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  • Poster presentation
  • 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:

Keywords

  • Small Sample Size
  • Pairwise Comparison
  • Gene Pattern
  • Response Pattern
  • Gene Response

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, Memphis, TN 38152, USA

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

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