A fully scalable online pre-processing algorithm for short oligonucleotide microarray atlases

Nucleic Acids Research

Volumn 41, Issue 10, 2013, Pages

  Lahti, Leo ^a,b   Torrente, Aurora ^c,d   Elo, Laura L ^e,f   Brazma, Alvis ^c   Rung, Johan ^c  

^a UNIVERSITY OF HELSINKI   (Finland)

^b WAGENINGEN UNIVERSITY   (Netherlands)

^c EUROPEAN BIOINFORMATICS INSTITUTE   (United Kingdom)

^d UNIVERSIDAD CARLOS III DE MADRID   (Spain)

^e UNIVERSITY OF TURKU   (Finland)

^f TURKU CENTRE FOR BIOTECHNOLOGY   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BAYES THEOREM; COMPUTER PROGRAM; CORRELATION ANALYSIS; GENE EXPRESSION; INFORMATION PROCESSING; LEARNING ALGORITHM; MATHEMATICAL ANALYSIS; MICROARRAY ANALYSIS; NOISE REDUCTION; ONLINE SYSTEM; PRIORITY JOURNAL; QUALITY CONTROL;

ALGORITHMS; BAYES THEOREM; GENE EXPRESSION PROFILING; HUMANS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS;

EID: 84878653850     PISSN: 03051048     EISSN: 13624962     Source Type: Journal    
DOI: 10.1093/nar/gkt229     Document Type: Article

References (37)

1. Parkinson, H., Sarkans, U., Kolesnikov, N., Abeygunawardena, N., Burdett, T., Dylag, M., Emam, I., Farne, A., Hastings, E., Holloway, E. et al. (2010) ArrayExpress update-an archive of microarray and high-throughput sequencing-based functional genomics experiments. Nucleic Acids Res., 39, D1002-D1004.
2. Barrett, T., Suzek, T.O., Troup, D.B., Wilhite, S.E., Ngau, W.C., Ledoux, P., Rudnev, D., Lash, A.E., Fujibuchi, W and Edgar, R. (2005) NCBI GEO: Mining millions of expression profiles - database and tools. Nucleic Acids Res., 33, D562-D566.
3. Kilpinen, S., Autio, R., Ojala, K., Iljin, K., Bucher, E., Sara, H., Pisto, T., Saarela, M., Skotheim, R.I., Bjorkman, M. et al. (2008) Systematic bioinformatic analysis of expression levels of 17 330 human genes across 9783 samples from 175 types of healthy and pathological tissues. Genome Biol., 9, R139.
4. Rudy, J. and Valafar, F. (2011) Empirical comparison of cross-platform normalization methods for gene expression data. BMC Bioinformatics, 12, 467.
5. Lukk, M., Kapushesky, M., Nikkilä, J., Parkinson, H., Goncalves, A., Huber, W., Ukkonen, E. and Brazma, A. (2010) A global map of human gene expression. Nat. Biotech., 28, 322-324.
6. Rung, J. and Brazma, A. (2013) Reuse of public genome-wide gene expression data. Nat. Rev. Genet., 14, 89-99.
7. Lockhart, D., Dong, H., Byrne, M., Follettie, M., Gallo, M., Chee, M., Mittmann, M., Wang, C., Kobayashi, M., Horton, H. et al. (1996) Expression monitoring by hybridization to highdensity oligonucleotide arrays. Nat. Biotech., 14, 1675-1680.
8. Brodie, E., DeSantis, T., Parker, J., Zubietta, I., Piceno, Y. and Andersen, G. (2007) Urban aerosols harbor diverse and dynamic bacterial populations. Proc. Natl Acad. Sci. USA, 104, 299-304.
9. Rajilić-Stojanović, M., Heilig, H.G.H.J., Molenaar, D., Kajander, K., Surakka, A., Smidt, H. and deVos, W.M. (2009) Development and application of the human intestinal tract chip, a phylogenetic microarray: Analysis of universally conserved phylotypes in the abundant microbiota of young and elderly adults. Environ. Microbiol., 11, 1736-1751.
10. Nikkilä, J. and de Vos, W.M. (2010) Advanced approaches to characterize the human intestinal microbiota by computational meta-analysis. J. Clin. Gastroent., 44(Suppl. 1), S2-S5.
11. Kohane, I. and Valtchinov, V.I. (2012) Quantifying the white blood cell transcriptome as an accessible window to the multiorgan transcriptome. Bioinformatics, 28, 538-545.
12. Schmid, P., Palmer, N., Kohane, I. and Berger, B. (2012) Making sense out of massive data by going beyond differential expression. Proc. Natl Acad. Sci. USA, 109, 5594-5599.
13. Zheng-Bradley, X., Rung, J., Parkinson, H. and Brazma, A. (2010) Large scale comparison of global gene expression patterns in human and mouse. Genome Biol., 11, R124.
14. Bolstad, B.M., Irizarry, R.A., Astrand, M. and Speed, T.P. (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics, 19, 185-193.
15. Irizarry, R.A., Bolstad, B.M., Collin, F., Cope, L.M., Hobbs, B. and Speed, T.P. (2003) Summaries of affymetrix genechip probe level data. Nucleic Acids Res., 31, e15.
16. Wu, Z. and Irizarry, R. (2004) Stochastic models inspired by hybridization theory for short oligonucleotide arrays. In: Bourne, P.E. and Gusfield, D. (eds), In: Proceedings of the 8th Annual International Conference on Computational Molecular Biology (RECOMB'04). ACM Press, New York, pp. 98-106.
17. Li, C. and Wong, W.H. (2001) Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection. Proc. Natl Acad. Sci. USA, 98, 31-36.
18. Affymetrix Inc. (2005) Guide to probe logarithmic intensity error (PLIER) estimation. Technical note. Affymetrix Inc., Santa Clara, CA, USA.
19. Katz, S., Irizarry, R.A., Lin, X., Tripputi, M. and Porter, M.W. (2006) A summarization approach for affymetrix GeneChip data using a reference training set from a large, biologically diverse database. BMC Bioinformatics, 7, 464.
20. McCall, M.N., Bolstad, B.M. and Irizarry, R.A. (2010) Frozen robust multiarray analysis (fRMA). Biostatistics, 11, 242-253.
21. McCall, M.N. and Irizarry, R.A. (2011) Thawing frozen robust multi-array analysis (fRMA). BMC Bioinformatics, 12, 369.
22. McCall, M.N., Jaffee, H.A. and Irizarry, R.A. (2012) fRMA ST: Frozen robust multiarray analysis for affymetrix exon and gene ST arrays. Bioinformatics, 28, 3153-3154.
23. Lahti, L., Elo, L.L., Aittokallio, T. and Kaski, S. (2011) Probabilistic analysis of probe reliability in differential gene expression studies with short oligonucleotide arrays. IEEE Tr. Comp. Biol. Bioinf., 8, 217-225.
24. Schmidberger, M., Vicedo, E. and Mansmann, U. (2009) affyPara-a bioconductor package for parallelized preprocessing algorithms of affymetrix microarray data. Bioinform. Biol. Insights, 3, 83-87.
25. Cope, L.M., Irizarry, R.A., Jaffee, H.A., Wu, Z. and Speed, T.P. (2004) A benchmark for affymetrix GeneChip expression measures. Bioinformatics, 20, 323-331.
26. Gelman, A., Carlin, J.B., Stern, H.S. and Rubin, D.B. (2003) Bayesian Data Analysis, 2nd edn. Chapman & Hall/CRC, Boca Raton, FL.
27. Goldfarb, D. (1970) A family of variable-metric methods derived by variational means. Math. Comput., 24, 23-26.
28. Irizarry, R.A., Hobbs, B., Collin, F., BeazerBarclay, Y.D., Antonellis, K.J., Scherf, U. and Speed, T.P. (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics., 4, 249-264.
29. Xing, Y., Kapur, K. and Wong, W.H. (2006) Probe selection and expression index computation of affymetrix exon arrays. PLoS One, 1, e88.
30. Dai, M., Wang, P., Boyd, A.D., Kostov, G., Athey, B., Jones, E.G., Bunney, W.E., Myers, R.M., Speed, T.P., Akil, H. et al. (2005) Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res., 33, e175.
31. Hochreiter, S., Clevert, D.A. and Obermayer, K. (2006) A new summarization method for affymetrix probe level data. Bioinformatics, 22, 943-949.
32. Breiman, L. (2001) Random forests. Mach. Learn., 45, 5-32.
33. Elo, L.L., Lahti, L., Skottman, H., Kyläniemi, M., Lahesmaa, R. and Aittokallio, T. (2005) Integrating probe-level expression changes across generations of Affymetrix arrays. Nucleic Acids Res., 33, e193.
34. Su, A.I., Wiltshire, T., Batalov, S., Lapp, H., Ching, K.A., Block, D., Zhang, J., Soden, R., Hayakawa, M., Kreiman, G. et al. (2004) A gene atlas of the mouse and human protein-encoding transcriptomes. Proc. Natl Acad. Sci. USA, 101, 6062-6067.
35. Salonen, A., Salojärvi, J., Lahti, L. and de Vos, W.M. (2012) The adult intestinal core microbiota is determined by analysis depth and health status. Clin. Microb. Inf., 18, 16-20.
36. Chen, C., Grennan, K., Badner, J., Zhang, D., Gershon, E., Jin, L. and Liu, C. (2011) Removing batch effects in analysis of expression microarray data: An evaluation of six batch adjustment methods. PLoS One, 6, e17238.
37. Leek, J.T., Johnson, W.E., Parker, H.S., Jaffe, A.E. and Storey, J.D. (2012) The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics, 28, 882-883.