A Novel Calibration Step in Gene Co-Expression Network Construction

High-throughput technologies such as DNA microarrays and RNA-sequencing are used to measure the expression levels of large numbers of genes simultaneously. To support the extraction of biological knowledge, individual gene expression levels are transformed to Gene Co-expression Networks (GCNs). In a...

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Autores principales:	Niloofar Aghaieabiane, Ioannis Koutis
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	Gene co-expression networks Similarity function Clustering Gene Ontology Topological Overlap Measure Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/590b63cca351477fad86afbcecf91498
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id	oai:doaj.org-article:590b63cca351477fad86afbcecf91498
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spelling	oai:doaj.org-article:590b63cca351477fad86afbcecf914982021-11-30T14:07:49ZA Novel Calibration Step in Gene Co-Expression Network Construction2673-764710.3389/fbinf.2021.704817https://doaj.org/article/590b63cca351477fad86afbcecf914982021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fbinf.2021.704817/fullhttps://doaj.org/toc/2673-7647High-throughput technologies such as DNA microarrays and RNA-sequencing are used to measure the expression levels of large numbers of genes simultaneously. To support the extraction of biological knowledge, individual gene expression levels are transformed to Gene Co-expression Networks (GCNs). In a GCN, nodes correspond to genes, and the weight of the connection between two nodes is a measure of similarity in the expression behavior of the two genes. In general, GCN construction and analysis includes three steps; 1) calculating a similarity value for each pair of genes 2) using these similarity values to construct a fully connected weighted network 3) finding clusters of genes in the network, commonly called modules. The specific implementation of these three steps can significantly impact the final output and the downstream biological analysis. GCN construction is a well-studied topic. Existing algorithms rely on relatively simple statistical and mathematical tools to implement these steps. Currently, software package WGCNA appears to be the most widely accepted standard. We hypothesize that the raw features provided by sequencing data can be leveraged to extract modules of higher quality. A novel preprocessing step of the gene expression data set is introduced that in effect calibrates the expression levels of individual genes, before computing pairwise similarities. Further, the similarity is computed as an inner-product of positive vectors. In experiments, this provides a significant improvement over WGCNA, as measured by aggregate p-values of the gene ontology term enrichment of the computed modules.Niloofar AghaieabianeIoannis KoutisFrontiers Media S.A.articleGene co-expression networksSimilarity functionClusteringGene OntologyTopological Overlap MeasureComputer applications to medicine. Medical informaticsR858-859.7ENFrontiers in Bioinformatics, Vol 1 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Gene co-expression networks Similarity function Clustering Gene Ontology Topological Overlap Measure Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Gene co-expression networks Similarity function Clustering Gene Ontology Topological Overlap Measure Computer applications to medicine. Medical informatics R858-859.7 Niloofar Aghaieabiane Ioannis Koutis A Novel Calibration Step in Gene Co-Expression Network Construction
description	High-throughput technologies such as DNA microarrays and RNA-sequencing are used to measure the expression levels of large numbers of genes simultaneously. To support the extraction of biological knowledge, individual gene expression levels are transformed to Gene Co-expression Networks (GCNs). In a GCN, nodes correspond to genes, and the weight of the connection between two nodes is a measure of similarity in the expression behavior of the two genes. In general, GCN construction and analysis includes three steps; 1) calculating a similarity value for each pair of genes 2) using these similarity values to construct a fully connected weighted network 3) finding clusters of genes in the network, commonly called modules. The specific implementation of these three steps can significantly impact the final output and the downstream biological analysis. GCN construction is a well-studied topic. Existing algorithms rely on relatively simple statistical and mathematical tools to implement these steps. Currently, software package WGCNA appears to be the most widely accepted standard. We hypothesize that the raw features provided by sequencing data can be leveraged to extract modules of higher quality. A novel preprocessing step of the gene expression data set is introduced that in effect calibrates the expression levels of individual genes, before computing pairwise similarities. Further, the similarity is computed as an inner-product of positive vectors. In experiments, this provides a significant improvement over WGCNA, as measured by aggregate p-values of the gene ontology term enrichment of the computed modules.
format	article
author	Niloofar Aghaieabiane Ioannis Koutis
author_facet	Niloofar Aghaieabiane Ioannis Koutis
author_sort	Niloofar Aghaieabiane
title	A Novel Calibration Step in Gene Co-Expression Network Construction
title_short	A Novel Calibration Step in Gene Co-Expression Network Construction
title_full	A Novel Calibration Step in Gene Co-Expression Network Construction
title_fullStr	A Novel Calibration Step in Gene Co-Expression Network Construction
title_full_unstemmed	A Novel Calibration Step in Gene Co-Expression Network Construction
title_sort	novel calibration step in gene co-expression network construction
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/590b63cca351477fad86afbcecf91498
work_keys_str_mv	AT niloofaraghaieabiane anovelcalibrationstepingenecoexpressionnetworkconstruction AT ioanniskoutis anovelcalibrationstepingenecoexpressionnetworkconstruction AT niloofaraghaieabiane novelcalibrationstepingenecoexpressionnetworkconstruction AT ioanniskoutis novelcalibrationstepingenecoexpressionnetworkconstruction
_version_	1718406504745271296

A Novel Calibration Step in Gene Co-Expression Network Construction

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