Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution
By Bin Li, Wen Zhang, Chuang Guo, Hao Xu, Longfei Li, Minghao Fang, Yinlei Hu, Xinye Zhang, Xinfeng Yao, Meifang Tang and 7 more. Nature Methods, 2022
382 citations recorded by the source corpus.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/fc35e6010dbf5c44949a96091634ba3fa192f918
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In Molecular Biology
- Malte D. Luecken
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Citations in the corpus
- Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST (2023, positive): “The fi rst example tested simulated data obtained from the benchmarking study by Li et al. 41 , downloaded from https://github. com/QuKunLab/SpatialBenchmarking/tree/main/FigureData/Figure4.”
- Best practices for single-cell analysis across modalities (2023, methodology): “…, for example, leverage spatial correlation testing, BayesSpace 227 uses Markov random fields, spaGCN 229 uses graph neural networks to integrate gene expression data, spatial information and histology images, and sepal 235 utilizes diffusion-based modelling to identify genes with spatial patterns.”