Identification of differentially expressed genes and false discovery rate in microarray studies

Current Opinion in Lipidology

Volumn 18, Issue 2, 2007, Pages 187-193

  Gusnanto, Arief ^a   Calza, Stefano ^b   Pawitan, Yudi ^c  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b UNIVERSITY OF BRESCIA   (Italy)

^c KAROLINSKA INSTITUTE   (Sweden)

Author keywords

Differentially expressed; False discovery rate; Gene expression; Microarray

Indexed keywords

LIPID; MESSENGER RNA; RNA;

COMPUTER PROGRAM; GENE EXPRESSION; GENE IDENTIFICATION; GENE MUTATION; GENETIC ANALYSIS; GENETIC VARIABILITY; GENOME; HIGH THROUGHPUT SCREENING; HYPOXIA; LIPID METABOLISM; MICROARRAY ANALYSIS; NONHUMAN; PRIORITY JOURNAL; REGRESSION ANALYSIS; REVIEW; SLEEP APNEA SYNDROME; STATISTICAL ANALYSIS; STUDENT T TEST;

AUTOMATIC DATA PROCESSING; FALSE NEGATIVE REACTIONS; GENES; HUMANS; NEOPLASMS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PROBABILITY; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; RESEARCH; RESEARCH DESIGN;

EID: 33947172745     PISSN: 09579672     EISSN: None     Source Type: Journal    
DOI: 10.1097/MOL.0b013e3280895d6f     Document Type: Review

References (33)

1. Alon U, Barkai N, Notterman DA, et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci U S A 1999; 96:6745-6750.
2. Golub TR, Slonim DK, Tamayo P, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999; 286:531-537.
3. Alizadeh AA, Eisen MB, Davis RE, et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000; 403:503-511.
4. Li C, Wong WH. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Natl Acad Sci U S A 2001; 98:31-36.
5. Irizarry RA, Hobbs B, Collin F, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 2003; 4:249-264.
6. Kerr MK, Martin M, Churchill GA. Analysis of variance for gene expression microarray data. J Comp Biol 2000; 7:819-837.
7. Wolfinger RD, Gibson G, Wolfinger ED, et al. Assessing gene significance from cDNA microarray expression data via mixed models. J Comp Biol 2001; 8:625-637.
8. Fang Y, Brass A, Hoyle DC, et al. A model-based analysis of microarray experimental error and normalisation. Nucleic Acids Res 2003; 31:e96.
9. Vuylsteke M, van Eeuwijk F, Van Hummelen P, et al. Genetic analysis of variation in gene expression in Arabidopsis thaliana. Genetics 2005; 171:1267-1275.
10. Tseng GC, Oh MK, Rohlin L, et al. Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. Nucleic Acids Res 2001; 29:2549-2557.
11. Yang YH, Dudoit S, Luu P, et al. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res 2002; 30:e15.
12. Bolstad BM, Irizarry RA, Astrand M, et al. A comparison of normalization methods for high density oligonucleotide array data based on bias and variance. Bioinformatics 2003; 19:185-193.
13. Calza S, Raffelsberger W, Ploner A, et al. Filtering genes to improve sensitivity in oligonucleotide microarray data analysis. Genome Biol (in press).
14. Cui X, Churchill GA. Statistical tests for differentially expression in cDNA microarray experiments. Genome Biol 2003; 4:210.
15. Dudoit S, Shaffer JP, Boldrick JC. Multiple hypothesis testing in microarray experiments. Stat Sci 2003; 18:71-103.
16. Holm S. A simple sequentially rejective multiple trial procedure. Scand J Stat 1979; 6:65-70.
17. Westfall PH, Young SS. Resampling-based multiple testing. New York: Wiley; 1993.
18. Dudoit S, Yang YH, Speed TP, et al. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Stat Sin 2002; 12:111-139.
19. Benjamini Y, Hochberg Y. Controlling the false discovery rate - a practical and powerful approach to multiple testing. J R Stat Soc Ser B 1995; 57:289-300.
20. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci U S A 2003; 100:9440-9445.
21. Pawitan Y, Michiels S, Koscielny S, et al. False discovery rate, sensitivity, and sample size for microarray studies. Bioinformatics 2005; 21:3017-3024.
22. Schena M, Shalon D, Heller R, et al. Parallel human genome analysis: microarray-based expression monitoring of 1000 genes. Proc Natl Acad Sci USA 1996; 93:10614-10619.
23. Draghici S. Statistical intelligence: effective analysis of high-density microarray data. Drug Discov Today 2002; 7:S55-S63.
24. Jeffery IB, Higgins DG, Culhane AC. Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data. BMC Bioinform 2006; 7:359.
25. Ploner A, Calza S, Gusnanto A, et al. Multidimensional local false discovery rate for microarray studies. Bioinformatics 2005; 22:556-565.
26. Tusher V, Tibshirani R, Chu G. Signifiance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 2001; 98:5116-5121.
27. Lö nnstedt I, Speed TP. Replicated microarray data. Stat Sin 2002; 12:31-46.
28. Smyth GK. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004; 3:article 3.
29. Dobbin KK, Kawasaki ES, Petersen DW, et al. Characterizing dye bias in microarray experiments. Bioinformatics 2005; 21:2430-2437.
30. Gusnanto A, Ploner A, Pawitan Y. Fold-change estimation and identification of differentially expressed genes using mixture mixed-model. Stat Appl Genet Mol Biol 2005; 4:article 26.
31. Efron B, Tibshirani R, Storey J, et al. Empirical Bayes analysis of a microarray experiment. J Am Stat Assoc 2001; 96:1151-1160.
32. Li J, Grigoryev DN, Ye SQ, et al. Chronic intermittent hypoxia upregulates genes of lipid biosynthesis in obese mice. J Appl Physiol 2005; 99:1634-1635.
33. R Development Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2006. •• The R software has been a main workhorse for analysing gene expression data, both for display and inference purposes. R is also the language on which the Bioconductor packages are based.