ArticleNucleic acids research2025
Harnessing the potential of spatial statistics for spatial omics data with pasta.
Article in Nucleic acids research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Analysis of multicellular anatomical structures from spatial omics data using sosta.PLoS computational biology · 2026Article
- Spatialproteomics: an interoperable toolbox for analyzing highly multiplexed fluorescence image data.Nature methods · 2026Article
- Estimating tumour immune infiltration: methodological convergence across histology and spatial technologies.Briefings in bioinformatics · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Spatial omics allow for the molecular characterization of cells in their spatial context. Notably, the two main technological streams, imaging-based and high-throughput sequencing-based, give rise to very different data modalities. The characteristics of the two data types are well known in spatial statistics as point patterns and lattice data. In this perspective, we show the versatility of spatial statistics to quantify biological phenomena from local gene expression to tissue organization. As an example, we describe how to use exploratory metrics to address scientific questions in breast cancer, including cellular co-localization and gene co-expression analysis. We discuss technical concepts like window sampling, homogeneity, and weight matrix construction and show their importance. We also provide pasta (https://robinsonlabuzh.github.io/pasta), an extensive analysis vignette for spatial statistics both using R and Python packages with further biology-driven applications.
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What OpenQuestion holds
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.