Evaluating adaptive stress response gene signatures using transcriptomics- Data for Figures 1-4
The construction of SRP consensus signatures sets was completed in three steps. First, we constructed consensus signatures by merging and pruning relevant gene sets from the MSigDB v7.1. Second, we developed an independent gene expression validation set by identifying reference perturbagens from the literature and curating their transcriptomic profiles from publicly available sources. Third, we used gene set enrichment analysis (GSEA) to score matches between signatures and transcriptomic profiles. Lastly, we evaluated the performance of GSEA scores as classifiers of SRP activity within reference perturbagen transcriptomic profiles using ROC AUC analysis.
This dataset is associated with the following publication:
Chambers, B., and I. Shah. Evaluating adaptive stress response gene signatures using transcriptomics. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 20: 100179, (2021).
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supplemental-materials-tables-v4.21.xlsx
APPLICATION/VND.OPENXMLFORMATS-OFFICEDOCUMENT.SPREADSHEETML.SHEET
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"020:00"
]
|
| contactPoint |
{
"fn": "Imran Shah",
"hasEmail": "mailto:shah.imran@epa.gov"
}
|
| description | The construction of SRP consensus signatures sets was completed in three steps. First, we constructed consensus signatures by merging and pruning relevant gene sets from the MSigDB v7.1. Second, we developed an independent gene expression validation set by identifying reference perturbagens from the literature and curating their transcriptomic profiles from publicly available sources. Third, we used gene set enrichment analysis (GSEA) to score matches between signatures and transcriptomic profiles. Lastly, we evaluated the performance of GSEA scores as classifiers of SRP activity within reference perturbagen transcriptomic profiles using ROC AUC analysis. This dataset is associated with the following publication: Chambers, B., and I. Shah. Evaluating adaptive stress response gene signatures using transcriptomics. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 20: 100179, (2021). |
| distribution |
[
{
"title": "supplemental-materials-tables-v4.21.xlsx",
"mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
"downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1523149/supplemental-materials-tables-v4.21.xlsx"
}
]
|
| identifier | https://doi.org/10.23719/1523149 |
| keyword |
[
"computational toxicology",
"gene signatures",
"receiver operating characteristic",
"stress response pathways",
"transcriptomics"
]
|
| license | https://pasteur.epa.gov/license/sciencehub-license.html |
| modified | 2021-09-22 |
| programCode |
[
"020:000"
]
|
| publisher |
{
"name": "U.S. EPA Office of Research and Development (ORD)",
"subOrganizationOf": {
"name": "U.S. Environmental Protection Agency",
"subOrganizationOf": {
"name": "U.S. Government"
}
}
}
|
| references |
[
"https://doi.org/10.1016/j.comtox.2021.100179"
]
|
| rights |
null
|
| title | Evaluating adaptive stress response gene signatures using transcriptomics- Data for Figures 1-4 |
