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Characterizing Human Cell Types and Tissue Origin Using the Benford Law
Processing massive transcriptomic datasets in a meaningful manner requires novel, possibly interdisciplinary, approaches. One principle that can address this challenge is the Benford law (BL), which posits that the occurrence probability of a leading digit in a large numerical dataset decreases as i...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6770594/ https://www.ncbi.nlm.nih.gov/pubmed/31470662 http://dx.doi.org/10.3390/cells8091004 |
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author | Morag, Sne Salmon-Divon, Mali |
author_facet | Morag, Sne Salmon-Divon, Mali |
author_sort | Morag, Sne |
collection | PubMed |
description | Processing massive transcriptomic datasets in a meaningful manner requires novel, possibly interdisciplinary, approaches. One principle that can address this challenge is the Benford law (BL), which posits that the occurrence probability of a leading digit in a large numerical dataset decreases as its value increases. Here, we analyzed large single-cell and bulk RNA-seq datasets to test whether cell types and tissue origins can be differentiated based on the adherence of specific genes to the BL. Then, we used the Benford adherence scores of these genes as inputs to machine-learning algorithms and tested their separation accuracy. We found that genes selected based on their first-digit distributions can distinguish between cell types and tissue origins. Moreover, despite the simplicity of this novel feature-selection method, its separation accuracy is higher than that of the mean-expression level approach and is similar to that of the differential expression approach. Thus, the BL can be used to obtain biological insights from massive amounts of numerical genomics data—a capability that could be utilized in various biomedical applications, e.g., to resolve samples of unknown primary origin, identify possible sample contaminations, and provide insights into the molecular basis of cancer subtypes. |
format | Online Article Text |
id | pubmed-6770594 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-67705942019-10-30 Characterizing Human Cell Types and Tissue Origin Using the Benford Law Morag, Sne Salmon-Divon, Mali Cells Article Processing massive transcriptomic datasets in a meaningful manner requires novel, possibly interdisciplinary, approaches. One principle that can address this challenge is the Benford law (BL), which posits that the occurrence probability of a leading digit in a large numerical dataset decreases as its value increases. Here, we analyzed large single-cell and bulk RNA-seq datasets to test whether cell types and tissue origins can be differentiated based on the adherence of specific genes to the BL. Then, we used the Benford adherence scores of these genes as inputs to machine-learning algorithms and tested their separation accuracy. We found that genes selected based on their first-digit distributions can distinguish between cell types and tissue origins. Moreover, despite the simplicity of this novel feature-selection method, its separation accuracy is higher than that of the mean-expression level approach and is similar to that of the differential expression approach. Thus, the BL can be used to obtain biological insights from massive amounts of numerical genomics data—a capability that could be utilized in various biomedical applications, e.g., to resolve samples of unknown primary origin, identify possible sample contaminations, and provide insights into the molecular basis of cancer subtypes. MDPI 2019-08-29 /pmc/articles/PMC6770594/ /pubmed/31470662 http://dx.doi.org/10.3390/cells8091004 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Morag, Sne Salmon-Divon, Mali Characterizing Human Cell Types and Tissue Origin Using the Benford Law |
title | Characterizing Human Cell Types and Tissue Origin Using the Benford Law |
title_full | Characterizing Human Cell Types and Tissue Origin Using the Benford Law |
title_fullStr | Characterizing Human Cell Types and Tissue Origin Using the Benford Law |
title_full_unstemmed | Characterizing Human Cell Types and Tissue Origin Using the Benford Law |
title_short | Characterizing Human Cell Types and Tissue Origin Using the Benford Law |
title_sort | characterizing human cell types and tissue origin using the benford law |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6770594/ https://www.ncbi.nlm.nih.gov/pubmed/31470662 http://dx.doi.org/10.3390/cells8091004 |
work_keys_str_mv | AT moragsne characterizinghumancelltypesandtissueoriginusingthebenfordlaw AT salmondivonmali characterizinghumancelltypesandtissueoriginusingthebenfordlaw |