Robust decomposition of cell type mixtures in spatial transcriptomics
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Abstract
A limitation of spatial transcriptomics technologies is that individual measurements may contain contributions from multiple cells, hindering the discovery of cell-type-specific spatial patterns of localization and expression. Here, we develop robust cell type decomposition (RCTD), a computational method that leverages cell type profiles learned from single-cell RNA-seq to decompose cell type mixtures while correcting for differences across sequencing technologies. We demonstrate the ability of RCTD to detect mixtures and identify cell types on simulated datasets. Furthermore, RCTD accurately reproduces known cell type and subtype localization patterns in Slide-seq and Visium datasets of the mouse brain. Finally, we show how RCTD's recovery of cell type localization enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD enables the spatial components of cellular identity to be defined, uncovering new principles of cellular organization in biological tissue. RCTD is publicly available as an open-source R package at https://github.com/dmcable/RCTD .
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Deconvolution methods can infer the proportion of different cell types within each spatial spot when scRNA-seq data from the same tissue are available.
“When scRNA-seq data from the same tissue are available, computational methods such as SPOTlight, robust cell type decomposition (RCTD), Tangram and conditional autoregressive deconvolution (CARD)84–87 deconvolute the spatial transcriptomic data and infer the proportion of different cell types within each spatial spot.”
From Spatial mapping of cellular senescence: emerging challenges and opportunities (2023) · cites this paper as [85]
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- Authors
- Cable, D. M., Murray, E., Zou, L. S., Goeva, A., Macosko, E. Z., Chen, F., & Irizarry, R. A.
- Affiliations
- Broad Institute (US)
- Harvard University (US)
- Corresponding author
- Chen, Fei
- Irizarry, Rafael A.
- Venue
- Nature Biotechnology
- Published
- 2021-02-18
- Volume
- 40
- Issue
- 4
- Pages
- 517-526
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC8606190
- OpenAlex
- W3129476455
- Cited by
- 1,487
- References
- 35
- Citations by year
- 2026: 465 · 2025: 399 · 2024: 314 · 2023: 159 · 2022: 116 · 2021: 30 · 2020: 3 · 2019: 1
- FWCI
- 51.12
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Biotechnology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (acceptedVersion)
- Funders
- Hertz Foundation
- NSF Graduate Research Fellowship
- U.S. Department of Health & Human Services | NIH | NIH Office of the Director (1DP5OD024583)
- U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (R01HG010647, R01HG005220)
- Burroughs Wellcome Fund
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R35GM131802)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- spatial transcriptomics, Slide-seq, Visium, mouse brain, spatial gene expression, tissue organization
- MeSH terms
- Exome Sequencing, Animals, Software, Sequence Analysis, RNA, Mice, Single-Cell Analysis, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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