用于分析 miRNA-seq 原始读数的重尾模型。

IF 0.9 4区 数学 Q3 Mathematics
Annika Krutto, Therese Haugdahl Nøst, Magne Thoresen
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引用次数: 0

摘要

本文探讨了现有统计模型在分析和解释从零到数百万的高倾斜 miRNA-seq 原始读数数据时存在的局限性。本文提出了一种使用离散稳定分布的重尾模型,作为更好地捕捉 miRNA-seq 数据中常见的异质性和极端值的新方法。此外,还提出了离散稳定分布的参数作为差异表达分析的替代目标。提供了计算和估计离散稳定分布的 R 软件包。提出的模型适用于挪威妇女与癌症研究(NOWAC)和癌症基因组图谱(TCGA)数据库中的 miRNA-seq 原始计数。拟合优度与常用的泊松分布和负二项分布进行了比较,发现离散稳定分布对这两个数据集的拟合效果更好。总之,离散稳定分布的使用有可能为潜在的生物过程建立更准确的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A heavy-tailed model for analyzing miRNA-seq raw read counts.

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.

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来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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