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A heavy-tailed model for analyzing miRNA-seq raw read counts

Author

Listed:
  • Krutto Annika

    (Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway)

  • Haugdahl Nøst Therese

    (Department of Community Medicine, Department of Community Medicine, 8016 UiT The Arctic University of Norway , Tromsø, Norway)

  • Thoresen Magne

    (Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway)

Abstract

This article addresses the limitations of existing statistical models in analyzing and interpreting highly skewed miRNA-seq raw read count data that can range from zero to millions. A heavy-tailed model using discrete stable distributions is proposed as a novel approach to better capture the heterogeneity and extreme values commonly observed in miRNA-seq data. Additionally, the parameters of the discrete stable distribution are proposed as an alternative target for differential expression analysis. An R package for computing and estimating the discrete stable distribution is provided. The proposed model is applied to miRNA-seq raw counts from the Norwegian Women and Cancer Study (NOWAC) and the Cancer Genome Atlas (TCGA) databases. The goodness-of-fit is compared with the popular Poisson and negative binomial distributions, and the discrete stable distributions are found to give a better fit for both datasets. In conclusion, the use of discrete stable distributions is shown to potentially lead to more accurate modeling of the underlying biological processes.

Suggested Citation

  • Krutto Annika & Haugdahl Nøst Therese & Thoresen Magne, 2024. "A heavy-tailed model for analyzing miRNA-seq raw read counts," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 23(1), pages 1-30.
    • Handle: RePEc:bpj:sagmbi:v:23:y:2024:i:1:p:30:n:1001
    DOI: 10.1515/sagmb-2023-0016
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    References listed on IDEAS

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