Partition Quantitative Assessment (PQA): A quantitative methodology to assess the embedded noise in clustered omics and systems biology data

TL;DR

This paper introduces a new quantitative methodology based on autocorrelation to assess the embedded noise in clustered omics and systems biology data, addressing a gap in statistical evaluation of clustering results.

Contribution

The paper presents a novel autocorrelation-based method to quantify noise in clustered data, providing a statistical measure of the randomness in clustering outcomes.

Findings

Method effectively quantifies noise in clustered data.

Provides a statistical measure for evaluating clustering quality.

Applicable to omics and systems biology datasets.

Abstract

Identifying groups that share common features among datasets through clustering analysis is a typical problem in many fields of science, particularly in post-omics and systems biology research. In respect of this, quantifying how a measure can cluster or organize intrinsic groups is important since currently there is no statistical evaluation of how ordered is, or how much noise is embedded in the resulting clustered vector. Many of the literature focuses on how well the clustering algorithm orders the data, with several measures regarding external and internal statistical measures; but none measure has been developed to statistically quantify the noise in an arranged vector posterior a clustering algorithm, i.e., how much of the clustering is due to randomness. Here, we present a quantitative methodology, based on autocorrelation, to assess this problem.