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

Annika Krutto ¹, Therese Haugdahl Nøst ^2,3, Magne Thoresen ¹

⁰Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway.

Statistical Applications in Genetics and Molecular Biology +

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May 29, 2024

View abstract on PubMed

Summary

This study introduces a novel heavy-tailed model using discrete stable distributions to analyze highly skewed microRNA sequencing (miRNA-seq) read count data. This approach offers improved accuracy for differential expression analysis and better modeling of biological processes.

Area Of Science

Bioinformatics
Statistical Genetics
Genomics

Background

MicroRNA sequencing (miRNA-seq) data exhibit high skewness and zero counts, posing challenges for standard statistical models.
Existing models often fail to adequately capture the heterogeneity and extreme values in miRNA-seq raw read counts.

Purpose Of The Study

To propose a novel statistical approach for analyzing highly skewed miRNA-seq raw read count data.
To introduce discrete stable distributions as a superior alternative to traditional models for miRNA-seq data.
To offer parameters of the discrete stable distribution as a new target for differential expression analysis.

Main Methods

Development and application of a heavy-tailed model based on discrete stable distributions.
Creation of an R package for computing and estimating discrete stable distributions.
Comparative analysis of model goodness-of-fit against Poisson and negative binomial distributions using real-world datasets.

Main Results

Discrete stable distributions provide a significantly better fit to miRNA-seq raw counts compared to Poisson and negative binomial distributions.
The proposed model effectively captures data heterogeneity and extreme values prevalent in miRNA-seq datasets.
Application to Norwegian Women and Cancer Study (NOWAC) and Cancer Genome Atlas (TCGA) data demonstrates superior performance.

Conclusions

Discrete stable distributions offer a more accurate and robust method for modeling miRNA-seq raw read count data.
This novel approach has the potential to enhance the understanding of underlying biological processes through improved statistical analysis.
The developed R package facilitates the practical implementation of discrete stable distributions in miRNA-seq research.

Keywords:

TCGA breast cancer discrete stable distributions extremes lung cancer miRNA-seq raw read counts

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