Nonlinear dimensionality reduction methods for synthetic biology biobricks' visualization.
Nonlinear dimensionality reduction methods for synthetic biology biobricks' visualization.
复制标题
合成生物学生物砖可视化的非线性降维方法
DOI：
10.1186/s12859-017-1484-4
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发表时间：
2017-01-19
影响因子：
3
通讯作者：
Alterovitz G
中科院分区：
文献类型：
--
作者：
Yang J;Wang H;Ding H;An N;Alterovitz G
通过降维实现数据可视化是生物信息学中的一项重要策略，它有助于发现数据隐藏的属性和检测数据质量问题，如数据噪声、数据标记不当等。由于基于众包的合成生物学数据库面临类似的数据质量问题，我们提出可视化生物砖来解决这些问题。然而，现有的降维方法不能直接应用于生物砖数据集。因此，我们使用归一化编辑距离来增强降维方法，包括Isomap和Laplacian eigenmap。通过从合成生物学数据库Registry of Standard Biological Parts中提取生物砖，测试了6种不同类型生物砖的组合。可视化图形显示了被区分的生物砖和不适当标记的生物砖。采用聚类算法K-means对约简结果进行量化。Isomap和Laplacian eigenmap的平均聚类精度分别为0.857和0.844。Laplacian Eigenmaps的识别速度是Isomap的5倍，其可视化图形更加集中。将归一化编辑距离与Isomap和Laplacian eigenmap相结合，成功实现了合成生物学生物块在二维空间的可视化。对不同类型的生物砖进行区分，确定不适当标记的生物砖，有助于评估基于众包的合成生物学数据库的质量，进行生物砖的选择。本文的在线版本（doi:10.1186/s12859-017-1484-4）包含补充材料，可供授权用户使用。
Visualizing data by dimensionality reduction is an important strategy in Bioinformatics, which could help to discover hidden data properties and detect data quality issues, e.g. data noise, inappropriately labeled data, etc. As crowdsourcing-based synthetic biology databases face similar data quality issues, we propose to visualize biobricks to tackle them. However, existing dimensionality reduction methods could not be directly applied on biobricks datasets. Hereby, we use normalized edit distance to enhance dimensionality reduction methods, including Isomap and Laplacian Eigenmaps. By extracting biobricks from synthetic biology database Registry of Standard Biological Parts, six combinations of various types of biobricks are tested. The visualization graphs illustrate discriminated biobricks and inappropriately labeled biobricks. Clustering algorithm K-means is adopted to quantify the reduction results. The average clustering accuracy for Isomap and Laplacian Eigenmaps are 0.857 and 0.844, respectively. Besides, Laplacian Eigenmaps is 5 times faster than Isomap, and its visualization graph is more concentrated to discriminate biobricks. By combining normalized edit distance with Isomap and Laplacian Eigenmaps, synthetic biology biobircks are successfully visualized in two dimensional space. Various types of biobricks could be discriminated and inappropriately labeled biobricks could be determined, which could help to assess crowdsourcing-based synthetic biology databases’ quality, and make biobricks selection. The online version of this article (doi:10.1186/s12859-017-1484-4) contains supplementary material, which is available to authorized users.