A spectral dimension reduction technique that improves pattern detection in multivariate spatial data

The algorithm is implemented in the open source software R and is available at https://github.com/IMSBCompBio/SpaCo.

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Publication Details

Journal
Bioinformatics
Published
2026-01-28
DOI
https://doi.org/10.1093/bioinformatics/btag052
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00
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article

A spectral dimension reduction technique that improves pattern detection in multivariate spatial data

Kiarash Rastegar, Till Baar, David Köhler, Chrysa Nikopoulou et al.
Bioinformatics
Single-cell and spatial transcriptomics
article

A spectral dimension reduction technique that improves pattern detection in multivariate spatial data

Kiarash Rastegar, Till Baar, David Köhler, Chrysa Nikopoulou, Vangelis Kondylis, Achim Tresch, Niklas Kleinenkuhnen, Peter Tessarz, Matthias Schmid, Vlada Milchevskaya
article en

Abstract

The algorithm is implemented in the open source software R and is available at https://github.com/IMSBCompBio/SpaCo.

BioinformaticsVol. 42(2)
University of Cologne (DE), University Hospital Bonn (DE), Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases (DE), University Hospital Cologne (DE), Max Planck Institute for Biology of Ageing (DE)
Openalex Percentile: Top 71%
Single-cell and spatial transcriptomics
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A spectral dimension reduction technique that improves pattern detection in multivariate spatial data — Kiarash Rastegar, Till Baar, et al. · Bioinformatics (2026) | TGRS Research Map | TGRS