Integrating biological information into the statistical analysis and design of microarray experiments

Animal

Volumn 4, Issue 2, 2010, Pages 165-172

  Rosa, G J M ^a   Vazquez, A I ^a  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

Biological knowledge; Differential expression; Experimental design; Microarray

Indexed keywords

ANIMALIA;

EID: 76849086736     PISSN: 17517311     EISSN: 1751732X     Source Type: Journal    
DOI: 10.1017/S1751731109991054     Document Type: Article

References (52)

1. Beissbarth T and Speed TP 2004. GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics 20, 1464-1465.
2. Bueno Filho JSS, Gilmour SG and Rosa GJM 2006. Design of microarray experiments for genetical genomics studies. Genetics 174, 945-957.
3. Bussemaker HJ, Li H and Siggia ED 2001. Regulatory element detection using correlation with expression. Nature Genetics 27, 167-171.
4. Cardoso FF, Rosa GJM, Steibel JP, Ernst CW, Bates RO and Tempelman RJ 2008. Selective transcriptional profiling and data analysis strategies for eQTL mapping in outbred F2 populations. Genetics 180, 1679-1690.
5. Cassar-Malek I, Picard B, Bernard C and Hocquette J-F 2008. Application of gene expression studies in livestock production systems: a European perspective. Australian Journal of Experimental Agriculture 48, 701-710.
6. Cohen BA, Mitra RD, Hughes JD and Church GM 2000. A computational analysis of whole-genome expression data reveals chromosomal domains of gene expression. Nature Genetics 26, 183-186.
7. Conlon EM, Liu XS, Lieb JD and Liu JS 2003. Integrating regulatory motif discovery and genome-wide expression analysis. Proceedings of the National Academy of sciences of the United States of America 100, 3339-3344.
8. Cui XG, Hwang JTG, Qiu J, Blades NJ and Churchill GA 2005. Improved statistical tests for differential gene expression by shrinking variance components estimates. Biostatistics 6, 59-75.
9. Fu J and Jansen RC 2006. Optimal design and analysis of genetic studies on gene expression. Genetics 172, 1993-1999.
10. Fukuoka Y, Inaoka H and Kohane IS 2004. Inter-species differences of coexpression of neighboring genes in eukaryotic genomes. BMC Genomics 5, 4.
11. Hiendleder S, Bauersachs S, Boulesteix A, Blum H, Arnold GJ, Frohlich T and Wolf E 2005. Functional genomics: tools for improving farm animal health and welfare. Revue Scientifique Et Technique-Office International Des Epizooties 24, 355-377.
12. Hoeschele I and Li H 2005. A note on joint versus gene-specific mixed model analysis of microarray gene expression data. Biostatistics 6, 183-186.
13. Huang DS and Pan W 2006. Incorporating biological knowledge into distancebased clustering analysis of microarray gene expression data. Bioinformatics 22, 1259-1268.
14. Huang DW, Sherman BT and Lempicki RA 2009. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Research 37, 1-13.
15. Imoto S, Higuchi T, Goto T, Kuhara S, Miyano S and Tashiro T 2003. Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks. Proceedings IEEE Computer Society Bioinformatics Conference 104-113.
16. Jaffrezic F, De Koning DJ, Boettcher PJ, Bonnet A, Buitenhuis B, Closset R, Déjean S, Delmas C, Detilleux JC, Dovc P, Duval M, Foulley JL, Hedegaard J, Hornshoj H, Hulsegge IB, Janss L, Jensen K, Jiang L, Lavric M, LêCao KA, Lund MS, Malinverni R, Marot G, Nie H, Petzl W, Pool MH, Robert-Granié C, SanCristobal M, van Schothorst EM, Schuberth HJ, Sorensen P, Stella A, Tosser-Klopp G, Waddington D, Watson M, Yang W, Zerbe H and Seyfert HM 2007. Analysis of the real EADGENE data set: comparison of methods and guidelines for data normalisation and selection of differentially expressed genes. Genetics Selection Evolution 39, 633-650.
17. Jannink JL 2005. Selective phenotyping to accurately map quantitative trait loci. Crop Science 45, 901-908.
18. Jansen RC and Nap J 2001. Genetical genomics: the added value from segregation. Trends in Genetics 17, 388-391.
19. Jin CF, Lan H, Attie AD, Churchill GA, Bulutuglo D and Yandell BS 2004. Selective phenotyping for increased efficiency in genetic mapping studies. Genetics 168, 2285-2293.
20. Kerr MK and Churchill GA 2001. Statistical design and the analysis of gene expression microarray data. Genetical Research 77, 123-128.
21. Kim Y, Doan BQ, Duggal P and Bailey-Wilson JE 2007. Normalization of microarray expression data using within-pedigree pool and its effect on linkage analysis. BMC Proceedings 1, S152.
22. Kondor RI and Lafferty J 2002. Diffusion Kernels on Graphs and Other Discrete Input Spaces. Proceedings of the 19th International Conference on Machine Learning, pp. 315-322.
23. Kruglyak S and Tang H 2000. Regulation of adjacent yeast genes. Trends in Genetics 16, 109-111.
24. Lam AC, Fu JY, Jansen RC, Haley CS and de Koning DJ 2008. Optimal design of genetic studies of gene expression with two-color microarrays in outbred crosses. Genetics 180, 1691-1698.
25. Lee I, Date SV, Adai AT and Marcotte EM 2004. A probabilistic functional network of yeast genes. Science 360, 1555-1558.
26. Lercher MJ, Urrutia AO and Hurst LD 2002. Clustering of housekeeping genes provides a unified model of gene order in human genome. Nature Genetics 31, 180-183.
27. Morozova O and Marra MA 2008. Applications of next-generation sequencing technologies in functional genomics. Genomics 92, 255-264.
28. Oshlack A, Emslie D, Corcoran LM and Smyth GK 2007. Normalization of boutique two-color microarrays with a high proportion of differentially expressed probes. Genome Biology 8, R2.
29. Pan W 2006a. Incorporating gene functional annotations in detecting differential gene expression. Applied Statistics 55, 301-316.
30. Pan W 2006b. Incorporating gene functional annotations in detecting differential gene expression. Journal of the Royal Statistical Society Series C 55, 301-316.
31. Piepho H-P 2005. Optimal allocation in designs for assessing heterosis from cDNA gene expression data. Genetics 171, 359-364.
32. Rosa GJM, De Leon N and Rosa AJM 2006. Review of microarray experimental design strategies for genetical genomics studies. Physiological Genomics 28, 15-23.
33. Rosa GJM, Steibel JP and Tempelman RJ 2005. Reassessing design and analysis of two-colour microarray experiments using mixed effects models. Comparative and Functional Genomics 6, 123-131.
34. Roy PJ, Stuart JM, Lund J and Kim SK 2002. Chromosomal clustering of muscleexpressed genes in Canenorhabditis elegans. Nature 418, 975-979.
35. Smith GW and Rosa GJM 2007. Interpretation of microarray data: trudging out of the abyss towards elucidation of biological significance. Journal of Animal Science 85(suppl. E), E20-E23.
36. Smyth GK 2004. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3(1), Article 3.
37. Sorensen P, Bonnet A, Buitenhuis B, Closset R, Dé jean S, Delmas C, Duval M, Tosser-Klopp G, Hedegaard J, Hornshoj H, Hulsegge IB, Jaffré zic F, Jensen K, Jiang L, de Koning DJ, LêCao KA, Nie H, Petzl W, Pool MH, Robert-Granié C, San Cristobal M, van Schothorst EM, Schuberth HJ, Seyfert HM, Waddington D, Watson M, Yang W, Zerbe H and Lund MS 2007. Analysis of the real EADGENE data set: Multivariate approaches and post analysis. Genetics Selection Evolution 39, 651-668.
38. Spellman PT and Rubin GM 2002. Evidence for large domains of similarly expressed genes in Drosophila genome. Journal of Biology 1, 5.
39. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES and Mesirov JP 2005. Gene set enrichment analysis: a knowledge-based approach for interpreting genomewide expression profiles. Proceedings of the National Academy of Sciences of the United States of America 102, 15545-15550.
40. Torres TT, Metta M, Ottenwä lder B and Schlö tterer C 2008. Gene expression profiling by massively parallel sequencing. Genome Research 18, 172-177.
41. Tseng GC 2007. Penalized and weighted K-means for clustering with scattered objects and prior information in high-throughput biological data. Bioinformatics 23, 2247-2255.
42. Tuikkala J, Elo L, Nevalainen OS and Aittokallio T 2006. Improving missing value estimation in microarray data with gene ontology. Bioinformatics 22, 566-572.
43. Vazquez AI, Rosa GJM, de los Campos G, Weigel KA and Gianola D 2009. Biologically informed models for microarray gene expression data analysis. In symposium on statistical genetics of livestock for the post-genomic era, Madison, WI, USA, 4 to 6 May 2009. (http://dysci.wisc.edu/sglpge/ posters.html)
44. Vert J-P and Kanehisa M 2003. Graph-driven features extraction from microarray data using diffusion kernels and kernel CCA. In Advances in Neural Information Processing Systems 15 (ed. S Becker, S Thrun and K Obermayer), pp. 1425-1432.
45. MIT Press, Cambridge, MA.
46. Wei P and Pan W 2008. Incorporating gene networks into statistical tests for genomic data via a spatially correlated mixture model. Bioinformatics 24, 404-411.
47. Werhli AV and Husmeier D 2007. Reconstructing gene regulatory networks with Bayesian networks by combining expression data with multiple sources of prior knowledge. Statistical Applications in Genetics and Molecular Biology 6, 15.
48. Williams EJB and Bowles DJ 2004. Coexpression of neighboring genes in the genome of Arabidopsis thaliana. Genome Research 14, 1060-1067.
49. Wolfinger RD, Gibson G, Wolfinger ED, Bennett L, Hamadeh H, Bushel P, Afshari C and Paules RS 2001. Assessing gene significance from cDNA microarray expression data via mixed models. Journal of Computational Biology 8, 625-637.
50. Xu Z, Zou F and Vision TJ 2005. Improving QTL mapping resolution in experimental crosses by the use of genotypically selected samples. Genetics 170, 401-408.
51. Yang YH and Speed T 2002. Design issues for cDNA microarray experiments. Nature Reviews Genetics 3, 579-588.
52. Zhang K, Lefkowitz ER, Wei H, Lorraine A, Page G and Allison DB 2009 Does sequence similarity predict expression similarity - A case study in Arabidopsis thaliana (in preparation).