A Cyclic Permutation Approach to Removing Spatial Dependency between Clustered Gene Ontology Terms
Traditional gene set enrichment analysis falters when applied to large genomic domains, where neighboring genes often share functions. This spatial dependency creates misleading enrichments, mistaking mere physical proximity for genuine biological connections. Here we present Spatial Adjusted Gene O...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Biology |
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| Online Access: | https://www.mdpi.com/2079-7737/13/3/175 |
| _version_ | 1797242035895271424 |
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| author | Rachel Rapoport Avraham Greenberg Zohar Yakhini Itamar Simon |
| author_facet | Rachel Rapoport Avraham Greenberg Zohar Yakhini Itamar Simon |
| author_sort | Rachel Rapoport |
| collection | DOAJ |
| description | Traditional gene set enrichment analysis falters when applied to large genomic domains, where neighboring genes often share functions. This spatial dependency creates misleading enrichments, mistaking mere physical proximity for genuine biological connections. Here we present Spatial Adjusted Gene Ontology (SAGO), a novel cyclic permutation-based approach, to tackle this challenge. SAGO separates enrichments due to spatial proximity from genuine biological links by incorporating the genes’ spatial arrangement into the analysis. We applied SAGO to various datasets in which the identified genomic intervals are large, including replication timing domains, large H3K9me3 and H3K27me3 domains, HiC compartments and lamina-associated domains (LADs). Intriguingly, applying SAGO to prostate cancer samples with large copy number alteration (CNA) domains eliminated most of the enriched GO terms, thus helping to accurately identify biologically relevant gene sets linked to oncogenic processes, free from spatial bias. |
| first_indexed | 2024-04-24T18:32:49Z |
| format | Article |
| id | doaj.art-e7c22cc9f0aa45efb1c038df0991540d |
| institution | Directory Open Access Journal |
| issn | 2079-7737 |
| language | English |
| last_indexed | 2024-04-24T18:32:49Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biology |
| spelling | doaj.art-e7c22cc9f0aa45efb1c038df0991540d2024-03-27T13:22:12ZengMDPI AGBiology2079-77372024-03-0113317510.3390/biology13030175A Cyclic Permutation Approach to Removing Spatial Dependency between Clustered Gene Ontology TermsRachel Rapoport0Avraham Greenberg1Zohar Yakhini2Itamar Simon3Microbiology and Molecular Genetics, Hebrew University of Jerusalem-IMRIC, Jerusalem 9112102, IsraelMicrobiology and Molecular Genetics, Hebrew University of Jerusalem-IMRIC, Jerusalem 9112102, IsraelEfi Arazi School of Computer Science, Reichman University (IDC Herzliya), Herzliya 4610101, IsraelMicrobiology and Molecular Genetics, Hebrew University of Jerusalem-IMRIC, Jerusalem 9112102, IsraelTraditional gene set enrichment analysis falters when applied to large genomic domains, where neighboring genes often share functions. This spatial dependency creates misleading enrichments, mistaking mere physical proximity for genuine biological connections. Here we present Spatial Adjusted Gene Ontology (SAGO), a novel cyclic permutation-based approach, to tackle this challenge. SAGO separates enrichments due to spatial proximity from genuine biological links by incorporating the genes’ spatial arrangement into the analysis. We applied SAGO to various datasets in which the identified genomic intervals are large, including replication timing domains, large H3K9me3 and H3K27me3 domains, HiC compartments and lamina-associated domains (LADs). Intriguingly, applying SAGO to prostate cancer samples with large copy number alteration (CNA) domains eliminated most of the enriched GO terms, thus helping to accurately identify biologically relevant gene sets linked to oncogenic processes, free from spatial bias.https://www.mdpi.com/2079-7737/13/3/175gene set enrichment analysis (GSEA)GO annotationsspatial dependenciescyclic permutationreplication timingcopy number alterations (CNA) |
| spellingShingle | Rachel Rapoport Avraham Greenberg Zohar Yakhini Itamar Simon A Cyclic Permutation Approach to Removing Spatial Dependency between Clustered Gene Ontology Terms Biology gene set enrichment analysis (GSEA) GO annotations spatial dependencies cyclic permutation replication timing copy number alterations (CNA) |
| title | A Cyclic Permutation Approach to Removing Spatial Dependency between Clustered Gene Ontology Terms |
| title_full | A Cyclic Permutation Approach to Removing Spatial Dependency between Clustered Gene Ontology Terms |
| title_fullStr | A Cyclic Permutation Approach to Removing Spatial Dependency between Clustered Gene Ontology Terms |
| title_full_unstemmed | A Cyclic Permutation Approach to Removing Spatial Dependency between Clustered Gene Ontology Terms |
| title_short | A Cyclic Permutation Approach to Removing Spatial Dependency between Clustered Gene Ontology Terms |
| title_sort | cyclic permutation approach to removing spatial dependency between clustered gene ontology terms |
| topic | gene set enrichment analysis (GSEA) GO annotations spatial dependencies cyclic permutation replication timing copy number alterations (CNA) |
| url | https://www.mdpi.com/2079-7737/13/3/175 |
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