微生物组计数数据的归一化和差分丰度方法综述

IF 4.4 2区 数学 Q1 STATISTICS & PROBABILITY
Dionne Swift, Kellen Cresswell, Robert Johnson, Spiro C. Stilianoudakis, Xingtao Wei
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引用次数: 9

摘要

近年来,低成本高通量DNA测序技术的发展极大地促进了微生物组的研究。然而,已经有充分的证据表明,观察到的微生物组数据存在组合性、稀疏性和高变异性。所有这些都给分析微生物组数据带来了严峻的挑战。在过去的十年中,人们对解决这些挑战的统计和计算方法产生了相当大的兴趣。推理的选择有助于选择适当的统计方法,因为只有少数方法允许对绝对丰度进行推理,而大多数方法允许对相对丰度进行推理。概述了微生物组数据的差异丰度分析和规范化的最新方法,重点介绍了以前文献中尚未广泛覆盖的方法。在每种方法的详细描述中,我们讨论了假设,以及这些方法是否以及如何解决微生物组数据的挑战。在真实和模拟环境下,对这些方法的精度指标进行了比较。目标是为微生物组数据的差异丰度和规范化提供一套全面但非详尽的潜在和易于获取的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A review of normalization and differential abundance methods for microbiome counts data
The recent development of cost‐effective high‐throughput DNA sequencing technologies has tremendously increased microbiome research. However, it has been well documented that the observed microbiome data suffers from compositionality, sparsity, and high variability. All of which pose serious challenges when analyzing microbiome data. Over the last decade, there has been considerable amount of interest into statistical and computational methods to tackle these challenges. The choice of inference aids in the selection of the appropriate statistical methods since only a few methods allow inferences for absolute abundance while most methods allow inferences for relative abundances. An overview of recent methods for differential abundance analysis and normalization of microbiome data is presented, focusing on methods that are accessible but have not been widely covered in previous literature. In detailed descriptions of each method, we discuss assumptions and if and how these methods address the challenges of microbiome data. These methods are compared based on accuracy metrics in real and simulated settings. The goal is to provide a comprehensive but non‐exhaustive set of potential and easily‐accessible tools for differential abundance and normalization of microbiome data.
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来源期刊
CiteScore
6.20
自引率
0.00%
发文量
31
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