Using Fano factors to determine certain types of gene autoregulation

TL;DR

This paper introduces a simple, robust method using Fano factors to infer gene autoregulation from expression data, requiring only non-interventional data and minimal assumptions, with applications to experimental datasets.

Contribution

It generalizes noise-based inference of autoregulation using Markov chain propositions, providing a practical tool that needs no parameter estimation or intervention.

Findings

Identified potential autoregulated genes in experimental data

Validated some inferred autoregulations with existing experiments

Proposed a model-agnostic, data-efficient inference method

Abstract

The expression of one gene might be regulated by its corresponding protein, which is called autoregulation. Although gene regulation is a central topic in biology, autoregulation is much less studied. In general, it is extremely difficult to determine the existence of autoregulation with direct biochemical approaches. Nevertheless, some papers have observed that certain types of autoregulations are linked to noise levels in gene expression. We generalize these results by two propositions on discrete-state continuous-time Markov chains. These two propositions form a simple but robust method to infer the existence of autoregulation in certain scenarios from gene expression data. This method only depends on the Fano factor, namely the ratio of variance and mean of the gene expression level. Compared to other methods for inferring autoregulation, our method only requires non-interventional one-time data, and does not need to estimate parameters. Besides, our method has few restrictions on the model. We apply this method to four groups of experimental data and find some genes that might have autoregulation. Some inferred autoregulations have been verified by experiments or other theoretical works.