Wavelet-domain elastic net for clustering on genomes strains

Abstract We propose to evaluate genome similarity by combining discrete non-decimated wavelet transform (NDWT) and elastic net. The wavelets represent a signal with levels of detail, that is, hidden components are detected by means of the decomposition of this signal, where each level provides a different characteristic. The main feature of the elastic net is the grouping of correlated variables where the number of predictors is greater than the number of observations. The combination of these two methodologies applied in the clustering analysis of the Mycobacterium tuberculosis genome strains proved very effective, being able to identify clusters at each level of decomposition.

摘要：本文提出将离散非抽取小波变换（discrete non-decimated wavelet transform，NDWT）与弹性网（elastic net）相结合，用于基因组相似性评估。小波变换可通过多尺度细节表征信号，即通过对该信号进行分解以检测隐藏成分，且每一分解尺度均具备独特的特征。弹性网的核心优势在于可对相关变量进行分组，尤其适用于预测变量数量多于观测样本量的场景。将这两种方法结合应用于结核分枝杆菌（Mycobacterium tuberculosis）基因组菌株的聚类分析，结果显示其成效显著，能够在各分解尺度下精准识别聚类簇。