Leveraging two-way probe-level block design for identifying differential gene expression with high-density oligonucleotide arrays

BMC Bioinformatics

Volumn 5, Issue , 2004, Pages

  Barrera, Leah ^a,b   Benner, Chris ^a,b   Tao, Yong Chuan ^c   Winzeler, Elizabeth ^a,d   Zhou, Yingyao ^a  

^a GENOMICS INSTITUTE OF THE NOVARTIS RESEARCH FOUNDATION   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c NOVARTIS INSTITUTES FOR BIOMEDICAL RESEARCH   (United States)

^d SCRIPPS RESEARCH INSTITUTE   (United States)

Author keywords

ANOVA; Differential expression; FDR; Gene expression analysis; High density oligonucleotide array

Indexed keywords

DIFFERENTIAL EXPRESSIONS; DIFFERENTIAL GENE EXPRESSIONS; DIFFERENTIALLY EXPRESSED GENE; FDR; GENE EXPRESSION ANALYSIS; OLIGONUCLEOTIDE ARRAYS; OLIGONUCLEOTIDE MICROARRAY; RECEIVER OPERATING CHARACTERISTIC CURVES;

ANALYSIS OF VARIANCE (ANOVA); GENE EXPRESSION; OLIGONUCLEOTIDES; SAMPLING; TESTING;

PROBES;

OLIGONUCLEOTIDE; DNA PROBE;

ANALYSIS OF VARIANCE; ARTICLE; CONCENTRATION (PARAMETERS); CONTROLLED STUDY; DNA MICROARRAY; GENE EXPRESSION; GENE IDENTIFICATION; INTERMETHOD COMPARISON; RANK SUM TEST; RECEIVER OPERATING CHARACTERISTIC; SAMPLE SIZE; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; STUDENT T TEST; BIOLOGY; CELL LINE; COMPARATIVE STUDY; CONFORMATION; CYTOLOGY; DNA PROBE; FIBROBLAST; GENE EXPRESSION PROFILING; GENETIC VARIABILITY; GENETICS; HUMAN; METABOLISM; METHODOLOGY; PROCEDURES; QUALITY CONTROL; REPRODUCIBILITY; STATISTICS AND NUMERICAL DATA;

ANALYSIS OF VARIANCE; BENCHMARKING; CELL LINE; COMPUTATIONAL BIOLOGY; DNA PROBES; FIBROBLASTS; GENE EXPRESSION PROFILING; GENETIC VARIATION; HUMANS; NUCLEIC ACID CONFORMATION; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; REPRODUCIBILITY OF RESULTS; RESEARCH DESIGN; SAMPLE SIZE; SENSITIVITY AND SPECIFICITY;

EID: 2942604440     PISSN: 14712105     EISSN: None     Source Type: Journal    
DOI: 10.1186/1471-2105-5-42     Document Type: Article

References (23)

1. Pan W: A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics 2002, 18:546-554.
2. Storey JD, Tibshirani R: SAM thresholding and false discovery rates for detecting differential gene expression in DNA microarrays. In To appear in The Analysis of Gene Expression Data: Methods and Software. Springer, New York 2003.
3. Dudoit S, Yang YH, Callow MJ, Speed TP: Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Stat Sinica 2002, 12:111-139.
4. Troyanskaya O, Garber ME, Brown PO, Botstein D, Altman RB: Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics 2002, 18:1454-1461.
5. Pan W, Lin J, Le CT: How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach. Genome Biol 2002, 3:research0022.1-0022.10.
6. Pan W, Lin J, Le C: A Mixture Model Approach to Detecting Differentially Expressed Genes with Microarray Data. To appear in Functional & Integrative Genomics 2003. (Also Report 2003-004, Division of Biostatistics, University of Minnesota) 2003.
7. Tusher V, Tibshirani R, Chu G: Significance analysis of microarrays applied to transcriptional responses to ionizing radiation. Proc Natl Acad Sci USA 2001, 98:5116-5121.
8. Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B 1995, 57:289-300.
9. Reiner A, Yekutieli D, Benjamini Y: Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 2003, 19:368-375.
10. Zhou Y, Abagyan R: Algorithms for high-density oligonucleotide array. Curr Opin Drug Discov Devel 2003, 6:339-345.
11. Li C, Wong WH: Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection,. Proc Natl Acad Sci 2001, 98:31-36.
12. Yang Y, Hoh J, Broger C, Neeb M, Edington J, Lindpainter K, Ott J: Statistical Methods for Analyzing Microarray Feature Data with Replications. J Comput Biol 2003, 10:157-169.
13. Chu TM, Weir B, Wolfinger R: A systematic statistical linear modeling approach to oligonucleotide array experiments. Math Biosci 2002, 176:35-51.
14. Lemon WJ, Liyanarachchi S, You M: A high performance test of differential gene expression for oligonucleotide arrays. Genome Biology 2003, 4:R67.
15. Affymetrix Latin Square Data for Expression Algorithm Assessment: Affymetrix Inc., Santa Clara, CA, USA [http://www.affymetrix.com/analysis/downloadn_center2.affx].
16. Lemon WJ, Palatini JJT, Krahe R, Wright FW: Theoretical and experimental comparisons of gene expression indices for oligonucleotide arrays. Bioinformatics 2002, 18:1470-1476.
17. Liu W, Mei R, Di X, Ryder TB, Hubbell E, Dee S, Webster TA, Harrington CA, Ho M, Baid J, Smeekens SP: Analysis of high-density expression microarrays with signed-rank call algorithms. Bioinformatics 2002, 18:1593-1599.
18. Hoffmann R, Seidl T, Dugas M: Profound effect of normalization on detection of differentially expressed genes in oligonucleotide microarray analysis. Genome Biol 2002, 3:research0033.1-0033.11.
19. Rice JA: Mathematical Statistics and Data Analysis. 2nd edition. Duxbury Press, Belmont, CA; 1995.
20. Rajagopalan D: Comparison of statistical methods for oligonucleotide arrays. Bioinformatics 2003, 19:1469-1476.
21. Hollander M, Wolfe D: Nonparametric Statistical Methods. Wiley, New York 2 1999.
22. Sokal RF: Biometry. Freeman, New York 3 1995.
23. Jobson JD: Applied Multivariate Data Analysis, Volume I: Regression and Experimental Design. Springer-Verlag, New York 1991.