ArticleThe annals of applied statistics2023
CO-CLUSTERING OF SPATIALLY RESOLVED TRANSCRIPTOMIC DATA.
Article in The annals of applied statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
5 citing papers in PubMed, 9 citations in OpenAlex.
- BISON: bi-clustering of spatial omics data with feature selection.Bioinformatics (Oxford, England) · 2025Article
- Spatially Informed Nonnegative Matrix Trifactorization for Coclustering Mass Spectrometry Data.Biometrical journal. Biometrische Zeitschrift · 2025Article
- Dimension-agnostic and granularity-based spatially variable gene identification using BSP.Nature communications · 2023Article
- CO-CLUSTERING OF SPATIALLY RESOLVED TRANSCRIPTOMIC DATA.The annals of applied statistics · 2023Article
- Spatial components of molecular tissue biology.Nature biotechnology · 2022Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Spatial transcriptomics is a groundbreaking technology that allows the measurement of the activity of thousands of genes in a tissue sample and maps where the activity occurs. This technology has enabled the study of the spatial variation of the genes across the tissue. Comprehending gene functions and interactions in different areas of the tissue is of great scientific interest, as it might lead to a deeper understanding of several key biological mechanisms, such as cell-cell communication or tumor-microenvironment interaction. To do so, one can group cells of the same type and genes that exhibit similar expression patterns. However, adequate statistical tools that exploit the previously unavailable spatial information to more coherently group cells and genes are still lacking. In this work, we introduce SpaRTaCo, a new statistical model that clusters the spatial expression profiles of the genes according to a partition of the tissue. This is accomplished by performing a co-clustering, i.e., inferring the latent block structure of the data and inducing two types of clustering: of the genes, using their expression across the tissue, and of the image areas, using the gene expression in the
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Registered trials
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.