Matrix completion with side information and its applications in predicting the antigenicity of influenza viruses
Matrix completion with side information and its applications in predicting the antigenicity of influenza viruses
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DOI：
10.1093/bioinformatics/btx390
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发表时间：
2017-10-15
期刊：
影响因子：
5.8
通讯作者：
Yang, Jialiang
中科院分区：
文献类型：
--
作者：
Huang, Li;Li, Xianhong;Yang, Jialiang
动机：根据部分揭示的血凝抑制表，低等级矩阵补全已被证明在预测流感病毒和疫苗之间的抗原距离方面是有效的。同时，流感血凝素(HA)蛋白序列也可以有效地推断抗原距离。因此，将HA蛋白序列信息集成到低阶矩阵完成模型中以帮助推断流感抗原性是很自然的，这对流感疫苗的开发至关重要。结果：我们提出了一种新的算法，称为带边信息的生物矩阵完成算法(BMCSI)，该算法首先测量流感病毒之间的HA蛋白序列相似性(特别是表位)，然后将相似性信息整合到低阶矩阵完成模型中来预测流感的抗原性。该算法既利用了血清学试验中病毒和疫苗之间的相关性，又利用了HA序列在预测流感抗原性方面的能力。我们将该模型应用于H3N2型季节性流感病毒数据。与以前的方法相比，我们在10倍交叉验证分析中显著降低了预测均方误差。根据输入数据构建的地图，我们发现H3N2型季节性流感的抗原进化总体上是S形的，而基因进化是半圆形的。我们还表明，遗传距离和抗原距离(抗原簇之间)之间的Spearman相关性为0.83，表明流感基因和抗原进化之间的全局高度一致性和局部差异。最后，我们发现4.4%-61.2%的遗传变异(对应于3.1161.08抗原距离)在历史上导致了H3N2型流感病毒的抗原漂移事件。
Motivation: Low-rank matrix completion has been demonstrated to be powerful in predicting antigenic distances among influenza viruses and vaccines from partially revealed hemagglutination inhibition table. Meanwhile, influenza hemagglutinin (HA) protein sequences are also effective in inferring antigenic distances. Thus, it is natural to integrate HA protein sequence information into low-rank matrix completion model to help infer influenza antigenicity, which is critical to influenza vaccine development.Results: We have proposed a novel algorithm called biological matrix completion with side information (BMCSI), which first measures HA protein sequence similarities among influenza viruses (especially on epitopes) and then integrates the similarity information into a low-rank matrix completion model to predict influenza antigenicity. This algorithm exploits both the correlations among viruses and vaccines in serological tests and the power of HA sequence in predicting influenza antigenicity. We applied this model into H3N2 seasonal influenza virus data. Comparing to previous methods, we significantly reduced the prediction root-mean-square error in a 10-fold cross validation analysis. Based on the cartographies constructed from imputed data, we showed that the antigenic evolution of H3N2 seasonal influenza is generally S-shaped while the genetic evolution is half-circle shaped. We also showed that the Spearman correlation between genetic and antigenic distances (among antigenic clusters) is 0.83, demonstrating a globally high correspondence and some local discrepancies between influenza genetic and antigenic evolution. Finally, we showed that 4.4% 61.2% genetic variance (corresponding to 3.1161.08 antigenic distances) caused an antigenic drift event for H3N2 influenza viruses historically.