Integration of statistical inference methods and a novel control measure to improve sensitivity and specificity of data analysis in expression profiling studies

Journal of Biomedical Informatics

Volumn 40, Issue 5, 2007, Pages 552-560

  Zang, Shizhu ^a   Guo, Ruifang ^a   Zhang, Liang ^b   Lu, Youyong ^a  

^a PEKING UNIVERSITY CANCER HOSPITAL AND INSTITUTE   (China)

^b NATIONAL ENGINEERING RESEARCH CENTER FOR BEIJING BIOCHIP TECHNOLOGY   (China)

Author keywords

BAGEL; Gastric cancer; Gene expression profiling; Microarray; SAM

Indexed keywords

BAYESIAN NETWORKS; BIOINFORMATICS; DATA REDUCTION; GENE EXPRESSION; SENSITIVITY ANALYSIS; STATISTICAL METHODS;

BAGEL; GASTRIC CANCERS; RAW DATA; SIGNIFICANCE ANALYSIS OF MICROARRAYS (SAM);

MICROARRAYS;

ARTICLE; BAYES THEOREM; COMPUTER INTERFACE; DATA ANALYSIS; GENE EXPRESSION; MICROARRAY ANALYSIS; NUCLEOTIDE SEQUENCE; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; STOMACH CANCER;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; GENE EXPRESSION PROFILING; MODELS, GENETIC; MODELS, STATISTICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SYSTEMS INTEGRATION;

EID: 34548314306     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2007.01.002     Document Type: Article

References (30)

1. Cui X., and Churchill G.A. Statistical tests for differential expression in cDNA microarray experiments. Genome Biol 4 (2003) 210
2. Yang Y.H., Xiao Y., and Segal M.R. Identifying differentially expressed genes from microarray experiments via statistic synthesis. Bioinformatics 21 7 (2005) 1084-1093
3. Pan W. A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics 18 (2002) 546-554
4. Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004; 3:Article3.
5. Tusher V.G., Tibshirani R., and Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 98 (2001) 5116-5121
6. Newton M.A., Noueiry A., Sarkar D., and Ahlquist P. Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics 5 (2004) 155-176
7. Townsend JP, Hartl DL. Bayesian analysis of gene expression levels: statistical quantification of relative mRNA level across multiple strains or treatments. Genome Biol 2002; 3:RESEARCH0071.
8. Ghosh D. Mixture models for assessing differential expression in complex tissues using microarray data. Bioinformatics 20 (2004) 1663-1669
9. Kerr M.K., Martin M., and Churchill G.A. Analysis of variance for gene expression microarray data. J Comput Biol 7 (2000) 819-837
10. Jeffery I.B., Higgins D.G., and Culhane A.C. Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data. BMC Bioinform 7 (2006) 359
11. Guo L., Lobenhofer E.K., Wang C., Shippy R., Harris S.C., Zhang L., Mei N., Chen T., Herman D., Goodsaid F.M., Hurban P., Phillips K.L., Xu J., Deng X., Sun Y.A., Tong W., Dragan Y.P., and Shi L. Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nat Biotechnol 24 (2006) 1162-1169
12. Huminiecki L., Gorn M., Suchting S., Poulsom R., and Bicknell R. Magic roundabout is a new member of the roundabout receptor family that is endothelial specific and expressed at sites of active angiogenesis. Genomics 79 (2002) 547-552
13. Murr R., Loizou J.I., Yang Y.G., Cuenin C., Li H., Wang Z.Q., et al. Histone acetylation by Trrap-Tip60 modulates loading of repair proteins and repair of DNA double-strand breaks. Nat Cell Biol 8 (2006) 91-99
14. Li H., Rauch T., Chen Z.X., Szabo P.E., Riggs A.D., and Pfeifer G.P. The histone methyltransferase SETDB1 and the DNA methyltransferase DNMT3A interact directly and localize to promoters silenced in cancer cells. J Biol Chem 281 (2006) 19489-19500
15. Rieder G., Tessier A.J., Qiao X.T., Madison B., Gumucio D.L., and Merchant J.L. Helicobacter-induced intestinal metaplasia in the stomach correlates with Elk-1 and serum response factor induction of villin. J Biol Chem 280 (2005) 4906-4912
16. Omidvar N., Wang E.C., Brennan P., Longhi M.P., Smith R.A., and Morgan B.P. Expression of glycosylphosphatidylinositol-anchored CD59 on target cells enhances human NK cell-mediated cytotoxicity. J Immunol 176 (2006) 2915-2923
17. Ozdag H., Teschendorff A.E., Ahmed A.A., Hyland S.J., Blenkiron C., Bobrow L., et al. Differential expression of selected histone modifier genes in human solid cancers. BMC Genomics 7 (2006) 90
18. Ozer A., Wu L.C., and Bruick R.K. The candidate tumor suppressor ING4 represses activation of the hypoxia inducible factor (HIF). Proc Natl Acad Sci USA 102 (2005) 7481-7486
19. Smyth G.K., Yang Y.H., and Speed T. Statistical issues in cDNA microarray data analysis. Meth Mol Biol 224 (2003) 111-136
20. Kutalik Z., Inwald J., Gordon S.V., Hewinson R.G., Butcher P., Hinds J., Cho K.H., and Wolkenhauer O. Advanced significance analysis of microarray data based on weighted resampling: a comparative study and application to gene deletions in Mycobacterium bovis. Bioinformatics 20 (2004) 357-363
21. Townsend J.P. Resolution of large and small differences in gene expression using models for the Bayesian analysis of gene expression levels and spotted DNA microarrays. BMC Bioinform 5 (2004) 54
22. Meiklejohn C.D., and Townsend J.P. A Bayesian method for analysing spotted microarray data. Brief Bioinform 6 (2005) 318-330
23. Eisen M.B., Spellman P.T., Brown P.O., and Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95 (1998) 14863-14868
24. Sturn A., Quackenbush J., and Trajanoski Z. Genesis: cluster analysis of microarray data. Bioinformatics 18 (2002) 207-208
25. Zeeberg B.R., Qin H., Narasimhan S., Sunshine M., Cao H., Kane D.W., Reimers M., Stephens R.M., Bryant D., Burt S.K., Elnekave E., Hari D.M., Wynn T.A., Cunningham-Rundles C., Stewart D.M., Nelson D., and Weinstein J.N. High-Throughput GoMiner, an 'industrial-strength' integrative gene ontology tool for interpretation of multiple-microarray experiments, with application to studies of Common Variable Immune Deficiency (CVID). BMC Bioinform 6 (2005) 168
26. Dennis G., Sherman B.T., Hosack D.A., Yang J., Gao W., Lane H.C., and Lempicki R.A. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol 4 (2003) 3
27. Tibshirani R., Hastie T., Narasimhan B., and Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA 99 (2002) 6567-6572
28. Pepe M.S., Longton G., Anderson G.L., and Schummer M. Selecting differentially expressed genes from microarray experiments. Biometrics 59 (2003) 133-142
29. Patterson T.A., Lobenhofer E.K., Fulmer-Smentek S.B., Collins P.J., Chu T.M., Bao W., Fang H., Kawasaki E.S., Hager J., Tikhonova I.R., Walker S.J., Zhang L., Hurban P., de Longueville F., Fuscoe J.C., Tong W., Shi L., and Wolfinger R.D. Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project. Nat Biotechnol 24 (2006) 1140-1150
30. Yang Y.H., Dudoit S., Luu P., Lin D.M., Peng V., Ngai J., and Speed T.P. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucl Acid Res 30 (2002) e15