Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data

Agnieszka Geras

doi:10.1186/s13059-023-02951-8

2023-05-17

Agnieszka Geras

Shadi Darvish Shafighi

DOI：10.1186/s13059-023-02951-8

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摘要

En 中文

Spatial transcriptomics maps gene expression across tissues, posing the challenge of determining the spatial arrangement of different cell types. However, spatial transcriptomics spots contain multiple cells. Therefore, the observed signal comes from mixtures of cells of different types. Here, we propose an innovative probabilistic model, Celloscope, that utilizes established prior knowledge on marker genes for cell type deconvolution from spatial transcriptomics data. Celloscope outperforms other methods on simulated data, successfully indicates known brain structures and spatially distinguishes between inhibitory and excitatory neuron types based in mouse brain tissue, and dissects large heterogeneity of immune infiltrate composition in prostate gland tissue.

Keyword:

Probabilistic model

MCMC sampling

Spatial transcriptomics data

Cell types

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Genome Biology

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9.4

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6.4K

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Polish Academy of Sciences

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3.0W

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centre national de la recherche scientifique (cnrs)

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University of Warsaw

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Royal Institute of Technology

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Warsaw University of Technology

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