Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.
Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, China.
Bioinformatics. 2019 Apr 15;35(8):1278-1283. doi: 10.1093/bioinformatics/bty796.
Stitching together trans-omics data is a powerful approach to assess the complex mechanisms of cancer occurrence, progression and treatment. However, the integration process suffers from the 'block missing' phenomena when part of individuals lacks some omics data.
We proposed a k-nearest neighbor (kNN) weighted imputation method for trans-omics block missing data (TOBMIkNN) to handle gene-absence individuals in RNA-seq datasets using external information obtained from DNA methylation probe datasets. Referencing to multi-hot deck, mean imputation and missing cases deletion, we assess the relative error, absolute error, inter-omics correlation structure change and variable selection.The proposed method, TOBMIkNN reliably imputed RNA-seq data by borrowing information from DNA methylation data, and showed superiority over the other three methods in imputation error and stability of correlation structure. Our study indicates that TOBMIkNN can be used as an advisable method for trans-omics block missing data imputation.
TOBMIkNN is freely available at https://github.com/XuesiDong/TOBMI.
Supplementary data are available at Bioinformatics online.
将跨组学数据整合在一起是评估癌症发生、进展和治疗复杂机制的一种强有力的方法。然而，在部分个体缺乏某些组学数据时，整合过程会受到“块缺失”现象的影响。
我们提出了一种 k-最近邻(kNN)加权插补方法（TOBMIkNN），用于处理 RNA-seq 数据集中基因缺失个体的跨组学块缺失数据（TOBMIkNN），该方法使用来自 DNA 甲基化探针数据集的外部信息。参照多热甲板、均值插补和缺失案例删除，我们评估了相对误差、绝对误差、组间相关性结构变化和变量选择。所提出的方法 TOBMIkNN 通过从 DNA 甲基化数据中借用信息可靠地插补 RNA-seq 数据，并在插补误差和相关性结构稳定性方面优于其他三种方法。我们的研究表明，TOBMIkNN 可作为跨组学块缺失数据插补的一种可行方法。
TOBMIkNN 可在 https://github.com/XuesiDong/TOBMI 上免费获得。
补充数据可在生物信息学在线获得。
