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Comment
. 2023 Feb;20(2):187-188.
doi: 10.1038/s41592-022-01725-7.

Parts-based decomposition of spatial genomics data finds distinct tissue regions

No authors listed
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Parts-based decomposition of spatial genomics data finds distinct tissue regions

No authors listed. Nat Methods. 2023 Feb.

Abstract

Dimension reduction is a cornerstone of exploratory data analysis; however, traditional methods fail to preserve the spatial context of spatial genomics data. In this work, we develop a nonnegative spatial factorization (NSF) model that allows interpretable, parts-based decomposition of spatial single-cell count data. NSF allows label-free annotation of regions of interest in spatial genomics data and identifies genes and cells that can be used to define those regions.

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Figures

Fig. 1
Fig. 1. The nonnegative spatial factorization hybrid (NSFH) model identifies distinct regions in Slide-seqV2 mouse hippocampus data.
Each panel shows a different latent spatial component as a surface in two-dimensional space; red and yellow capture higher values, blue captures values near zero. Regions identified include the choroid plexus (1), thalamus (2), dentate gyrus (8) and meninges (10). © 2023, Townes, F. W. & Engelhardt, B. E, CCBY 4.0.
See this image and copyright information in PMC

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References

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