Exploiting the bootstrap method to analyze patterns of gene expression
BMC Bioinformatics volume 15, Article number: P19 (2014)
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].
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.
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.
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This work is partly supported by NSF CCF-1320297.
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Vo, N.S., Phan, V. Exploiting the bootstrap method to analyze patterns of gene expression. BMC Bioinformatics 15 (Suppl 10), P19 (2014). https://doi.org/10.1186/1471-2105-15-S10-P19