GNTD: reconstructing spatial transcriptomes with graph-guided neural tensor decomposition informed by spatial and functional relations

Spatially-resolved RNA profiling has now been widely used to understand cells’ structural organizations and functional roles in tissues, yet it is challenging to reconstruct the whole spatial transcriptomes due to various inherent technical limitations in tissue section preparation and RNA capture and fixation in the application of the spatial RNA profiling technologies. Here, we introduce a graph-guided neural tensor decomposition (GNTD) model for reconstructing whole spatial transcriptomes in tissues. GNTD employs a hierarchical tensor structure and formulation to explicitly model the high-order spatial gene expression data with a hierarchical nonlinear decomposition in a three-layer neural network, enhanced by spatial relations among the capture spots and gene functional relations for accurate reconstruction from highly sparse spatial profiling data. Extensive experiments on 22 Visium spatial transcriptomics datasets and 3 high-resolution Stereo-seq datasets as well as simulation data demonstrate that GNTD consistently improves the imputation accuracy in cross-validations driven by nonlinear tensor decomposition and incorporation of spatial and functional information, and confirm that the imputed spatial transcriptomes provide a more complete gene expression landscape for downstream analyses of cell/spot clustering for tissue segmentation, and spatial gene expression clustering and visualizations.


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All manuscripts must include a data availability statement.This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A description of any restrictions on data availability -For clinical datasets or third party data, please ensure that the statement adheres to our policy Rui Kuang 10/16/2023
Datasets analyzed in this paper are available in raw form from their original studies.Specifically, 10 Visium spatial transcriptomics data for 5 mouse brain tissues, 1 mouse kidney tissue, 2 human breast cancer tissues, 1 human heart tissue, and 1 human lymph node tissue are collected from the 10x Genomics website https://support.10xgenomics.com/spatial-gene-expression/datasets/,where the manual annotation on the human breast cancer section 1 is accessible at https://github.com/JinmiaoChenLab/SEDR\_analyses/tree/master/data/BRCA1.12 Visium spatial transcriptomics data for human dorsolateral prefrontal cortex and their manual annotations are obtained from the LIBD project http://spatial.libd.org/spatialLIBD/.3 Stereo-seq spatial transcriptomics data for 1 mouse brain and 2 mouse olfactory bulb tissues and their manual annotations are available at https://db.cngb.org/stomics/mosta/download/.
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