In:
Bioinformatics, Oxford University Press (OUP), Vol. 38, No. 23 ( 2022-11-30), p. 5322-5325
Abstract:
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. Results 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. Availability and implementation 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 information Supplementary data are available at Bioinformatics online.
Type of Medium:
Online Resource
ISSN:
1367-4803
,
1367-4811
DOI:
10.1093/bioinformatics/btac684
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2022
detail.hit.zdb_id:
1468345-3
SSG:
12
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