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scINSIGHT 包：整合来自不同生物学条件的单细胞 RNA-Seq 数据。

The scINSIGHT Package for Integrating Single-Cell RNA-Seq Data from Different Biological Conditions.

机构信息

School of Mathematics and Physics, China University of Geosciences, Wuhan, China.

Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA.

出版信息

J Comput Biol. 2022 Nov;29(11):1233-1236. doi: 10.1089/cmb.2022.0244. Epub 2022 Aug 3.

DOI:10.1089/cmb.2022.0244
PMID:35920848
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9700338/
Abstract

Data integration is a critical step in the analysis of multiple single-cell RNA sequencing samples to account for heterogeneity due to both biological and technical variability. scINSIGHT is a new integration method for single-cell gene expression data, and can effectively use the information of biological condition to improve the integration of multiple single-cell samples. scINSIGHT is based on a novel non-negative matrix factorization model that learns common and condition-specific gene modules in samples from different biological or experimental conditions. Using these gene modules, scINSIGHT can further identify cellular identities and active biological processes in different cell types or conditions. Here we introduce the installation and main functionality of the scINSIGHT R package, including how to preprocess the data, apply the scINSIGHT algorithm, and analyze the output.

摘要

数据集成是分析多个单细胞 RNA 测序样本的关键步骤，可解释由于生物和技术变异性引起的异质性。scINSIGHT 是一种新的单细胞基因表达数据集成方法，可以有效地利用生物条件的信息来改善多个单细胞样本的集成。scINSIGHT 基于一种新的非负矩阵分解模型，该模型可以在来自不同生物或实验条件的样本中学习共同的和特定于条件的基因模块。使用这些基因模块，scINSIGHT 可以进一步识别不同细胞类型或条件下的细胞身份和活跃的生物过程。本文介绍了 scINSIGHT R 包的安装和主要功能，包括如何预处理数据、应用 scINSIGHT 算法以及分析输出。

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本文引用的文献

1
scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data.scINSIGHT 用于解释生物异质数据中的单细胞基因表达。
Genome Biol. 2022 Mar 21;23(1):82. doi: 10.1186/s13059-022-02649-3.
2
Selecting gene features for unsupervised analysis of single-cell gene expression data.选择基因特征用于单细胞基因表达数据的无监督分析。
Brief Bioinform. 2021 Nov 5;22(6). doi: 10.1093/bib/bbab295.
3
A benchmark of batch-effect correction methods for single-cell RNA sequencing data.单细胞 RNA 测序数据批次效应校正方法的基准测试。
Genome Biol. 2020 Jan 16;21(1):12. doi: 10.1186/s13059-019-1850-9.
4
Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity.单细胞多组学整合比较和对比脑细胞特征。
Cell. 2019 Jun 13;177(7):1873-1887.e17. doi: 10.1016/j.cell.2019.05.006. Epub 2019 Jun 6.
5
Comprehensive Integration of Single-Cell Data.单细胞数据的综合整合。
Cell. 2019 Jun 13;177(7):1888-1902.e21. doi: 10.1016/j.cell.2019.05.031. Epub 2019 Jun 6.
6
Learning common and specific patterns from data of multiple interrelated biological scenarios with matrix factorization.利用矩阵分解从多个相互关联的生物学场景的数据中学习常见和特定模式。
Nucleic Acids Res. 2019 Jul 26;47(13):6606-6617. doi: 10.1093/nar/gkz488.
7
Decomposing Cell Identity for Transfer Learning across Cellular Measurements, Platforms, Tissues, and Species.跨细胞测量、平台、组织和物种进行迁移学习的细胞身份分解。
Cell Syst. 2019 May 22;8(5):395-411.e8. doi: 10.1016/j.cels.2019.04.004.
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A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data.一种用于在异质组学多模态数据中检测模块的非负矩阵分解方法。
Bioinformatics. 2016 Jan 1;32(1):1-8. doi: 10.1093/bioinformatics/btv544. Epub 2015 Sep 15.

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