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

Krutto Annika (), Haugdahl Nøst Therese () and Thoresen Magne ()
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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

Statistical Applications in Genetics and Molecular Biology, 2024, vol. 23, issue 1, 30

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.

Keywords: breast cancer; discrete stable distributions; extremes; lung cancer; miRNA-seq raw read counts; TCGA (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/sagmb-2023-0016

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