Name
Abstract | ||
|---|---|---|
We evaluate and demonstrate the proposed method via extensive simulation studies and the application to mouse gut microbiome data. The results show that the proposed method has desirable properties and outperform the previously used methods in terms of both empirical power and Type I error. The method has been incorporated into the freely available R package BhGLM ( http://www.ssg.uab.edu/bhglm/ and http://github.com/abbyyan3/BhGLM ), providing a useful tool for analyzing microbiome data. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1186/s12859-016-1441-7 | BMC Bioinformatics |
Keywords | Field | DocType |
Correlated measures,Count data,Metagenomics,Microbiome,Negative binomial model,Penalized Quasi-likelihood,Random effects | Data mining,Random effects model,Computer science,Microbiome,Metagenomics,Mixed model,Negative binomial distribution,Count data,Bioinformatics,Type I and type II errors,Generalized linear mixed model | Journal |
Volume | Issue | ISSN |
18 | 1 | 1471-2105 |
Citations | PageRank | References |
3 | 0.52 | 10 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinyan Zhang | 1 | 9 | 2.47 |
| Himel Mallick | 2 | 3 | 2.21 |
| Zaixiang Tang | 3 | 7 | 1.56 |
| Lei Zhang | 4 | 25 | 3.00 |
| Xiangqin Cui | 5 | 3 | 0.52 |
| Andrew K. Benson | 6 | 12 | 1.16 |
| Nengjun Yi | 7 | 20 | 7.67 |