Name
Title | ||
|---|---|---|
Assumption weighting for incorporating heterogeneity into meta-analysis of genomic data. |
Abstract | ||
|---|---|---|
There is now a large literature on statistical methods for the meta-analysis of genomic data from multiple studies. However, a crucial assumption for performing many of these analyses is that the data exhibit small between-study variation or that this heterogeneity can be sufficiently modelled probabilistically.In this article, we propose 'assumption weighting', which exploits a weighted hypothesis testing framework proposed by Genovese et al. to incorporate tests of between-study variation into the meta-analysis context. This methodology is fast and computationally simple to implement. Several weighting schemes are considered and compared using simulation studies. In addition, we illustrate application of the proposed methodology using data from several high-profile stem cell gene expression datasets. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1093/bioinformatics/bts037 | Bioinformatics |
Keywords | Field | DocType |
between-study variation,meta-analysis context,genomic data,weighting scheme,expression datasets,data exhibit small between-study,crucial assumption,proposed methodology,assumption weighting,high-profile stem cell gene | Data mining,Weighting,Computer science,Bioinformatics,Meta-analysis,Gene expression profiling,Statistical hypothesis testing | Journal |
Volume | Issue | ISSN |
28 | 6 | 1367-4811 |
Citations | PageRank | References |
2 | 0.48 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yihan Li | 1 | 2 | 0.48 |
| Debashis Ghosh | 2 | 496 | 49.16 |