Name
Title | ||
|---|---|---|
Strategies for Identifying Statistically Significant Dense Regions in Microarray Data |
Abstract | ||
|---|---|---|
We propose and study the notion of dense regions for the analysis of categorized gene expression data and present some searching algorithms for discovering them. The algorithms can be applied to any categorical data matrices derived from gene expression level matrices. We demonstrate that dense regions are simple but useful and statistically significant patterns that can be used to 1) identify genes and/or samples of interest and 2) eliminate genes and/or samples corresponding to outliers, noise, or abnormalities. Some theoretical studies on the properties of the dense regions are presented which allow us to characterize dense regions into several classes and to derive tailor-made algorithms for different classes of regions. Moreover, an empirical simulation study on the distribution of the size of dense regions is carried out which is then used to assess the significance of dense regions and to derive effective pruning methods to speed up the searching algorithms. Real microarray data sets are employed to test our methods. Comparisons with six other well-known clustering algorithms using synthetic and real data are also conducted which confirm the superiority of our methods in discovering dense regions. The DRIFT code and a tutorial are available as supplemental material, which can be found on the Computer Society Digital Library at http://computer.org/tcbb/archives.htm. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/TCBB.2007.1022 | IEEE/ACM Trans. Comput. Biology Bioinform. |
Keywords | Field | DocType |
clustering,bayesian methods,search algorithm,clustering algorithms,bioinformatics,microarray,data mining,algorithm design and analysis,analysis of variance,digital library,statistical significance,data analysis,microarray data,searching algorithms,categorical data,molecular biophysics,gene expression,genetics,statistical analysis,testing,integrated circuits | Data mining,Search algorithm,Algorithm design,Categorical variable,Matrix (mathematics),Computer science,Outlier,Bioinformatics,Cluster analysis,Bayesian probability,Speedup | Journal |
Volume | Issue | ISSN |
4 | 3 | 1545-5963 |
Citations | PageRank | References |
3 | 0.38 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andy M. Yip | 1 | 232 | 20.65 |
| Ng Michael | 2 | 4231 | 311.70 |
| EDMOND HAOCUN WU | 3 | 10 | 2.29 |
| Tony F. Chan | 4 | 8733 | 659.77 |