Enrichment on steps, not genes, improves inference of differentially expressed pathways.
Enrichment analysis is frequently used in combination with differential expression data to investigate potential commonalities amongst lists of genes and generate hypotheses for further experiments. However, current enrichment analysis approaches on pathways ignore the functional relationships betwe...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-03-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011968&type=printable |
| _version_ | 1797213353756590080 |
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| author | Nicholas Markarian Kimberly M Van Auken Dustin Ebert Paul W Sternberg |
| author_facet | Nicholas Markarian Kimberly M Van Auken Dustin Ebert Paul W Sternberg |
| author_sort | Nicholas Markarian |
| collection | DOAJ |
| description | Enrichment analysis is frequently used in combination with differential expression data to investigate potential commonalities amongst lists of genes and generate hypotheses for further experiments. However, current enrichment analysis approaches on pathways ignore the functional relationships between genes in a pathway, particularly OR logic that occurs when a set of proteins can each individually perform the same step in a pathway. As a result, these approaches miss pathways with large or multiple sets because of an inflation of pathway size (when measured as the total gene count) relative to the number of steps. We address this problem by enriching on step-enabling entities in pathways. We treat sets of protein-coding genes as single entities, and we also weight sets to account for the number of genes in them using the multivariate Fisher's noncentral hypergeometric distribution. We then show three examples of pathways that are recovered with this method and find that the results have significant proportions of pathways not found in gene list enrichment analysis. |
| first_indexed | 2024-04-24T10:56:56Z |
| format | Article |
| id | doaj.art-f1c06ea0669445a09176de08abfe1869 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-24T10:56:56Z |
| publishDate | 2024-03-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-f1c06ea0669445a09176de08abfe18692024-04-12T05:30:51ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-03-01203e101196810.1371/journal.pcbi.1011968Enrichment on steps, not genes, improves inference of differentially expressed pathways.Nicholas MarkarianKimberly M Van AukenDustin EbertPaul W SternbergEnrichment analysis is frequently used in combination with differential expression data to investigate potential commonalities amongst lists of genes and generate hypotheses for further experiments. However, current enrichment analysis approaches on pathways ignore the functional relationships between genes in a pathway, particularly OR logic that occurs when a set of proteins can each individually perform the same step in a pathway. As a result, these approaches miss pathways with large or multiple sets because of an inflation of pathway size (when measured as the total gene count) relative to the number of steps. We address this problem by enriching on step-enabling entities in pathways. We treat sets of protein-coding genes as single entities, and we also weight sets to account for the number of genes in them using the multivariate Fisher's noncentral hypergeometric distribution. We then show three examples of pathways that are recovered with this method and find that the results have significant proportions of pathways not found in gene list enrichment analysis.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011968&type=printable |
| spellingShingle | Nicholas Markarian Kimberly M Van Auken Dustin Ebert Paul W Sternberg Enrichment on steps, not genes, improves inference of differentially expressed pathways. PLoS Computational Biology |
| title | Enrichment on steps, not genes, improves inference of differentially expressed pathways. |
| title_full | Enrichment on steps, not genes, improves inference of differentially expressed pathways. |
| title_fullStr | Enrichment on steps, not genes, improves inference of differentially expressed pathways. |
| title_full_unstemmed | Enrichment on steps, not genes, improves inference of differentially expressed pathways. |
| title_short | Enrichment on steps, not genes, improves inference of differentially expressed pathways. |
| title_sort | enrichment on steps not genes improves inference of differentially expressed pathways |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011968&type=printable |
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