The scINSIGHT Package for Integrating Single-Cell RNA-Seq Data from Different Biological Conditions.
The scINSIGHT Package for Integrating Single-Cell RNA-Seq Data from Different Biological Conditions.
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scINSIGHT 软件包用于整合来自不同生物条件的单细胞 RNA-Seq 数据。
DOI：
10.1089/cmb.2022.0244
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Li,WeiVivian
中科院分区：
文献类型：
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作者：
Qian,Kun;Fu,Shiwei;Li,Hongwei;Li,WeiVivian
数据整合是分析多个单细胞RNA测序样品的关键步骤，以解释由于生物学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整合方法，能够有效利用生物学状态信息，提高多个单细胞样本的整合能力。scINSIGHT基于一种新的非负矩阵因子分解模型，该模型可以从不同生物或实验条件下的样本中学习常见和条件特异性基因模块。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性生物过程。这里我们将介绍scINSIGHT R软件包的安装和主要功能，包括如何对数据进行预处理、应用scINSIGHT算法以及分析输出。
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.