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SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learning

Communications Biology, ISSN: 2399-3642, Vol: 6, Issue: 1, Page: 378
2023
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Most Recent News

Study Data from University of Pennsylvania Update Knowledge of Biology (Spadecon: Cell-type Deconvolution In Spatial Transcriptomics With Semi-supervised Learning)

2023 JUN 16 (NewsRx) -- By a News Reporter-Staff News Editor at Ivy League Daily News -- Investigators discuss new findings in Life Sciences -

Article Description

Spatially resolved transcriptomics (SRT) has advanced our understanding of the spatial patterns of gene expression, but the lack of single-cell resolution in spatial barcoding-based SRT hinders the inference of specific locations of individual cells. To determine the spatial distribution of cell types in SRT, we present SpaDecon, a semi-supervised learning approach that incorporates gene expression, spatial location, and histology information for cell-type deconvolution. SpaDecon was evaluated through analyses of four real SRT datasets using knowledge of the expected distributions of cell types. Quantitative evaluations were performed for four pseudo-SRT datasets constructed according to benchmark proportions. Using mean squared error and Jensen-Shannon divergence with the benchmark proportions as evaluation criteria, we show that SpaDecon performance surpasses that of published cell-type deconvolution methods. Given the accuracy and computational speed of SpaDecon, we anticipate it will be valuable for SRT data analysis and will facilitate the integration of genomics and digital pathology.

Bibliographic Details

Coleman, Kyle; Hu, Jian; Schroeder, Amelia; Lee, Edward B; Li, Mingyao

Springer Science and Business Media LLC

Medicine; Biochemistry, Genetics and Molecular Biology; Agricultural and Biological Sciences

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