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Aggregation Effect in Microarray Data Analysis

  • Linlin Chen
  • Anthony AlmudevarEmail author
  • Lev KlebanovEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 972)

Abstract

Inferring gene regulatory networks from microarray data has become a popular activity in recent years, resulting in an ever-increasing volume of publications. There are many pitfalls in network analysis that remain either unnoticed or scantily understood. A critical discussion of such pitfalls is long overdue. Here we discuss one feature of microarray data the investigators need to be aware of when embarking on a study of putative associations between elements of networks and pathways.

Key words

Microarray data Networks Pathways Genetic pathways 

Notes

Acknowledgement

The study was supported by Grant MSM 0021620839 of the Ministry of Education, Czech Republic.

References

  1. 1.
    Chu T, Glymour C, Scheines R, Spirtes P (2003) A statistical problem for inference to regulatory structure from associations of gene expression measurements with microarrays. Bioinformatics 19:1147–1152PubMedCrossRefGoogle Scholar
  2. 2.
    Klebanov L, Yakovlev A (2007) How high is the level of technical noise in microarray data? Biol Direct 2:9PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.School of Mathematical SciencesRochester Institute of TechnologyRochesterUSA
  2. 2.Department of Biostatistics and Computational BiologyUniversity of RochesterRochesterUSA
  3. 3.Department of Probability and StatisticsCharles University PraguePragueCzech Republic

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