Noise and nonlinearities in high-throughput data

TL;DR

This paper explores how non-linearities and noise affect high-throughput data analysis, demonstrating that simple non-parametric Bayesian models can effectively investigate these factors in various datasets.

Contribution

It introduces a non-parametric Bayesian approach to analyze the impact of non-linearities and noise in high-throughput data, highlighting its utility over complex models.

Findings

Non-linearities and noise significantly influence data interpretation.

Simple Bayesian models can effectively explore non-linear effects.

Linear models may be preferable for certain data qualities.

Abstract

High-throughput data analyses are becoming common in biology, communications, economics and sociology. The vast amounts of data are usually represented in the form of matrices and can be considered as knowledge networks. Spectra-based approaches have proved useful in extracting hidden information within such networks and for estimating missing data, but these methods are based essentially on linear assumptions. The physical models of matching, when applicable, often suggest non-linear mechanisms, that may sometimes be identified as noise. The use of non-linear models in data analysis, however, may require the introduction of many parameters, which lowers the statistical weight of the model. According to the quality of data, a simpler linear analysis may be more convenient than more complex approaches. In this paper, we show how a simple non-parametric Bayesian model may be used to explore the role of non-linearities and noise in synthetic and experimental data sets.