Compositional data analysis of the microbiome: fundamentals, tools, and challenges

Tsilimigras Matthew C. B.; Fodor Anthony A.

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Compositional data analysis of the microbiome: fundamentals, tools, and challenges

机译：微生物组组成数据分析：基本原理，工具和挑战

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Purpose: Human microbiome studies are within the realm of compositional data with the absolute abundances of microbes not recoverable from sequence data alone. In compositional data analysis, each sample consists of proportions of various organisms with a sum constrained to a constant. This simple feature can lead traditional statistical treatments when naively applied to produce errant results and spurious correlations.

机译：目的：人类微生物组研究属于组成数据领域，其中微生物的绝对丰度无法仅从序列数据中恢复。在成分数据分析中，每个样本都由各种生物的比例组成，其总和被限制为一个常数。如果天真地应用它来产生错误的结果和虚假的相关性，那么这个简单的功能可能会导致传统的统计处理。

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《Annals of epidemiology》 |2016年第5期|共6页
作者
Tsilimigras Matthew C. B.; Fodor Anthony A.;
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正文语种 eng
中图分类流行病学与防疫;
关键词
Metagenomics; Data interpretation; statistical; Microbiota; RNA; Ribosomal; 16S; Statistics as topic; Selection bias; High-throughput nucleotide sequencing;

机译：元基因组学;数据解释;统计;微生物群;RNA;核糖体;16S;以统计学为主题;选择偏倚;高通量核苷酸测序;

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专利

1. Compositional data analysis of the microbiome: fundamentals, tools, and challenges [J] . Tsilimigras Matthew C. B., Fodor Anthony A. Annals of epidemiology . 2016,第5期

机译：微生物组组成数据分析：基本原理，工具和挑战
2. Zero-inflated generalized Dirichlet multinomial regression model for microbiome compositional data analysis [J] . Tang Zheng-Zheng, Chen Guanhua Biostatistics . 2019,第4期

机译：微生物组成数据分析的零充气的广义Dirichlet多项式回归模型
3. Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data [J] . Gloor Gregory B., Reid Gregor Canadian journal of microbiology . 2016,第8期

机译：成分分析：分析微生物组高通量测序数据的有效方法
4. Pre-challenge microbiome composition is associated with improved weight gain in pigs after vaccination with a PRRS MLV vaccine followed by challenge with PRRSVandPCV2b [C] . Annual Meeting of the American Association of Swine Veterinarians . 2018

机译：在用PRRS MLV疫苗接种后，预挑战微生物组成与猪的改善的体重增加有关，然后用PRRSVANDPCV2B攻击
5. Statistical Modeling for High-Dimensional Compositional Data with Applications to the Human Microbiome [D] . Dao, Thy. 2021

机译：高尺寸成分数据与人类微生物组的统计建模
6. Forensic Microbiome Database: A Tool for Forensic Geolocation Meta-Analysis Using Publicly Available 16S rRNA Microbiome Sequencing [O] . Harinder Singh, Thomas Clarke, Lauren Brinkac, 2021

机译：法医微生物数据库：使用公开可用的16S rRNA微生物孔测序的法医地理定位元分析工具
7. Forensic Microbiome Database: A Tool for Forensic Geolocation Meta-Analysis Using Publicly Available 16S rRNA Microbiome Sequencing [O] . Harinder Singh, Thomas Clarke, Lauren Brinkac, 2021

机译：法医微生物数据库：使用公开可用的16S rRNA微生物孔测序的法医地理定位元分析工具

Compositional data analysis of the microbiome: fundamentals, tools, and challenges

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