Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Partition Quantitative Assessment (Pqa): A Quantitative Methodology to Assess the Embedded Noise in Clustered Omics and Systems Biology Data

Version 1 : Received: 28 December 2020 / Approved: 29 December 2020 / Online: 29 December 2020 (14:02:28 CET)

A peer-reviewed article of this Preprint also exists.

Camacho-Hernández, D.A.; Nieto-Caballero, V.E.; León-Burguete, J.E.; Freyre-González, J.A. Partition Quantitative Assessment (PQA): A Quantitative Methodology to Assess the Embedded Noise in Clustered Omics and Systems Biology Data. Appl. Sci. 2021, 11, 5999. Camacho-Hernández, D.A.; Nieto-Caballero, V.E.; León-Burguete, J.E.; Freyre-González, J.A. Partition Quantitative Assessment (PQA): A Quantitative Methodology to Assess the Embedded Noise in Clustered Omics and Systems Biology Data. Appl. Sci. 2021, 11, 5999.

Journal reference: Appl. Sci. 2021, 11, 5999
DOI: 10.3390/app11135999

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.

Subject Areas

omics data; hierarchical clustering; noise quantification

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