Clustering-independent analysis of genomic data using spectral simplicial theory.

ABSTRACT: The prevailing paradigm for the analysis of biological data involves comparing groups of replicates from different conditions (e.g. control and treatment) to statistically infer features that discriminate them (e.g. differentially expressed genes). However, many situations in modern genomics such as single-cell omics experiments do not fit well into this paradigm because they lack true replicates. In such instances, spectral techniques could be used to rank features according to their degree of consistency with an underlying metric structure without the need to cluster samples. Here, we extend spectral methods for feature selection to abstract simplicial complexes and present a general framework for clustering-independent analysis. Combinatorial Laplacian scores take into account the topol

SUBMITTER: Govek KW 

PROVIDER: S-EPMC6897424 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature