sepal: identifying transcript profiles with spatial patterns by diffusion-based modeling.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33704427", "kind": "science-record", "revision": 2, "content_hash": "sha256:cd98f67fb30d9ec1a763c250f5e10d5273f97d99123a76f3033be9fabd725af0", "created_at": "2026-11-01T15:41:28Z", "updated_at": "2026-11-01T18:54:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "sepal: identifying transcript profiles with spatial patterns by diffusion-based modeling.", "doi": "10.1093/bioinformatics/btab164", "pmid": "33704427", "authors": ["Alma Andersson", "Joakim Lundeberg"], "journal": "Bioinformatics (Oxford, England)", "year": 2021, "abstract": "MOTIVATION: Collection of spatial signals in large numbers has become a routine task in multiple omics-fields, but parsing of these rich datasets still pose certain challenges. In whole or near-full transcriptome spatial techniques, spurious expression profiles are intermixed with those exhibiting an organized structure. To distinguish profiles with spatial patterns from the background noise, a metric that enables quantification of spatial structure is desirable. Current methods designed for similar purposes tend to be built around a framework of statistical hypothesis testing, hence we were compelled to explore a fundamentally different strategy.\nRESULTS: We propose an unexplored approach to analyze spatial transcriptomics data, simulating diffusion of individual transcripts to extract genes with spatial patterns. The method performed as expected when presented with synthetic data. When applied to real data, it identified genes with distinct spatial profiles, involved in key biological processes or characteristic for certain cell types. Compared to existing methods, ours seemed to be less informed by the genes' expression levels and showed better time performance when run with multiple cores.\nAVAILABILITYAND IMPLEMENTATION: Open-source Python package with a command line interface (CLI), freely available at https://github.com/almaan/sepal under an MIT licence. A mirror of the GitHub repository can be found at Zenodo, doi: 10.5281/zenodo.4573237.\nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", "claims": [{"id": "claim-data-access", "statement": "All data used in this manuscript is publicly available, and we refer to the original publications/sources for further instructions on how to access the original data sets. We also provide processed data sets in the (to this manuscript) associated GitHub repository, https://github.com/almaan/sepal, where the synthetic data used for evalauation is included as well.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33704427", "locator": "sections[18] \"Data availability\" paragraph 2", "quote": "All data used in this manuscript is publicly available, and we refer to the original publications/sources for further instructions on how to access the original data sets. 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