Improved identification of concordant and discordant gene expression signatures using an updated rank-rank hypergeometric overlap approach
Abstract Recent advances in large-scale gene expression profiling necessitate concurrent development of biostatistical approaches to reveal meaningful biological relationships. Most analyses rely on significance thresholds for identifying differentially expressed genes. We use an approach to compare...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2018-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-018-27903-2 |
| _version_ | 1818345458572984320 |
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| author | Kelly M. Cahill Zhiguang Huo George C. Tseng Ryan W. Logan Marianne L. Seney |
| author_facet | Kelly M. Cahill Zhiguang Huo George C. Tseng Ryan W. Logan Marianne L. Seney |
| author_sort | Kelly M. Cahill |
| collection | DOAJ |
| description | Abstract Recent advances in large-scale gene expression profiling necessitate concurrent development of biostatistical approaches to reveal meaningful biological relationships. Most analyses rely on significance thresholds for identifying differentially expressed genes. We use an approach to compare gene expression datasets using ‘threshold-free’ comparisons. Significance cut-offs to identify genes shared between datasets may be too stringent and may miss concordant patterns of gene expression with potential biological relevance. A threshold-free approach gaining popularity in several research areas, including neuroscience, is Rank–Rank Hypergeometric Overlap (RRHO). Genes are ranked by their p-value and effect size direction, and ranked lists are compared to identify significantly overlapping genes across a continuous significance gradient rather than at a single arbitrary cut-off. We have updated the previous RRHO analysis by accurately detecting overlap of genes changed in the same and opposite directions between two datasets. Here, we use simulated and real data to show the drawbacks of the previous algorithm as well as the utility of our new algorithm. For example, we show the power of detecting discordant transcriptional patterns in the postmortem brain of subjects with psychiatric disorders. The new R package, RRHO2, offers a new, more intuitive visualization of concordant and discordant gene overlap. |
| first_indexed | 2024-12-13T17:02:42Z |
| format | Article |
| id | doaj.art-ea08d28b830a44899508fdecbeb39c8d |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-13T17:02:42Z |
| publishDate | 2018-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-ea08d28b830a44899508fdecbeb39c8d2022-12-21T23:37:45ZengNature PortfolioScientific Reports2045-23222018-06-018111110.1038/s41598-018-27903-2Improved identification of concordant and discordant gene expression signatures using an updated rank-rank hypergeometric overlap approachKelly M. Cahill0Zhiguang Huo1George C. Tseng2Ryan W. Logan3Marianne L. Seney4Department of Biostatistics, Graduate School of Public Health, University of PittsburghDepartment of Biostatistics, Graduate School of Public Health, University of PittsburghDepartment of Biostatistics, Graduate School of Public Health, University of PittsburghDepartment of Psychiatry, School of Medicine, University of PittsburghDepartment of Psychiatry, School of Medicine, University of PittsburghAbstract Recent advances in large-scale gene expression profiling necessitate concurrent development of biostatistical approaches to reveal meaningful biological relationships. Most analyses rely on significance thresholds for identifying differentially expressed genes. We use an approach to compare gene expression datasets using ‘threshold-free’ comparisons. Significance cut-offs to identify genes shared between datasets may be too stringent and may miss concordant patterns of gene expression with potential biological relevance. A threshold-free approach gaining popularity in several research areas, including neuroscience, is Rank–Rank Hypergeometric Overlap (RRHO). Genes are ranked by their p-value and effect size direction, and ranked lists are compared to identify significantly overlapping genes across a continuous significance gradient rather than at a single arbitrary cut-off. We have updated the previous RRHO analysis by accurately detecting overlap of genes changed in the same and opposite directions between two datasets. Here, we use simulated and real data to show the drawbacks of the previous algorithm as well as the utility of our new algorithm. For example, we show the power of detecting discordant transcriptional patterns in the postmortem brain of subjects with psychiatric disorders. The new R package, RRHO2, offers a new, more intuitive visualization of concordant and discordant gene overlap.https://doi.org/10.1038/s41598-018-27903-2 |
| spellingShingle | Kelly M. Cahill Zhiguang Huo George C. Tseng Ryan W. Logan Marianne L. Seney Improved identification of concordant and discordant gene expression signatures using an updated rank-rank hypergeometric overlap approach Scientific Reports |
| title | Improved identification of concordant and discordant gene expression signatures using an updated rank-rank hypergeometric overlap approach |
| title_full | Improved identification of concordant and discordant gene expression signatures using an updated rank-rank hypergeometric overlap approach |
| title_fullStr | Improved identification of concordant and discordant gene expression signatures using an updated rank-rank hypergeometric overlap approach |
| title_full_unstemmed | Improved identification of concordant and discordant gene expression signatures using an updated rank-rank hypergeometric overlap approach |
| title_short | Improved identification of concordant and discordant gene expression signatures using an updated rank-rank hypergeometric overlap approach |
| title_sort | improved identification of concordant and discordant gene expression signatures using an updated rank rank hypergeometric overlap approach |
| url | https://doi.org/10.1038/s41598-018-27903-2 |
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