2023,
Bioinformatics.
DOI:
10.1093/bioinformatics/btad596
DeepCCI: a deep learning framework for identifying cell–cell interactions from single-cell RNA sequencing data
Wenyi Yang, Pingping Wang, Meng Luo, Yideng Cai, Chang Xu, Guangfu Xue, Xiyun Jin, Rui Cheng, Jinhao Que, Fenglan Pang, Yuexin Yang, Huan Nie, Qinghua Jiang, Zhigang Liu, Zhaochun Xu
Abstract:
Abstract
Motivation
Cell–cell interactions (CCIs) play critical roles in many biological processes such as cellular differentiation, tissue homeostasis, and immune response. With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq) technologies, it is of high importance to identify CCIs from the ever-increasing scRNA-seq data. However, limited by the algorithmic constraints, current computational methods based on statistical strategies ignore some key latent information contained in scRNA-seq data with high sparsity and heterogeneity.
Results
Here, we developed a deep learning framework named DeepCCI to identify meaningful CCIs from scRNA-seq data. Applications of DeepCCI to a wide range of publicly available datasets from diverse technologies and platforms demonstrate its ability to predict significant CCIs accurately and effectively. Powered by the flexible and easy-to-use software, DeepCCI can provide the one-stop solution to discover meaningful intercellular interactions and build CCI networks from scRNA-seq data.
Availability and implementation
The source code of DeepCCI is available online at https://github.com/JiangBioLab/DeepCCI.
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2023-10-31 13:21:00
李翛然:
#paper doi:10.1093/bioinformatics/btad596 DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data
一个新的框架，在用scRNA的数据来解释细胞互作，不过我觉得最大的问题是，看了一下他的训练集和数据集，还是通过对于scRNA的初步处理数据，即做到uMAP的降维分类后就来训练，还是非常初级的想法，真正的细胞互作的机理在这个颗粒度下的解释会很糟糕。不过也算是一个跨领域的应用 值得鼓励