AIscEA: unsupervised integration of single-cell gene expression and chromatin accessibility via their biological consistency.
AIscEA: unsupervised integration of single-cell gene expression and chromatin accessibility via their biological consistency.
复制标题
AIscEA：通过其生物一致性对单细胞基因表达和染色质可及性进行无监督整合。
DOI：
10.1093/bioinformatics/btac683
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Wang,Yijie
中科院分区：
文献类型：
--
作者：
Jafari,Elham;Johnson,Travis;Wang,Yue;Liu,Yunlong;Huang,Kun;Wang,Yijie
单细胞基因表达和染色质可及性测量的综合分析对于揭示基因调控是必不可少的，但它是计算生物学的关键挑战之一。基因表达和染色质可及性是来自不同模态的测量，并且没有共同的特征可以直接用于指导整合。目前的最先进的方法缺乏实际的解决方案，寻找异构集群。然而，以前的方法可能不会产生可靠的结果时，集群异质性存在。更重要的是，目前的方法缺乏一种有效的方法来选择超参数下的无监督设置。因此，应用计算方法来整合单细胞基因表达和染色质可及性measurements仍然difficult.ResultsWe介绍AIscEA-对齐-based Integration of single-cell geneExpression and chromatinaccessibility-一种计算方法，它集成了单细胞基因表达和染色质可及性测量，使用它们的生物一致性。AIscEA首先定义了一个排序的相似性得分，以量化测量中细胞簇之间的生物一致性。然后，AIscEA使用排名的相似性得分和一种新的排列测试来识别跨测量的聚类对齐。AIscEA还利用对齐的细胞簇的图形对齐来跨测量对齐细胞。我们在几个基准数据集上将AIscEA与竞争方法进行了比较，并证明了AIscEA对超参数的选择具有高度鲁棒性，可以更好地处理聚类异构性问题。此外，AIscEA在整合真实世界的SNARE-seq和scMultiome-seq数据集时，在整合准确性方面明显优于最先进的方法。可用性和实施AIscEA可在FigShare上的https://figshare.com/articles/software/AIscEA_zip/21291135以及GitHub上的{https：//github.com/elhaam/AIscEA}上获得。补充信息补充数据可在Bioinformaticsonline上获得。
MotivationThe integrative analysis of single-cell gene expression and chromatin accessibility measurements is essential for revealing gene regulation, but it is one of the key challenges in computational biology. Gene expression and chromatin accessibility are measurements from different modalities, and no common features can be directly used to guide integration. Current state-of-the-art methods lack practical solutions for finding heterogeneous clusters. However, previous methods might not generate reliable results when cluster heterogeneity exists. More importantly, current methods lack an effective way to select hyper-parameters under an unsupervised setting. Therefore, applying computational methods to integrate single-cell gene expression and chromatin accessibility measurements remains difficult.ResultsWe introduce AIscEA—Alignment-basedIntegration of single-cell geneExpression and chromatinAccessibility—a computational method that integrates single-cell gene expression and chromatin accessibility measurements using their biological consistency. AIscEA first defines a ranked similarity score to quantify the biological consistency between cell clusters across measurements. AIscEA then uses the ranked similarity score and a novel permutation test to identify cluster alignment across measurements. AIscEA further utilizes graph alignment for the aligned cell clusters to align the cells across measurements. We compared AIscEA with the competing methods on several benchmark datasets and demonstrated that AIscEA is highly robust to the choice of hyper-parameters and can better handle the cluster heterogeneity problem. Furthermore, AIscEA significantly outperforms the state-of-the-art methods when integrating real-world SNARE-seq and scMultiome-seq datasets in terms of integration accuracy.Availability and implementationAIscEA is available at https://figshare.com/articles/software/AIscEA_zip/21291135 on FigShare as well as {https://github.com/elhaam/AIscEA} onGitHub.Supplementary informationSupplementary data are available atBioinformaticsonline.