ArticleNature methods2022
Cell type-specific inference of differential expression in spatial transcriptomics.
Article in Nature methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers.
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Who cites it
119 citing papers in PubMed, 158 citations in OpenAlex.
- Machine learning and computational approaches to model therapeutic response and resistance in diffuse midline glioma.Journal of neuro-oncology · 2026Review
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- STADiffuser: high-fidelity simulation and full-view 3D modeling of spatial transcriptomics.Nature communications · 2026Article
- SPACE-seq integrates spatial transcriptomics and lineage tracing in native tissues.Cell stem cell · 2026Article
- NicheTrans: spatial-aware cross-omics translation.Nature methods · 2026Article
- Article
- Investigating Spatial Dynamics in Spatial Omics Data with StarTrail.Journal of the American Statistical Association · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Scalable multi-group nonnegative spatial factorization for spatial genomics data with cell-type heterogeneity.bioRxiv : the preprint server for biology · 2026Article
- R4ST: a reference-guided graph-generative model for robust reconstruction of spatial transcriptomic profiles.Bioinformatics (Oxford, England) · 2026Article
- The applications of single-cell and spatial transcriptomics in neuroscience and brain disorders.Neuroscience and biobehavioral reviews · 2026Review
- Cerebellar microRNA-206 tunes Purkinje neuron firing dynamics to control sensorimotor gating.bioRxiv : the preprint server for biology · 2026Article
- Spatial co-expression and cell-cell communication inference from spatially resolved transcriptomics with CONCISE.bioRxiv : the preprint server for biology · 2026Article
- Decoding the causal drivers of spatial cellular topology.iScience · 2026Article
- Spatial and network principles behind neural generation of locomotion.Nature communications · 2026Article
- ST-LDAW: A Topic-Model and Damped Weighted Least-Squares Method for Integrative Deconvolution of Single-Cell and Spatial Transcriptomics.Interdisciplinary sciences, computational life sciences · 2026Article
- Pan-cancer analysis of spatial transcriptomics reveals heterogeneous tumor spatial microenvironment.Cell reports. Medicine · 2026Article
- Logistic regression for estimating functional effects with spatial transcriptomics.Nucleic acids research · 2026Article
- UNLOCKING MULTI-SAMPLE DIFFERENTIAL EXPRESSION FOR SPATIAL TRANSCRIPTOMICS DATA WITH TESSERA.bioRxiv : the preprint server for biology · 2026Article
- Alignment of spatial transcriptomics slices across diseases, platforms and conditions.Genome medicine · 2026Article
59 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
A central problem in spatial transcriptomics is detecting differentially expressed (DE) genes within cell types across tissue context. Challenges to learning DE include changing cell type composition across space and measurement pixels detecting transcripts from multiple cell types. Here, we introduce a statistical method, cell type-specific inference of differential expression (C-SIDE), that identifies cell type-specific DE in spatial transcriptomics, accounting for localization of other cell types. We model gene expression as an additive mixture across cell types of log-linear cell type-specific expression functions. C-SIDE's framework applies to many contexts: DE due to pathology, anatomical regions, cell-to-cell interactions and cellular microenvironment. Furthermore, C-SIDE enables statistical inference across multiple/replicates. Simulations and validation experiments on Slide-seq, MERFISH and Visium datasets demonstrate that C-SIDE accurately identifies DE with valid uncertainty quantification. Last, we apply C-SIDE to identify plaque-dependent immune activity in Alzheimer's disease and cellular interactions between tumor and immune cells. We distribute C-SIDE within the R package https://github.com/dmcable/spacexr .
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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.