基于距离转换参数优化的HiC数据增强分析

Analysis of HiC Data Enhancement Based on Distance Conversion Parameter Optimization

高通量染色质构象捕获(HiC)技术可反映染色体各位点之间的接触情况,为研究染色体三维空间结构和基因共调控机制提供依据。但目前受到测序技术和实验成本的制约,生物实验通常只能获得大量中低分辨率HiC数据,造成染色质远距离接触信息的缺失,为此,本研究将最短路径原理应用于增强低分辨率的HiC数据。首先将染色体接触数据转换为距离矩阵,利用Floyd算法对其进行最短路径更新后,再将其逆变换为增强的HiC数据,从而实现对远距离接触信息的恢复。在距离转换过程中,以Spearman相关系数作为评估指标,利用自适应权值粒子群算法(APSO)对转换参数进行全局寻优。本研究对人类不同组织细胞(人B淋巴细胞系(GM12878),人胚肺成纤维细胞系(IMR90)和人类白血病细胞系(K562)) HiC数据进行了增强,结果表明该算法能够对不同下采样的HiC数据显著增强。本研究的研究思路可为HiC数据增强提供有益借鉴。

The high-throughput chromatin conformational capture(HiC) technology reflects the contacts between the chromosomes and provides a basis for studying the three-dimensional spatial structure of chromosomes and the co-regulatory mechanism of genes. However, currently limited by sequencing technology and experimental cost,biological experiments usually can only obtain a large number of low-resolution HiC data, which causes the lack of information on chromatin long-distance contact. Therefore, this paper applies the shortest path principle to enhance low-resolution HiC data. The chromosome contact data is first converted into a distance matrix, and the shortest path is updated using the Floyd algorithm, and then it is inversely transformed into enhanced HiC data to achieve the recovery of long-distance contact information. In the distance conversion process, the Spearman correlation coefficient is used as the evaluation index, and the adaptive weighted particle swarm algorithm(APSO) is used to optimize the conversion parameters globally. This article enhances the HiC data of different human tissue cells(human B lymphocyte line(GM12878), human embryo lung fibroblast cell line(IMR90) and human leukemia cell line(K562)). The results show that the algorithm can significantly enhance the HIC data of different down sampling.The research ideas in this paper can provide useful reference for HiC data enhancement.