得益于高通量测序技术的发展,已经积累了大量的各种生物分子数据,以彻底改变基因组学和分子生物学的研究。分析这种生物分子数据的主要挑战之一是将它们的亚型聚类成亚群以促进随后的下游分析。最近,已经开发了许多聚类方法来解决生物分子数据。然而,计算方法经常受到许多限制,如高维,数据异质性和噪声。
在我们的研究中,我们开发了一种新颖的基于图的多层次一致性聚类(GMHCC)方法,该方法采用无监督的基于图的特征排序(FR)和基于图的链接方法,以探索多种类型生物分子数据的一致性聚类的底层分区的多个层次信息.的确,我们首先建议使用基于图的无监督FR模型来测量每个特征,方法是在成对特征上构建一个图,然后为每个特征提供一个秩。随后,为了保持基本分区(BP)的多样性和鲁棒性,我们提出了多个不同的特征子集来生成多个BP,然后通过细化全局一致性函数来探索多个BP的层次结构。最后,我们开发了一种新的基于图的链接方法,,它明确考虑集群之间的关系以生成最终分区。对包括35个癌症基因表达数据集和8个单细胞RNA-seq数据集在内的多种类型的生物分子数据的实验验证了我们的方法相对于几种最先进的共识聚类方法的有效性。此外,差异基因分析,进行了基因本体论富集分析和KEGG通路分析,提供对细胞发育谱系和表征机制的新见解。
源代码可在GitHub获得:https://github.com/yifuLu/GMHCC。软件和支持数据可从以下网址下载:https://figshare.com/articles/software/GMHCC/17111291。
补充数据可在生物信息学在线获得。
Thanks to the development of high-throughput sequencing technologies, massive amounts of various biomolecular data have been accumulated to revolutionize the study of genomics and molecular biology. One of the main challenges in analyzing this biomolecular data is to cluster their subtypes into subpopulations to facilitate subsequent downstream analysis. Recently, many clustering methods have been developed to address the biomolecular data. However, the computational methods often suffer from many limitations such as high dimensionality, data heterogeneity and noise.
In our study, we develop a novel Graph-based Multiple Hierarchical
Consensus Clustering (GMHCC) method with an unsupervised graph-based feature ranking (FR) and a graph-based linking method to explore the multiple hierarchical information of the underlying partitions of the
consensus clustering for multiple types of biomolecular data. Indeed, we first propose to use a graph-based unsupervised FR model to measure each feature by building a graph over pairwise features and then providing each feature with a rank. Subsequently, to maintain the diversity and robustness of basic partitions (BPs), we propose multiple diverse feature subsets to generate several BPs and then explore the hierarchical structures of the multiple BPs by refining the global
consensus function. Finally, we develop a new graph-based linking method, which explicitly considers the relationships between clusters to generate the final partition. Experiments on multiple types of biomolecular data including 35 cancer gene expression datasets and eight single-cell RNA-seq datasets validate the effectiveness of our method over several state-of-the-art
consensus clustering approaches. Furthermore, differential gene analysis, gene ontology enrichment analysis and KEGG pathway analysis are conducted, providing novel insights into cell developmental lineages and characterization mechanisms.
The source code is available at GitHub: https://github.com/yifuLu/GMHCC. The software and the supporting data can be downloaded from: https://figshare.com/articles/software/GMHCC/17111291.
Supplementary data are available at Bioinformatics online.