Partition Quantitative Assessment (PQA): A Quantitative Methodology to Assess the Embedded Noise in Clustered Omics and Systems Biology Data
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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-06-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/13/5999 |
| _version_ | 1797411566913585152 |
|---|---|
| author | Diego A. Camacho-Hernández Victor E. Nieto-Caballero José E. León-Burguete Julio A. Freyre-González |
| author_facet | Diego A. Camacho-Hernández Victor E. Nieto-Caballero José E. León-Burguete Julio A. Freyre-González |
| author_sort | Diego A. Camacho-Hernández |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T04:48:00Z |
| format | Article |
| id | doaj.art-7c17c30370d2456e8d2233136ede33b4 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T04:48:00Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7c17c30370d2456e8d2233136ede33b42023-12-03T13:13:57ZengMDPI AGApplied Sciences2076-34172021-06-011113599910.3390/app11135999Partition Quantitative Assessment (PQA): A Quantitative Methodology to Assess the Embedded Noise in Clustered Omics and Systems Biology DataDiego A. Camacho-Hernández0Victor E. Nieto-Caballero1José E. León-Burguete2Julio A. Freyre-González3Regulatory Systems Biology Research Group, Center for Genomic Sciences, Laboratory of Systems and Synthetic Biology, Universidad Nacional Autónoma de México (UNAM), Morelos 62210, MexicoRegulatory Systems Biology Research Group, Center for Genomic Sciences, Laboratory of Systems and Synthetic Biology, Universidad Nacional Autónoma de México (UNAM), Morelos 62210, MexicoRegulatory Systems Biology Research Group, Center for Genomic Sciences, Laboratory of Systems and Synthetic Biology, Universidad Nacional Autónoma de México (UNAM), Morelos 62210, MexicoRegulatory Systems Biology Research Group, Center for Genomic Sciences, Laboratory of Systems and Synthetic Biology, Universidad Nacional Autónoma de México (UNAM), Morelos 62210, MexicoIdentifying 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.https://www.mdpi.com/2076-3417/11/13/5999omics datadata clusteringnoise quantification |
| spellingShingle | Diego A. Camacho-Hernández Victor E. Nieto-Caballero José E. León-Burguete Julio A. Freyre-González Partition Quantitative Assessment (PQA): A Quantitative Methodology to Assess the Embedded Noise in Clustered Omics and Systems Biology Data Applied Sciences omics data data clustering noise quantification |
| title | Partition Quantitative Assessment (PQA): A Quantitative Methodology to Assess the Embedded Noise in Clustered Omics and Systems Biology Data |
| title_full | Partition Quantitative Assessment (PQA): A Quantitative Methodology to Assess the Embedded Noise in Clustered Omics and Systems Biology Data |
| title_fullStr | Partition Quantitative Assessment (PQA): A Quantitative Methodology to Assess the Embedded Noise in Clustered Omics and Systems Biology Data |
| title_full_unstemmed | Partition Quantitative Assessment (PQA): A Quantitative Methodology to Assess the Embedded Noise in Clustered Omics and Systems Biology Data |
| title_short | Partition Quantitative Assessment (PQA): A Quantitative Methodology to Assess the Embedded Noise in Clustered Omics and Systems Biology Data |
| title_sort | partition quantitative assessment pqa a quantitative methodology to assess the embedded noise in clustered omics and systems biology data |
| topic | omics data data clustering noise quantification |
| url | https://www.mdpi.com/2076-3417/11/13/5999 |
| work_keys_str_mv | AT diegoacamachohernandez partitionquantitativeassessmentpqaaquantitativemethodologytoassesstheembeddednoiseinclusteredomicsandsystemsbiologydata AT victorenietocaballero partitionquantitativeassessmentpqaaquantitativemethodologytoassesstheembeddednoiseinclusteredomicsandsystemsbiologydata AT joseeleonburguete partitionquantitativeassessmentpqaaquantitativemethodologytoassesstheembeddednoiseinclusteredomicsandsystemsbiologydata AT julioafreyregonzalez partitionquantitativeassessmentpqaaquantitativemethodologytoassesstheembeddednoiseinclusteredomicsandsystemsbiologydata |