Smmit: A pipeline for integrating multiple single-cell multi-omics samples.

IF 4.1 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Computational and structural biotechnology journal Pub Date : 2025-09-01 eCollection Date: 2025-01-01 DOI:10.1016/j.csbj.2025.08.020
Changxin Wan, Zhicheng Ji
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引用次数: 0

Abstract

Multi-sample single-cell multi-omics datasets, which simultaneously measure multiple data modalities in the same cells across multiple samples, facilitate the study of gene expression, gene regulatory activities, and protein abundances on a population scale. We developed Smmit, a computational pipeline for integrating data both across samples and modalities. Compared to existing methods, Smmit more effectively removes batch effects while preserving relevant biological information, resulting in superior integration outcomes. Additionally, Smmit is more computationally efficient and builds upon existing computational methods, requiring minimal effort for implementation. While the focus of Smmit is not algorithmic innovation, it provides an empirically useful solution for analyzing multi-sample single-cell multi-omics data. Smmit is an R software package that is freely available on GitHub: https://github.com/zji90/Smmit.

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Smmit:整合多个单细胞多组学样本的管道。
多样本单细胞多组学数据集可以同时测量多个样本中相同细胞的多种数据模式,有助于在群体规模上研究基因表达、基因调控活性和蛋白质丰度。我们开发了Smmit,这是一个跨样本和模式集成数据的计算管道。与现有方法相比,Smmit在保留相关生物信息的同时,更有效地消除了批效应,获得了更好的整合效果。此外,Smmit的计算效率更高,它建立在现有计算方法的基础上,实现所需的工作量最小。虽然Smmit的重点不是算法创新,但它为分析多样本单细胞多组学数据提供了一个经验上有用的解决方案。Smmit是一个R软件包,可以在GitHub上免费获得:https://github.com/zji90/Smmit。
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来源期刊
Computational and structural biotechnology journal
Computational and structural biotechnology journal Biochemistry, Genetics and Molecular Biology-Biophysics
CiteScore
9.30
自引率
3.30%
发文量
540
审稿时长
6 weeks
期刊介绍: Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to: Structure and function of proteins, nucleic acids and other macromolecules Structure and function of multi-component complexes Protein folding, processing and degradation Enzymology Computational and structural studies of plant systems Microbial Informatics Genomics Proteomics Metabolomics Algorithms and Hypothesis in Bioinformatics Mathematical and Theoretical Biology Computational Chemistry and Drug Discovery Microscopy and Molecular Imaging Nanotechnology Systems and Synthetic Biology
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