Turning straw into gold : building robustness into gene signature inference
Reproducible and generalizable gene signatures are essential for clinical deployment, but are hard to come by. The primary issue is insufficient mitigation of confounders: ensuring that hypotheses are appropriate, test statistics and null distributions are appropriate, and so on. To further improve...
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| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/137542 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Reproducible and generalizable gene signatures are essential for clinical deployment, but are hard to come by. The primary issue is insufficient mitigation of confounders: ensuring that hypotheses are appropriate, test statistics and null distributions are appropriate, and so on. To further improve robustness, additional good analytical practices (GAPs) are needed, namely: leveraging existing data and knowledge; careful and systematic evaluation of gene sets, even if they overlap with known sources of confounding; and rigorous testing of inferred signatures against as many published data sets as possible. Here, using a re-examination of a breast cancer data set and 48 published signatures, we illustrate the value of adopting these GAPs. |
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