Denoising large-scale biological data using network filters.

ABSTRACT:

Background

Large-scale biological data sets are often contaminated by noise, which can impede accurate inferences about underlying processes. Such measurement noise can arise from endogenous biological factors like cell cycle and life history variation, and from exogenous technical factors like sample preparation and instrument variation.

Results

We describe a general method for automatically reducing noise in large-scale biological data sets. This method uses an interaction network to identify groups of correlated or anti-correlated measurements that can be combined or "filtered" to better recover an underlying biological signal. Similar to the process of denoising an image, a single network filter may be applied to an entire system, or the system may be first decomposed in

SUBMITTER: Kavran AJ 

PROVIDER: S-EPMC7992843 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature