Article

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 into distinct modules and a different filter applied to each. Applied to synthetic data with known network structure and signal, network filters accurately reduce noise across a wide range of noise levels and structures. Applied to a machine learning task of predicting changes in human protein expression in healthy and cancerous tissues, network filtering prior to training increases accuracy up to 43% compared to using unfiltered data.

  Conclusions

  Network filters are a general way to denoise biological data and can account for both correlation and anti-correlation between different measurements. Furthermore, we find that partitioning a network prior to filtering can significantly reduce errors in networks with heterogenous data and correlation patterns, and this approach outperforms existing diffusion based methods. Our results on proteomics data indicate the broad potential utility of network filters to applications in systems biology.

  The datasets and code supporting the conclusions of the article are available in github repository https://github.com/andykavran/Network_Filters.

   

Creator

• Clauset, Aaron
• Kavran, Andrew J

Date Issued

• 2021

Academic Affiliation

• Biochemistry
• Biofrontiers Institute
• Computer Science

Journal Title

• BMC Bioinformatics

Journal Volume

• 22

Last Modified

• 2022-08-16

Resource Type

• Article

Rights Statement

• In Copyright

DOI

•  https://doi.org/10.1186/s12859-021-04075-x

ISSN

• 1471-2105

Language

• English [eng]

License

• Creative Commons BY Attribution 4.0 International

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