DNA microarrays provide data for genome wide patterns of expression between varying conditions. Microarray studies often have small samples sizes, however, due to cost constraints or specimen availability. This can lead to poor random error estimates and inaccurate statistical tests of differential expression. We compare the performance of the standard t-test, simple fold change, and three small n statistical test methods designed to circumvent these problems, by applying them to simulated and experimental microarray data. The Empirical Bayes t-statistic was the most robust and effective method across simulated data and experimental data. Overall, the Empirical Bayes methodology provided the most optimal balance between specificity and sensitivity in detecting differential expression.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.97977 |
| Date | January 2005 |
| Creators | Murie, Carl. |
| Publisher | McGill University |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Format | application/pdf |
| Coverage | Master of Science (School of Computer Science.) |
| Rights | © Carl Murie, 2005 |
| Relation | alephsysno: 002479814, proquestno: AAIMR24751, Theses scanned by UMI/ProQuest. |
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