Robust decomposition of cell type mixtures in spatial transcriptomics

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 m...

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Bibliographic Details
Published in:Nature biotechnology Vol. 40; no. 4; pp. 517 - 526
Main Authors: Cable, Dylan M., Murray, Evan, Zou, Luli S., Goeva, Aleksandrina, Macosko, Evan Z., Chen, Fei, Irizarry, Rafael A.
Format: Journal Article
Language:English
Published: New York Nature Publishing Group US 01.04.2022
Nature Publishing Group
Subjects:
631/114/2415
631/61
631/61/212
631/61/212/2019
Agriculture
Animals
Bioinformatics
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Biomedicine
Biotechnology
Computational neuroscience
Datasets
Decomposition
Exome Sequencing
Life Sciences
Localization
Mapping
Mice
Mixtures
Sequence Analysis, RNA
Single-Cell Analysis
Software
Tissues
Transcriptome - genetics
Transcriptomics
ISSN:1087-0156, 1546-1696, 1546-1696
Online Access:Get full text
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Description
Summary: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 . Cell type mapping in spatial transcriptomics is enabled by accounting for compositional mixtures and differences in sequencing technologies.
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Author Contributions
These authors contributed equally
D.M.C., F.C., R.A.I, and E.Z.M. conceived the study; F.C., E.M., and E.Z.M. designed the Slide-seq experiment; E.M. generated the Slide-seq data; D.M.C., R.A.I., and F.C. developed the statistical methods; D.M.C., F.C., R.A.I, and E.Z.M. designed the analysis; D.M.C., R.A.I., F.C, A.G., and L.S.Z. analyzed the data; D.M.C., F.C., R.A.I., E.Z.M., and L.S.Z. wrote the manuscript; all authors read and approved the final manuscript.
ISSN:1087-0156
1546-1696
1546-1696
DOI:10.1038/s41587-021-00830-w