Dataset Information

Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding.

ABSTRACT: Spatially resolved transcriptomics provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state, but at low transcript detection sensitivity or with limited gene throughput. Comprehensive annotating of cell types in spatially resolved transcriptomics to understand biological processes at the single cell level remains challenging. Here we propose Spatial-ID, a supervision-based cell typing method, that combines the existing knowledge of reference single-cell RNA-seq data and the spatial information of spatially resolved transcriptomics data. We present a series of benchmarking analyses on publicly available spatially resolved transcriptomics datasets, that demonstrate the superiority of Spatial-ID compared with state-of-the-art methods. Besides, we apply Spatial-ID on a self-collected mouse brain hemisphere dataset measured by Stereo-seq, that shows the scalability of Spatial-ID to three-dimensional large field tissues with subcellular spatial resolution.

SUBMITTER: Shen R

PROVIDER: S-EPMC9741613 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding.

Shen Rongbo R Liu Lin L Wu Zihan Z Zhang Ying Y Yuan Zhiyuan Z Guo Junfu J Yang Fan F Zhang Chao C Chen Bichao B Feng Wanwan W Liu Chao C Guo Jing J Fan Guozhen G Zhang Yong Y Li Yuxiang Y Xu Xun X Yao Jianhua J

Nature communications 20221210 1

Spatially resolved transcriptomics provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state, but at low transcript detection sensitivity or with limited gene throughput. Comprehensive annotating of cell types in spatially resolved transcriptomics to understand biological processes at the single cell level remains challenging. Here we propose Spatial-ID, a supervision-based cell typing method, that combines the existing knowledge of refe ...[more]

PMID: 36496406

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Project description:MotivationUnveiling the heterogeneity in the tissues is crucial to explore cell-cell interactions and cellular targets of human diseases. Spatial transcriptomics (ST) supplies spatial gene expression profile which has revolutionized our biological understanding, but variations in cell-type proportions of each spot with dozens of cells would confound downstream analysis. Therefore, deconvolution of ST has been an indispensable step and a technical challenge toward the higher-resolution panorama of tissues.ResultsHere, we propose a novel ST deconvolution method called SD2 integrating spatial information of ST data and embracing an important characteristic, dropout, which is traditionally considered as an obstruction in single-cell RNA sequencing data (scRNA-seq) analysis. First, we extract the dropout-based genes as informative features from ST and scRNA-seq data by fitting a Michaelis-Menten function. After synthesizing pseudo-ST spots by randomly composing cells from scRNA-seq data, auto-encoder is applied to discover low-dimensional and non-linear representation of the real- and pseudo-ST spots. Next, we create a graph containing embedded profiles as nodes, and edges determined by transcriptional similarity and spatial relationship. Given the graph, a graph convolutional neural network is used to predict the cell-type compositions for real-ST spots. We benchmark the performance of SD2 on the simulated seqFISH+ dataset with different resolutions and measurements which show superior performance compared with the state-of-the-art methods. SD2 is further validated on three real-world datasets with different ST technologies and demonstrates the capability to localize cell-type composition accurately with quantitative evidence. Finally, ablation study is conducted to verify the contribution of different modules proposed in SD2.Availability and implementationThe SD2 is freely available in github (https://github.com/leihouyeung/SD2) and Zenodo (https://doi.org/10.5281/zenodo.7024684).Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding.

Publications

Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding.

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