BiomeHorizon: Visualizing Microbiome Time Series Data in R.
BiomeHorizon: Visualizing Microbiome Time Series Data in R.
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DOI：
10.1128/msystems.01380-21
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发表时间：
2022-06-28
期刊：
影响因子：
6.4
通讯作者：
中科院分区：
文献类型：
--
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Despite playing a key role in the health of their hosts, host-associated microbial communities demonstrate considerable variation over time. These communities comprise thousands of temporally dynamic taxa, which makes visualizing microbial time series data challenging. As such, a method to visualize both the proportional and absolute change in the relative abundance of multiple taxa across multiple subjects over time is needed. To address this gap, we developed BiomeHorizon, the first automated, open-source R package that visualizes longitudinal compositional microbiome data using horizon plots. BiomeHorizon is available at https://github.com/blekhmanlab/biomehorizon/ and a guide with step-by-step instructions for using the package is provided at https://blekhmanlab.github.io/biomehorizon/. IMPORTANCE Host-associated microbiota (i.e., the number and types of bacteria in the body) can have profound impacts on an animal’s day-to-day functioning as well as their long-term health. Recent work has shown that these microbial communities change substantially over time, so it is important to be able to link changes in the abundance of certain microbes with host health outcomes. However, visualizing such changes is difficult because the microbiome comprises thousands of different microbes. To address this issue, we developed BiomeHorizon, an R package for visualizing longitudinal microbiome data using horizon plots. BiomeHorizon accepts a range of data formats and was developed with two common microbiome study designs in mind: human health studies, where the microbiome is sampled at set time points, and observational wildlife studies, where samples may be collected at irregular time intervals. BiomeHorizon thus provides a flexible, user-friendly approach to microbiome time series data visualization and analysis.
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影响因子：
15.5
作者：
Turroni S;Rampelli S;Biagi E;Consolandi C;Severgnini M;Peano C;Quercia S;Soverini M;Carbonero FG;Bianconi G;Rettberg P;Canganella F;Brigidi P;Candela M
通讯作者：
Candela M
DOI：
10.1126/science.1224203
发表时间：
2012-06-08
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Costello EK;Stagaman K;Dethlefsen L;Bohannan BJ;Relman DA
通讯作者：
Relman DA
影响因子：
6.4
作者：
Gruber-Vodicka HR;Seah BKB;Pruesse E
通讯作者：
Pruesse E
影响因子：
3.4
作者：
Barandas, Marilia;Folgado, Duarte;Gamboa, Hugo
通讯作者：
Gamboa, Hugo
影响因子：
12.3
作者：
Flores, Gilberto E.;Caporaso, J. Gregory;Fierer, Noah
通讯作者：
Fierer, Noah