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A Zipf-plot based normalization method for high-throughput RNA-seq data

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  • Bin Wang

Abstract

Normalization is crucial in RNA-seq data analyses. Due to the existence of excessive zeros and a large number of small measures, it is challenging to find reliable linear rescaling normalization parameters. We propose a Zipf plot based normalization method (ZN) assuming that all gene profiles have similar upper tail behaviors in their expression distributions. The new normalization method uses global information of all genes in the same profile without gene-level expression alteration. It doesn’t require the majority of genes to be not differentially expressed (DE), and can be applied to data where the majority of genes are weakly or not expressed. Two normalization schemes are implemented with ZN: a linear rescaling scheme and a non-linear transformation scheme. The linear rescaling scheme can be applied alone or together with the non-linear normalization scheme. The performance of ZN is benchmarked against five popular linear normalization methods for RNA-seq data. Results show that the linear rescaling normalization scheme by itself works well and is robust. The non-linear normalization scheme can further improve the normalization outcomes and is optional if the Zipf plots show parallel patterns.

Suggested Citation

  • Bin Wang, 2020. "A Zipf-plot based normalization method for high-throughput RNA-seq data," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-15, April.
    • Handle: RePEc:plo:pone00:0230594
    DOI: 10.1371/journal.pone.0230594
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    3. Farnoosh Abbas-Aghababazadeh & Qian Li & Brooke L Fridley, 2018. "Comparison of normalization approaches for gene expression studies completed with high-throughput sequencing," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-21, October.
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    Cited by:

    1. Chen Ge & Shu-Guang Zhang & Bin Wang, 2020. "Modeling the joint distribution of firm size and firm age based on grouped data," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-19, July.

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