Statistical Methods for Estimating Complexity from Competition Experiments between Two Populations

TL;DR

This paper introduces a statistical method to estimate the complexity of molecular target sets from competition experiments, enabling high-throughput analysis of ligand-target interactions.

Contribution

It presents a novel nonparametric statistical approach for estimating target complexity from competition data, applicable to high-throughput ligand screening.

Findings

Robust estimation of target complexity from simulated competition matrices

Feasibility of high-throughput implementation

Potential applicability to various ligand binding scenarios

Abstract

Often a screening or selection experiment targets a cell or tissue, which presents many possible molecular targets and identifies a correspondingly large number of ligands. We describe a statistical method to extract an estimate of the complexity or richness of the set of molecular targets from competition experiments between distinguishable ligands, including aptamers derived from combinatorial experiments (SELEX or phage display). In simulations, the nonparametric statistic provides a robust estimate of complexity from a 100x100 matrix of competition experiments, which is clearly feasible in high-throughput format. The statistic and method are potentially applicable to other ligand binding situations.