Learning Dysregulated Pathways in Cancers from Differential Variability Analysis
Analysis of gene sets can implicate activity in signaling pathways that is responsible for cancer initiation and progression, but is not discernible from the analysis of individual genes. Multiple methods and software packages have been developed to infer pathway activity from expression measurement...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2014-01-01
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| Series: | Cancer Informatics |
| Online Access: | https://doi.org/10.4137/CIN.S14066 |
| _version_ | 1811198936465014784 |
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| author | Bahman Afsari Donald German Elana J. Fertig |
| author_facet | Bahman Afsari Donald German Elana J. Fertig |
| author_sort | Bahman Afsari |
| collection | DOAJ |
| description | Analysis of gene sets can implicate activity in signaling pathways that is responsible for cancer initiation and progression, but is not discernible from the analysis of individual genes. Multiple methods and software packages have been developed to infer pathway activity from expression measurements for set of genes targeted by that pathway. Broadly, three major methodologies have been proposed: over-representation, enrichment, and differential variability. Both over-representation and enrichment analyses are effective techniques to infer differentially regulated pathways from gene sets with relatively consistent differentially expressed (DE) genes. Specifically, these algorithms aggregate statistics from each gene in the pathway. However, they overlook multivariate patterns related to gene interactions and variations in expression. Therefore, the analysis of differential variability of multigene expression patterns can be essential to pathway inference in cancers. The corresponding methodologies and software packages for such multivariate variability analysis of pathways are reviewed here. We also introduce a new, computationally efficient algorithm, expression variation analysis (EVA), which has been implemented along with a previously proposed algorithm, Differential Rank Conservation (DIRAC), in an open source R package, gene set regulation (GSReg). EVA inferred similar pathways as DIRAC at reduced computational costs. Moreover, EVA also inferred different dysregulated pathways than those identified by enrichment analysis. |
| first_indexed | 2024-04-12T01:39:37Z |
| format | Article |
| id | doaj.art-718b9107f28043c6a5a3552e8684c5eb |
| institution | Directory Open Access Journal |
| issn | 1176-9351 |
| language | English |
| last_indexed | 2024-04-12T01:39:37Z |
| publishDate | 2014-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Cancer Informatics |
| spelling | doaj.art-718b9107f28043c6a5a3552e8684c5eb2022-12-22T03:53:14ZengSAGE PublishingCancer Informatics1176-93512014-01-0113s510.4137/CIN.S14066Learning Dysregulated Pathways in Cancers from Differential Variability AnalysisBahman Afsari0Donald German1Elana J. Fertig2Postdoctoral Fellow, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA.Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA.Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA.Analysis of gene sets can implicate activity in signaling pathways that is responsible for cancer initiation and progression, but is not discernible from the analysis of individual genes. Multiple methods and software packages have been developed to infer pathway activity from expression measurements for set of genes targeted by that pathway. Broadly, three major methodologies have been proposed: over-representation, enrichment, and differential variability. Both over-representation and enrichment analyses are effective techniques to infer differentially regulated pathways from gene sets with relatively consistent differentially expressed (DE) genes. Specifically, these algorithms aggregate statistics from each gene in the pathway. However, they overlook multivariate patterns related to gene interactions and variations in expression. Therefore, the analysis of differential variability of multigene expression patterns can be essential to pathway inference in cancers. The corresponding methodologies and software packages for such multivariate variability analysis of pathways are reviewed here. We also introduce a new, computationally efficient algorithm, expression variation analysis (EVA), which has been implemented along with a previously proposed algorithm, Differential Rank Conservation (DIRAC), in an open source R package, gene set regulation (GSReg). EVA inferred similar pathways as DIRAC at reduced computational costs. Moreover, EVA also inferred different dysregulated pathways than those identified by enrichment analysis.https://doi.org/10.4137/CIN.S14066 |
| spellingShingle | Bahman Afsari Donald German Elana J. Fertig Learning Dysregulated Pathways in Cancers from Differential Variability Analysis Cancer Informatics |
| title | Learning Dysregulated Pathways in Cancers from Differential Variability Analysis |
| title_full | Learning Dysregulated Pathways in Cancers from Differential Variability Analysis |
| title_fullStr | Learning Dysregulated Pathways in Cancers from Differential Variability Analysis |
| title_full_unstemmed | Learning Dysregulated Pathways in Cancers from Differential Variability Analysis |
| title_short | Learning Dysregulated Pathways in Cancers from Differential Variability Analysis |
| title_sort | learning dysregulated pathways in cancers from differential variability analysis |
| url | https://doi.org/10.4137/CIN.S14066 |
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