Robust decomposition of cell type mixtures in spatial transcriptomics

By Dylan M. Cable, Evan Murray, Luli S. Zou, A. Goeva, Evan Z. Macosko, Fei Chen, Rafael A. Irizarry. Nature Biotechnology, 2020

1,169 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2f06354848bbdb52231bddc252c7a79ddba36125

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Citations in the corpus

Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder (2021, methodology): “The second category utilizes the cell type signatures defined by single-cell RNA-seq to deconvolute the spots [8, 9].”
Computational principles and challenges in single-cell data integration (2021, background): “Thus, a natural approach to associate pixels with cell type identities is to use multitask or transfer learning to exploit the cell type information contained in (dissociated) reference atlases 99–103 .”
Spatial transcriptomics at subspot resolution with BayesSpace (2021, neutral): “13, 15 and 17).”
SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network (2021, methodology): “The specificity of spatial expression patterns revealed by SpaGCN-detected SVGs were further confirmed by Moran’s I and Geary’s C statistics 27 , two commonly used metrics for quantifying spatial autocorrelation of gene expression 28,29 .”
Giotto: a toolbox for integrative analysis and visualization of spatial expression data (2021, result): “We also compared it with RCTD [30], which is a newly developed method for deconvolution.”
Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues (2023, positive): “To acquire cell type information, spot cell type scores between 0 and 1 can be derived from reference scRNA-seq datasets using provided python wrapper functions for tools such as RCTD 75 or Seurat v3 label transfer 76 .”
Cell2location maps fine-grained cell types in spatial transcriptomics (2022, methodology): “4 and 5 and Supplementary Fig.”
Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST (2023, positive): “To analyze data from low-resolution capture techniques, computational methods such as RCTD 26 , stereoscope 27 , SPOTlight 28 , cell2location 29 ,CARD 30 ,NNLS(AutoGeneS) 31,32 ,andspatialDWLS 33 have been developed.”
Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics (2022, methodology): “We used the hippocampus scRNA-seq dataset by Drop-seq 23,59 for deconvoluting both datasets (Supplementary Table 2).”
Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution (2022, methodology): “RCTD had the highest AS score (0.94), followed by Stereoscope (0.92).”
Best practices for single-cell analysis across modalities (2023, methodology): “Methods for this purpose vary broadly with respect to their assumptions and their definition of SVGs, and there is no consensus on how to best identify SVGs. SPARK 233 and SpatialDE 234 , for example, leverage spatial correlation testing, BayesSpace 227 uses Markov random fields, spaGCN 229 uses…”