2017/08/18 by Gal Mishne, Ronen Talmon, Mishne, Gal +7 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Bioinformatics and Genomic Networks #Cell Image Analysis Techniques #FOS: Biological sciences #FOS: Computer and information sciences #Gene expression and cancer classification #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Quantitative Methods (q-bio.QM) #cs.LG #q-bio.QM #stat.ML
paper · pdf · doi:10.48550/arxiv.1708.05768
16 pages, 5 figures. Accepted to IEEE Transactions on Signal and Information Processing over Networks
arxiv created 2017/08/18 · openalex publication_date 2017/08/18 · arxiv updated 2017/08/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider the analysis of high dimensional data given in the form of a matrix with columns consisting of observations and rows consisting of features. Often the data is such that the observations do not reside on a regular grid, and the given order of the features is arbitrary and does not convey a notion of locality. Therefore, traditional transforms and metrics cannot be used for data organization and analysis. In this paper, our goal is to organize the data by defining an appropriate representation and metric such that they respect the smoothness and structure underlying the data. We also aim to generalize the joint clustering of observations and features in the case the data does not fall into clear disjoint groups. For this purpose, we propose multiscale data-driven transforms and metrics based on trees. Their construction is implemented in an iterative refinement procedure that exploits the co-dependencies between features and observations. Beyond the organization of a single dataset, our approach enables us to transfer the organization learned from one dataset to another and to integrate several datasets together. We present an application to breast cancer gene expression analysis: learning metrics on the genes to cluster the tumor samples into cancer sub-types and validating the joint organization of both the genes and the samples. We demonstrate that using our approach to combine information from multiple gene expression cohorts, acquired by different profiling technologies, improves the clustering of tumor samples.