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Activity Number: 581 - Advanced Cross-Disciplinary Statistical Methods in Statistical Genomics
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 3:00 PM to 4:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #312182
Title: Multi-Resolution Clustering of Omics Data for Pattern Discovery
Author(s): Ali Rahnavard*
Companies: George Washington University
Keywords: Omics data; Clustering; Discretization ; Association testing; Metagenomics; Entropy

We present a clustering method that detects biologically relevant features using omics data (e.g. microbial genes and gene expressions) where the similarity of features can be different resolutions of similarity. The resolution of similarity score takes into account not only similarity between measurements and also the entire relationship between all features (using hierarchical structure) of data and the number of features which group together. The proposed multi-resolution clustering is well suitable for dimension reduction and finding biological meaningful subsets of samples or features using omics data. This method captures biological signals in the presence of noise introduced by measuring technologies and as a powerful technique to discretizing omics data. We cluster microbial strains form metagenomics data and cell lines from gene expression as applications for our proposed approach.

Authors who are presenting talks have a * after their name.

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