Testing for dependence on tree structures
Proceedings of the National Academy of Sciences of the United States of America, ISSN: 1091-6490, Vol: 117, Issue: 18, Page: 9787-9792
2020
- 13Citations
- 47Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations13
- Citation Indexes13
- 13
- CrossRef7
- Captures47
- Readers47
- 47
Article Description
Tree structures, showing hierarchical relationships and the latent structures between samples, are ubiquitous in genomic and biomedical sciences. A common question in many studies is whether there is an association between a response variable measured on each sample and the latent group structure represented by some given tree. Currently, this is addressed on an ad hoc basis, usually requiring the user to decide on an appropriate number of clusters to prune out of the tree to be tested against the response variable. Here, we present a statistical method with statistical guarantees that tests for association between the response variable and a fixed tree structure across all levels of the tree hierarchy with high power while accounting for the overall false positive error rate. This enhances the robustness and reproducibility of such findings.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85084311742&origin=inward; http://dx.doi.org/10.1073/pnas.1912957117; http://www.ncbi.nlm.nih.gov/pubmed/32321827; https://pnas.org/doi/full/10.1073/pnas.1912957117; https://dx.doi.org/10.1073/pnas.1912957117; https://www.pnas.org/content/117/18/9787
Proceedings of the National Academy of Sciences
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