Python(编程语言)
计算机科学
插补(统计学)
工作流程
聚类分析
数据挖掘
源代码
兰德指数
图形
人工智能
机器学习
缺少数据
数据库
理论计算机科学
程序设计语言
作者
Haocheng Gu,Hao Cheng,Anjun Ma,Yang Li,Juexin Wang,Dong Xu,Qin Ma
标识
DOI:10.1093/bioinformatics/btac684
摘要
Gene expression imputation has been an essential step of the single-cell RNA-Seq data analysis workflow. Among several deep learning methods, the debut of scGNN gained substantial recognition in 2021 for its superior performance and the ability to produce a cell-cell graph. However, the implementation of scGNN was relatively time-consuming and its performance could still be optimized.The implementation of scGNN 2.0 is significantly faster than scGNN thanks to a simplified close-loop architecture. For all eight datasets, cell clustering performance was increased by 85.02% on average in terms of adjusted rand index, and the imputation Median L1 Error was reduced by 67.94% on average. With the built-in visualizations, users can quickly assess the imputation and cell clustering results, compare against benchmarks, and interpret the cell-cell interaction. The expanded input and output formats also pave the way for custom workflows that integrate scGNN 2.0 with other scRNA-Seq toolkits on both Python and R platforms.scGNN 2.0 is implemented in Python (as of version 3.8) with the source code available at https://github.com/OSU-BMBL/scGNN2.0.Supplementary files are available at Bioinformatics online.
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