A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments

Bioinformatics

Volumn 18, Issue 4, 2002, Pages 546-554

  Pan, Wei ^a  

^a UNIVERSITY OF MINNESOTA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACUTE GRANULOCYTIC LEUKEMIA; ACUTE LYMPHOBLASTIC LEUKEMIA; ARTICLE; BAYES THEOREM; COMPUTER PROGRAM; CONTROLLED STUDY; DATA ANALYSIS; DNA MICROARRAY; DNA REPLICATION; EXPERIMENTAL DESIGN; GENE EXPRESSION; GENE IDENTIFICATION; INTERMETHOD COMPARISON; LEUKEMIA; PRIORITY JOURNAL; REGRESSION ANALYSIS; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICAL SIGNIFICANCE; TISSUE SLICE; UNINDEXED SEQUENCE; ACUTE DISEASE; ACUTE LYMPHOCYTIC LEUKEMIA; ALGORITHM; BIOLOGICAL MODEL; COMPARATIVE STUDY; GENE EXPRESSION PROFILING; GENETICS; HUMAN; METHODOLOGY; MULTIVARIATE ANALYSIS; MYELOID LEUKEMIA; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ACUTE DISEASE; ALGORITHMS; BAYES THEOREM; GENE EXPRESSION; GENE EXPRESSION PROFILING; HUMANS; LEUKEMIA, LYMPHOCYTIC, ACUTE; LEUKEMIA, MYELOID; MODELS, GENETIC; MODELS, STATISTICAL; MULTIVARIATE ANALYSIS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 0035999977     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/18.4.546     Document Type: Article

References (31)

1. Controlling the false discovery rate: A practical and powerful approach to multiple testing
2. Welch's approximate solution for the Behrens-Fisher problem
3. Exploring the new world of the genome with DNA microarrays
4. Ratio-based decisions and the quantitative analysis of cDNA microarray images
5. Maximum likelihood estimation from incomplete data via the EM algorithm (with discussion)
6. Comment on 'regression models for discrete longitudinal responses'
7. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments
8. Microarrays and their use in a comparative experiment
9. Empirical Bayes analysis of a microarray experiment
10. How many clusters? Which clustering methods?-Answers via model-based cluster analysis
11. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring
12. Testing for differentially-expressed genes by maximum likelihood analysis of microarray data
13. Analysis of variance for gene expression microarray data
14. Array of hope
15. Importance of replication in microarray gene expression studies: Statistical methods and evidence from repetitive cDNA hybridizations
16. Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection
17. Mining for low-abundance transcripts in microarray data
18. Fitting of mixtures of normal and t-components
19. On differential variability of expression ratios: Improving statistical inference about gene expression changes from microarray data
20. A mixture model approach to detecting differentially expressed genes with microarray data
21. How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach
22. Practical solutions of the Behrens-Fisher problem
23. Estimating the dimensions of a model
24. An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles
25. Statistical Analysis of Finite Mixture Distributions
26. Significance analysis of microarrays applied to the ionizing radiation response
27. The generalization of 'students' problem when several different population variances are involved
28. Comparison of methods for image analysis on cDNA microarray data
29. Statistical modeling of large microarray data sets to identify stimulus-response profiles