带状相似矩阵的邻接约束层次聚类及其在基因组学中的应用
Adjacency-constrained hierarchical clustering of a band similarity matrix with application to Genomics
AI总结：
针对基因组数据分析中邻接约束层次聚类的二次复杂度问题，提出基于远距对象相似性可忽略假设的准线性复杂度实现，验证了其生物学有效性与高效性，提供R包adjclust。
摘要：
研究背景与问题：全基因组关联研究（GWAS）或Hi-C研究等基因组数据分析通常面临这样的问题：需要基于高分辨率基因座水平测量的相似矩阵，将染色体划分为连续区域。一种直观的解决方法是执行改进的层次凝聚聚类（HAC），该方法仅允许合并相邻的簇（依据染色体内的位置排序）。但该方法的一个主要实际缺陷是其时间和空间复杂度与基因座数量呈二次关系，而每条染色体的基因座数量通常在10^4到10^5量级。\n研究方法：通过假设物理距离较远的对象之间的相似性可忽略不计，我们提出了一种准线性复杂度的邻接约束HAC实现方法。\n实验设置与结果：我们在GWAS和Hi-C数据集上的验证证明了该假设的合理性，同时表明该方法能够识别具有生物学意义的信号。得益于较低的时间和内存占用，该方法可以在标准笔记本电脑上运行数分钟甚至数秒即可完成。\n可用性与实现：相关软件和样本数据以R包adjclust的形式提供，可从综合R存档网络（CRAN）下载。
英文摘要：
Motivation: Genomic data analyses such as Genome-Wide Association Studies (GWAS) or Hi-C studies are often faced with the problem of partitioning chromosomes into successive regions based on a similarity matrix of high-resolution, locus-level measurements. An intuitive way of doing this is to perform a modified Hierarchical Agglomerative Clustering (HAC), where only adjacent clusters (according to the ordering of positions within a chromosome) are allowed to be merged. A major practical drawback of this method is its quadratic time and space complexity in the number of loci, which is typically of the order of 10^4 to 10^5 for each chromosome. Results: By assuming that the similarity between physically distant objects is negligible, we propose an implementation of this adjacency-constrained HAC with quasi-linear complexity. Our illustrations on GWAS and Hi-C datasets demonstrate the relevance of this assumption, and show that this method highlights biologically meaningful signals. Thanks to its small time and memory footprint, the method can be run on a standard laptop in minutes or even seconds. Availability and Implementation: Software and sample data are available as an R package, adjclust, that can be downloaded from the Comprehensive R Archive Network (CRAN).
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