How to decide whether small samples comply with an equidistribution
| dc.creator | Poeschel, Thorsten | |
| dc.creator | Freund, Jan A. | |
| dc.date | 2002-05-10 | |
| dc.date.accessioned | 2026-07-07T02:45:25Z | |
| dc.date.available | 2026-07-07T02:45:25Z | |
| dc.description | The decision whether a measured distribution complies with an equidistribution is a central element of many biostatistical methods. High throughput differential expression measurements, for instance, necessitate to judge possible over-representation of genes. The reliability of this judgement, however, is strongly affected when rarely expressed genes are pooled. We propose a method that can be applied to frequency ranked distributions and that yields a simple but efficient criterion to assess the hypothesis of equiprobable expression levels. By applying our technique to surrogate data we exemplify how the decision criterion can differentiate between a true equidistribution and a triangular distribution. The distinction succeeds even for small sample sizes where standard tests of significance (e.g. chi^2) fail. Our method will have a major impact on several problems of computational biology where rare events baffle a reliable assessment of frequency distributions. | |
| dc.description | 7 pages, 8 figures | |
| dc.identifier | https://arxiv.org/abs/cond-mat/0205225 | |
| dc.identifier | http://arxiv.org/abs/cond-mat/0205225 | |
| dc.identifier | BioSystems, Vol. 69, 63-72 (2003) | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/19291 | |
| dc.subject | Disordered Systems and Neural Networks | |
| dc.subject | Quantitative Methods | |
| dc.title | How to decide whether small samples comply with an equidistribution | |
| dc.type | text |