Model-based prediction of spatial gene expression via generative linear mapping

Yasushi Okochi,Takefumi Kondo,Ken Nakae,Shunta Sakaguchi,Honda Naoki

doi:10.1038/s41467-021-24014-x

Yasushi Okochi, Takefumi Kondo + Show 3 more

Open Access

https://doi.org/10.1038/s41467-021-24014-x

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Abstract

Decoding spatial transcriptomes from single-cell RNA sequencing (scRNA-seq) data has become a fundamental technique for understanding multicellular systems; however, existing computational methods lack both accuracy and biological interpretability due to their model-free frameworks. Here, we introduce Perler, a model-based method to integrate scRNA-seq data with reference in situ hybridization (ISH) data. To calibrate differences between these datasets, we develop a biologically interpretable model that uses generative linear mapping based on a Gaussian mixture model using the Expectation–Maximization algorithm. Perler accurately predicts the spatial gene expression of Drosophila embryos, zebrafish embryos, mammalian liver, and mouse visual cortex from scRNA-seq data. Furthermore, the reconstructed transcriptomes do not over-fit the ISH data and preserved the timing information of the scRNA-seq data. These results demonstrate the generalizability of Perler for dataset integration, thereby providing a biologically interpretable framework for accurate reconstruction of spatial transcriptomes in any multicellular system.

Highlights

Decoding spatial transcriptomes from single-cell RNA sequencing data has become a fundamental technique for understanding multicellular systems; existing computational methods lack both accuracy and biological interpretability due to their modelfree frameworks
Perler is a computational method for model-based prediction of spatial genome-wide expression profiles from scRNA-seq data that works by referencing spatial gene-expression profiles measured by in situ hybridization (ISH) (Fig. 1a)
We developed a model-based computational method (Perler) that predicts genome-wide spatial transcriptomes

Summary

Introduction

Decoding spatial transcriptomes from single-cell RNA sequencing (scRNA-seq) data has become a fundamental technique for understanding multicellular systems; existing computational methods lack both accuracy and biological interpretability due to their modelfree frameworks. To compensate for the lost spatial information, new computational approaches have emerged (Seurat (v.1)[4], DistMap[5], Achim et al.[6], Halpern et al.7), enabling reconstruction of genome-wide spatial expression profiles from scRNA-seq data by integrating existing ISH data as a spatial reference map in silico Their methods require binarization of gene-expression data[8], which leads to unsatisfactory accuracy, or tissue-specific modeling, which leads difficulty in application to other systems. Given this model-free property, these methods are dependent upon nonlinear NN mapping, which innately causes overfitting to the reference ISH data To address these issues, we propose a model-based computational method for probabilistic embryo reconstruction by linear evaluation of scRNA-seq (Perler), which reconstructs spatial gene-expression profiles via generative linear modeling in a biologically interpretable framework.

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