SLEPR: a sample-level enrichment-based pathway ranking method -- seeking biological themes through pathway-level consistency.
Analysis of microarray and other high throughput data often involves identification of genes consistently up or down-regulated across samples as the first step in extraction of biological meaning. This gene-level paradigm can be limited as a result of valid sample fluctuations and biological complex...
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Public Library of Science (PLoS)
2008-09-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0003288&type=printable |
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| _version_ | 1850107861378531328 |
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| author | Ming Yi Robert M Stephens |
| author_facet | Ming Yi Robert M Stephens |
| author_sort | Ming Yi |
| collection | DOAJ |
| description | Analysis of microarray and other high throughput data often involves identification of genes consistently up or down-regulated across samples as the first step in extraction of biological meaning. This gene-level paradigm can be limited as a result of valid sample fluctuations and biological complexities. In this report, we describe a novel method, SLEPR, which eliminates this limitation by relying on pathway-level consistencies. Our method first selects the sample-level differentiated genes from each individual sample, capturing genes missed by other analysis methods, ascertains the enrichment levels of associated pathways from each of those lists, and then ranks annotated pathways based on the consistency of enrichment levels of individual samples from both sample classes. As a proof of concept, we have used this method to analyze three public microarray datasets with a direct comparison with the GSEA method, one of the most popular pathway-level analysis methods in the field. We found that our method was able to reproduce the earlier observations with significant improvements in depth of coverage for validated or expected biological themes, but also produced additional insights that make biological sense. This new method extends existing analyses approaches and facilitates integration of different types of HTP data. |
| format | Article |
| id | doaj-art-c9e4d17ada454c479a4e34c500a9e3cc |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2008-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-c9e4d17ada454c479a4e34c500a9e3cc2025-08-20T02:38:29ZengPublic Library of Science (PLoS)PLoS ONE1932-62032008-09-0139e328810.1371/journal.pone.0003288SLEPR: a sample-level enrichment-based pathway ranking method -- seeking biological themes through pathway-level consistency.Ming YiRobert M StephensAnalysis of microarray and other high throughput data often involves identification of genes consistently up or down-regulated across samples as the first step in extraction of biological meaning. This gene-level paradigm can be limited as a result of valid sample fluctuations and biological complexities. In this report, we describe a novel method, SLEPR, which eliminates this limitation by relying on pathway-level consistencies. Our method first selects the sample-level differentiated genes from each individual sample, capturing genes missed by other analysis methods, ascertains the enrichment levels of associated pathways from each of those lists, and then ranks annotated pathways based on the consistency of enrichment levels of individual samples from both sample classes. As a proof of concept, we have used this method to analyze three public microarray datasets with a direct comparison with the GSEA method, one of the most popular pathway-level analysis methods in the field. We found that our method was able to reproduce the earlier observations with significant improvements in depth of coverage for validated or expected biological themes, but also produced additional insights that make biological sense. This new method extends existing analyses approaches and facilitates integration of different types of HTP data.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0003288&type=printable |
| spellingShingle | Ming Yi Robert M Stephens SLEPR: a sample-level enrichment-based pathway ranking method -- seeking biological themes through pathway-level consistency. PLoS ONE |
| title | SLEPR: a sample-level enrichment-based pathway ranking method -- seeking biological themes through pathway-level consistency. |
| title_full | SLEPR: a sample-level enrichment-based pathway ranking method -- seeking biological themes through pathway-level consistency. |
| title_fullStr | SLEPR: a sample-level enrichment-based pathway ranking method -- seeking biological themes through pathway-level consistency. |
| title_full_unstemmed | SLEPR: a sample-level enrichment-based pathway ranking method -- seeking biological themes through pathway-level consistency. |
| title_short | SLEPR: a sample-level enrichment-based pathway ranking method -- seeking biological themes through pathway-level consistency. |
| title_sort | slepr a sample level enrichment based pathway ranking method seeking biological themes through pathway level consistency |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0003288&type=printable |
| work_keys_str_mv | AT mingyi sleprasamplelevelenrichmentbasedpathwayrankingmethodseekingbiologicalthemesthroughpathwaylevelconsistency AT robertmstephens sleprasamplelevelenrichmentbasedpathwayrankingmethodseekingbiologicalthemesthroughpathwaylevelconsistency |