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| Content Provider | PubMed Central |
|---|---|
| Author | Tan, Yuan-de Fornage, Myriam Fu, Yun-xin |
| Abstract | Microarray technology provides a powerful tool for the expression profile of thousands of genes simultaneously, which makes it possible to explore the molecular and metabolic etiology of the development of a complex disease under study. However, classical statistical methods and technologies fail to be applied to microarray data. Therefore, it is necessary and motivated to develop the powerful methods for large-scale statistical analyses. In this paper, we described a novel method, called Ranking Analysis of Microarray data (RAM). RAM, which is a large-scale two-sample t-test method, is based on comparisons between a set of ranked T-statistics and a set of ranked Z-values (a set of ranked estimated null scores) yielded by a “randomly splitting” approach instead of a “permutation” approach and two-simulation strategy for estimating the proportion of genes identified by chance, i.e., the false discovery rate (FDR). The results obtained from the simulated and observed microarray data shows that RAM is more efficient in identification of genes differentially expressed and estimation of FDR under the undesirable conditions such as a large fudge factor, small sample size, or mixture distribution of noises than Significance Analysis of Microarrays (SAM). |
| Related Links | http://dx.doi.org/10.1016/j.ygeno.2006.08.003 |
| Ending Page | 854 |
| Page Count | 9 |
| Starting Page | 846 |
| File Format | |
| ISSN | 08887543 |
| e-ISSN | 10898646 |
| Journal | Genomics |
| Issue Number | 6 |
| Volume Number | 88 |
| Language | English |
| Publisher Date | 2006-12-01 |
| Access Restriction | Open |
| Subject Keyword | Genetics Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Genetics |
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