Abstract
BackgroundLarge-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.ResultsWe 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.ConclusionsNetwork 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.
Highlights
Large-scale biological data sets are often contaminated by noise, which can impede accurate inferences about underlying processes
Network filters A network filter is specified by a function f [i, x, G], which takes as input the index of the measurement to be denoised, the list of all measurements x, and the network structure G among those measurements
We note that the idea of a network filter can naturally generalize to exploit information, if available, about the sign or strength of interactions in G
Summary
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. System-wide molecular profiling data are often contaminated by noise, which can obscure biological signals of interest Such noise can arise from both endogenous biological factors and exogenous technical factors. These factors include reagent and protocol variability, researcher technique, passage number effects, stochastic gene expression, and cell cycle asynchronicity. Identifying and correcting noisy measurements before analysis is likely to improve the detection of subtle biological signals and enable more accurate predictions in systems biology
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
