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LDADW: An Algorithm for Integrating Single-Cell and Spatial Transcriptomic Data Based on the Topic Model
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DOI：10.1007/978-981-95-0698-9_18.png)
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Abstract
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Single-cell RNA sequencing and spatial transcriptomics(ST) have provided new perspectives for exploring the mechanisms of cancer development, diagnosis, and treatment. However, due to the highly sparse nature of single-cell and spatial transcriptome data, it remains a challenge to extract the cell type composition information of each point in the spatial transcriptome data from single-cell data. To address this issue, this study proposes an integrated analysis method called LDADW, which is based on the Latent Dirichlet Allocation (LDA) topic model and damped weighted least squares. By using LDA to mine the cell function topics in single-cell data, a topic-gene feature matrix is constructed. Combined with the damped weighted least squares deconvolution technique, the cell composition of spatial sites is analyzed. This method significantly outperforms the current RCTD, Seurat, Spotlight, and Tangram models. Verified on the simulated dataset of the mouse pancreas, the recall rate of LDADW reaches 94% and the accuracy rate reaches 80%. It effectively reveals the spatial heterogeneity of tumors and provides a new tool for multi-omics integration.
Keywords:
Single-cell transcriptome
Spatial transcriptome
LDA topic model
Damped weighted least squares
Spatial heterogeneity of tumors
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25
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Cited Papers
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