion substantially overlapped with genes displaying zero inflation. In addition, we find that either the Poisson or the negative binomial model is sufficient for modeling the majority of genes across most spatial transcriptomics technologies. We further show major sources of overdispersion and zero inflation in spatial transcriptomics including gene expression heterogeneity across tissue locations and spatial distribution of cell types. In particular, when we focus on a relatively homogeneous set of tissue locations or control for cell type compositions, the number of detected overdispersed and/or zero-inflated genes is substantially reduced, and a simple Poisson model is often sufficient to fit the gene expression data there.
conclusionsOur study provides the first comprehensive evidence that excessive zeros in spatial transcriptomics are not due to zero inflation, supporting the use of count models without a zero inflation component for modeling spatial transcriptomics.
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