Next Article in Journal
Unveiling Biological Activities of Marine Fungi: The Effect of Sea Salt
Previous Article in Journal
MenuNER: Domain-Adapted BERT Based NER Approach for a Domain with Limited Dataset and Its Application to Food Menu Domain
Previous Article in Special Issue
A Comparative Cross-Platform Meta-Analysis to Identify Potential Biomarker Genes Common to Endometriosis and Recurrent Pregnancy Loss
Article

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

1
Regulatory Systems Biology Research Group, Center for Genomic Sciences, Laboratory of Systems and Synthetic Biology, Universidad Nacional Autónoma de México (UNAM), Morelos 62210, Mexico
2
Undergraduate Program in Genomic Sciences, Center for Genomic Sciences, Universidad Nacional Autónoma de México (UNAM), Morelos 62210, Mexico
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2021, 11(13), 5999; https://doi.org/10.3390/app11135999
Received: 28 December 2020 / Revised: 8 January 2021 / Accepted: 10 January 2021 / Published: 28 June 2021
(This article belongs to the Special Issue Towards a Systems Biology Approach)
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. Much of the literature focuses on how well the clustering algorithm orders the data, with several measures regarding external and internal statistical validation; but no score has been developed to quantify statistically the noise in an arranged vector posterior to a clustering algorithm, i.e., how much of the clustering is due to randomness. Here, we present a quantitative methodology, based on autocorrelation, in order to assess this problem. View Full-Text
Keywords: omics data; data clustering; noise quantification omics data; data clustering; noise quantification
Show Figures

Figure 1

MDPI and ACS Style

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. https://doi.org/10.3390/app11135999

AMA Style

Camacho-Hernández DA, Nieto-Caballero VE, León-Burguete JE, Freyre-González JA. Partition Quantitative Assessment (PQA): A Quantitative Methodology to Assess the Embedded Noise in Clustered Omics and Systems Biology Data. Applied Sciences. 2021; 11(13):5999. https://doi.org/10.3390/app11135999

Chicago/Turabian Style

Camacho-Hernández, Diego A., Victor E. Nieto-Caballero, José E. León-Burguete, and Julio A. Freyre-González 2021. "Partition Quantitative Assessment (PQA): A Quantitative Methodology to Assess the Embedded Noise in Clustered Omics and Systems Biology Data" Applied Sciences 11, no. 13: 5999. https://doi.org/10.3390/app11135999

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop