Density based pruning for identification of differentially expressed genes from microarray data

BMC Genomics

Volumn 11, Issue SUPPL. 2, 2010, Pages

  Hu, Jianjun ^a   Xu, Jia ^a  

^a University of South Carolina   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; CONTROLLED STUDY; DENSITY BASED PRUNING ALGORITHM; GENE EXPRESSION; GENE IDENTIFICATION; GENE MAPPING; MICROARRAY ANALYSIS; PREDICTION; DNA MICROARRAY; GENE EXPRESSION PROFILING; GENETIC DATABASE; METHODOLOGY;

ALGORITHMS; DATABASES, GENETIC; GENE EXPRESSION; GENE EXPRESSION PROFILING; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PATTERN RECOGNITION, AUTOMATED;

EID: 78149323451     PISSN: None     EISSN: 14712164     Source Type: Journal    
DOI: 10.1186/1471-2164-11-S2-S3     Document Type: Article

References (26)

1. Derisi JL, Iyer VR, Brown PO. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 1997, 278:680-686. 10.1126/science.278.5338.680, 9381177.
2. Breitling R, Armengaud P, Amtmann A, Herzyk P. Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett. 2004, 573:83-92. 10.1016/j.febslet.2004.07.055, 15327980.
3. Jeffery IB, Higgins DG, Culhane AC. Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data. Bmc Bioinformatics 2006, 7. 1544358, 16872483.
4. Nam D, Kim SY. Gene-set approach for expression pattern analysis. Briefings in Bioinformatics 2008, 9:189-197. 10.1093/bib/bbn001, 18202032.
5. Shi J, Walker MG. Gene set enrichment analysis (GSEA) for interpreting gene expression profiles. Current Bioinformatics 2007, 2:133-137.
6. Chen R, Morgan AA, Dudley J, Deshpande T, Li L, Kodama K, Chiang AP, Butte AJ. FitSNPs: highly differentially expressed genes are more likely to have variants associated with disease. Genome Biol. 2008, 9:R170. 2646274, 19061490, 10.1186/gb-2008-9-12-r170.
7. Nevarez L, Vasseur V, Le DG, Tanguy A, Guisle-Marsollier I, Houlgatte R, Barbier G. Isolation and analysis of differentially expressed genes in Penicillium glabrum subjected to thermal stress. Microbiology 2008, 154:3752-3765. 10.1099/mic.0.2008/021386-0, 19047743.
8. Estler M, Boskovic G, Denvir J, Miles S, Primerano DA, Niles RM. Global analysis of gene expression changes during retinoic acid-induced growth arrest and differentiation of melanoma: comparison to differentially expressed genes in melanocytes vs melanoma. Bmc Genomics 2008, 9:478. 2572629, 18847503, 10.1186/1471-2164-9-478.
9. Satish L, Laframboise WA, O'Gorman DB, Johnson S, Janto B, Gan BS, Baratz ME, Hu FZ, Post JC, Ehrlich GD, et al. Identification of differentially expressed genes in fibroblasts derived from patients with Dupuytren's Contracture. BMC Med. Genomics, 2008, 1:10.
10. Datta S, Satten GA, Xia JZ, Heslin MJ, Datta S. An empirical bayes adjustment to increase the sensitivity of detecting differentially expressed genes in microarray experiments. Bioinformatics 2004, 20:235-242. 10.1093/bioinformatics/btg396, 14734315.
11. Shi L, Jones WD, Jensen RV, Harris SC, Perkins RG, Goodsaid FM, Guo L, Croner LJ, Boysen C, Fang H, et al. The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies. BMC Bioinformatics 2008, 9(Suppl 9):S10. 2537561, 18793455, 10.1186/1471-2105-9-S9-S10.
12. Yang K, Li J, Gao H. The impact of sample imbalance on identifying differentially expressed genes. Bmc Bioinformatics, 2006, 7(Suppl 4):S8.
13. Baldi P, Long AD. A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes. Bioinformatics 2001, 17:509-519. 10.1093/bioinformatics/17.6.509, 11395427.
14. Jain N, Thatte J, Braciale T, Ley K, O'Connell M, Lee JK. Local-pooled-error test for identifying differentially expressed genes with a small number of replicated microarrays. Bioinformatics 2003, 19,:1945-1951.
15. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. U. S A 2001, 98:5116-5121. 33173, 11309499, 10.1073/pnas.091062498.
16. Wille A, Gruissem W, Buhlmann P, Hennig L. EVE (external variance estimation) increases statistical power for detecting differentially expressed genes. Plant Journal 2007, 52:561-569. 10.1111/j.1365-313X.2007.03227.x, 17680783.
17. Kim RD, Park PJ. Improving identification of differentially expressed genes in microarray studies using information from public databases. Genome Biology 2004, 5.
18. Hackstadt AJ, Hess AM. Filtering for increased power for microarray data analysis. Bmc Bioinformatics 2009, 10. 2661050, 19133141.
19. Zhou Y, Cras-Meneur C, Ohsugi M, Stormo GD, Permutt MA. A global approach to identify differentially expressed genes in cDNA (two-color) microarray experiments. Bioinformatics 2007, 23:2073-2079. 10.1093/bioinformatics/btm292, 17550914.
20. Kadota K, Nakai Y, Shimizu K. A weighted average difference method for detecting differentially expressed genes from microarray data. Algorithms for Molecular Biology 2008, 3. 2464587, 18578891.
21. Dettling M, Gabrielson E, Parmigiani G. Searching for differentially expressed gene combinations. Genome Biol. 2005, 6:R88. 1257471, 16207359, 10.1186/gb-2005-6-10-r88.
22. Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Edgar R. NCBI GEO: mining tens of millions of expression profiles - database and tools update. Nucleic Acids Research 2007, 35:D760-D765. 1669752, 17099226, 10.1093/nar/gkl887.
23. Kadota K, Nakai Y, Shimizu K. Ranking differentially expressed genes from Affymetrix gene expression data: methods with reproducibility, sensitivity, and specificity. Algorithms for Molecular Biology 2009, 4. 2679019, 19386098.
24. Hong FX, Breitling R, McEntee CW, Wittner BS, Nemhauser JL, Chory J. RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis. Bioinformatics 2006, 22:2825-2827. 10.1093/bioinformatics/btl476, 16982708.
25. Xiaodong C, Giannakis GB. Identifying differentially expressed genes in microarray experiments with model-based variance estimation. IEEE Transaction on Signal Processing 2008, 54:2418-2426.
26. Shaik JS, Yeasin M. A unified framework for finding differentially expressed genes from microarray experiments. Bmc Bioinformatics 2007, 8. 2099446, 17877806.