Denoising large-scale biological data using network filters

Abstract

Large-scale biological data sets, e.g., transcriptomic, proteomic, or ecological, 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. Here 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 58% compared to using unfiltered data. These results indicate the broad potential utility of network-based filters to applications in systems biology. Author SummarySystem-wide measurements of many biological signals, whether derived from molecules, cells, or entire organisms, are often noisy. Removing or mitigating this noise prior to analysis can improve our understanding and predictions of biological phenomena. We describe a general way to denoise biological data that can account for both correlation and anti-correlation between different measurements. These "network filters" take as input a set of biological measurements, e.g., metabolite concentration, animal traits, neuron activity, or gene expression, and a network of how those measurements are biologically related, e.g., a metabolic network, food web, brain connectome, or protein-protein interaction network. Measurements are then "filtered" for correlated or anti-correlated noise using a set of other measurements that are identified using the network. We investigate the accuracy of these filters in synthetic and real-world data sets, and find that they can substantially reduce noise of different levels and structure. By denoising large-scale biological data sets, network filters have the potential to improve the analysis of many types of biological data.