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BMC Bioinformatics

Open Access

Exploiting the bootstrap method to analyze patterns of gene expression

BMC Bioinformatics201415(Suppl 10):P19

https://doi.org/10.1186/1471-2105-15-S10-P19

Published: 29 September 2014

Background

High-throughput technologies like microarrays or the recent RNA-Seq provide large amounts of data for gene expression studies. Although there have been diverse methods to design gene-expression experiments and analyze gene-expression data, the prediction of true patterns of gene expression in case of having few samples remains a challenging problem [1, 2].

Materials and methods

We propose a method to predict response patterns of gene expression studies in the case of small sample size using a bootstrap method [3]. Our approach adopts partially order sets (posets) to represent gene patterns, which are determined based on pairwise comparisons [4].

Results

We show that patterns that are not linearly orderable cannot be true patterns of gene response to treatments. From this result, we propose a strategy using bootstrap resampling to infer true responses of non-linearly-orderable patterns. Our experiments showed that this method produced gene lists with more biological functional enrichment than those obtained without bootstrap resampling.

Conclusions

Our method is useful in designing cost-effective experiments with small sample sizes. Researchers can still use a small sample size to determine true patterns for most genes. For highly-variantly expressed genes, their true patterns can be identified using the proposed method.

Declarations

Acknowledgements

This work is partly supported by NSF CCF-1320297.

Authors’ Affiliations

(1)
Department of Computer Science, University of Memphis

References

  1. Yang H, Churchill G: Estimating p-values in small microarray experiments. Bioinformatics. 2007, 23 (1): 38-43. 10.1093/bioinformatics/btl548.View ArticlePubMedGoogle Scholar
  2. Glaus P, Honkela A, Rattray M: Identifying differentially expressed transcripts from rna-seq data with biological variation. Bioinformatics. 2012, 28 (13): 1721-1728. 10.1093/bioinformatics/bts260.PubMed CentralView ArticlePubMedGoogle Scholar
  3. Davison AC, Hinkey DV: Bootstrap methods and their application. Cambridge Series in Statistical and Probabilistic Mathematics. 1997, New York: Cambridge University Press, 1:Google Scholar
  4. Vo N, Phan V: Exploiting dependencies of patterns in gene expression analysis using pairwise comparisons. Lecture Notes in Computer Science, Bioinformatics Research and Applications. Edited by: Cai Z, Eulenstein O, Janies D, Schwartz D. 2013, Berlin: Springer, 7875: 173-184. 10.1007/978-3-642-38036-5_19.View ArticleGoogle Scholar

Copyright

© Vo and Phan; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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