scholarly journals Modeling time-series data from microbial communities

2016 ◽  
Author(s):  
Benjamin J Ridenhour ◽  
Sarah L Brooker ◽  
Janet E Williams ◽  
James T Van Leuven ◽  
Aaron W Miller ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Amplicon Sequencing ◽  
Series Data ◽  
Ecological Interactions ◽  
Sequencing Technologies ◽  
Ecological Understanding ◽  
Plant Secondary Compound ◽  
16S Rdna Amplicon Sequencing ◽  
Microbiome Data

AbstractAs sequencing technologies have advanced, the amount of information regarding the composition of bacterial communities from various environments (e.g. skin, soil) has grown exponentially. To date, most work has focused on cataloging taxa present in samples and determining whether the distribution of taxa shifts with exogenous covariates. However, important questions regarding how taxa interact with each other and their environment remain open, thus preventing in-depth ecological understanding of microbiomes. Time-series data from 16S rDNA amplicon sequencing are becoming more common within microbial ecology, but given the ‘big data’ nature of these studies, there are currently no methods capable of utilizing the breadth of the data to infer ecological interactions from these longitudinal data. We address this gap by presenting a method of analysis using Poisson regression fit with an elastic-net penalty that 1) takes advantage of the fact that the data are time series; 2) constrains estimates to allow for the possibility of many more interactions than data; and 3) is scalable enough to handle data consisting of thousands of taxa. We test the method on gut microbiome data from white-throated woodrats (Neotoma albigula) that were fed varying amounts of the plant secondary compound oxalate over a period of 22 days to estimate interactions between OTUs and their environment.

ESTIMATING COMMUNITY STABILITY AND ECOLOGICAL INTERACTIONS FROM TIME-SERIES DATA

2003 ◽  
Vol 73 (2) ◽  
pp. 301-330 ◽  
Author(s):  
A. R. Ives ◽  
B. Dennis ◽  
K. L. Cottingham ◽  
S. R. Carpenter
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Ecological Interactions ◽  
Community Stability

scholarly journals Identification and visualisation of differential isoform expression in RNA-seq time series

2017 ◽  
Author(s):  
María José Nueda ◽  
Jordi Martorell-Marugan ◽  
Cristina Martí ◽  
Sonia Tarazona ◽  
Ana Conesa
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Expression Patterns ◽  
R Package ◽  
Series Data ◽  
Rna Seq ◽  
Sequencing Technologies ◽  
Isoform Expression ◽  
Time Series Data Analysis ◽  
Differential Isoform Expression

AbstractAs sequencing technologies improve their capacity to detect distinct transcripts of the same gene and to address complex experimental designs such as longitudinal studies, there is a need to develop statistical methods for the analysis of isoform expression changes in time series data. Iso-maSigPro is a new functionality of the R package maSigPro for transcriptomics time series data analysis. Iso-maSigPro identifies genes with a differential isoform usage across time. The package also includes new clustering and visualization functions that allow grouping of genes with similar expression patterns at the isoform level, as well as those genes with a shift in major expressed isoform. The package is freely available under the LGPL license from the Bioconductor web site (http://bioconductor.org).

scholarly journals Reverse-engineering ecological theory from data

2018 ◽  
Vol 285 (1878) ◽  
pp. 20180422 ◽  
Author(s):  
Benjamin T. Martin ◽  
Stephan B. Munch ◽  
Andrew M. Hein
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Theory Development ◽  
Theoretical Models ◽  
Series Data ◽  
Ecological Interactions ◽  
Dynamical Equations ◽  
Ecological Processes ◽  
Functional Forms ◽  
Dynamics Of Populations

Ecologists have long sought to understand the dynamics of populations and communities by deriving mathematical theory from first principles. Theoretical models often take the form of dynamical equations that comprise the ecological processes (e.g. competition, predation) believed to govern system dynamics. The inverse of this approach—inferring which processes and ecological interactions drive observed dynamics—remains an open problem in ecology. Here, we propose a way to attack this problem using a machine learning method known as symbolic regression, which seeks to discover relationships in time-series data and to express those relationships using dynamical equations. We found that this method could rapidly discover models that explained most of the variance in three classic demographic time series. More importantly, it reverse-engineered the models previously proposed by theoretical ecologists to describe these time series, capturing the core ecological processes these models describe and their functional forms. Our findings suggest a potentially powerful new way to merge theory development and data analysis.

scholarly journals BiomeHorizon: visualizing microbiome time series data in R

2021 ◽  
Author(s):  
Isaac Fink ◽  
Richard J. Abdill ◽  
Ran Blekhman ◽  
Laura Grieneisen
Keyword(s):  
Time Series ◽  
Open Source ◽  
Time Series Data ◽  
R Package ◽  
Supplementary Information ◽  
Series Data ◽  
Link Type ◽  
Microbiome Research ◽  
Microbiome Data ◽  
Over Time

AbstractSummaryA key aspect of microbiome research is analysis of longitudinal dynamics using time series data. A method to visualize both the proportional and absolute change in the abundance of multiple taxa across multiple subjects over time is needed. We developed BiomeHorizon, an open-source R package that visualizes longitudinal compositional microbiome data using horizon plots.Availability and ImplementationBiomeHorizon is available at https://github.com/blekhmanlab/biomehorizon/ and released under the MIT license. A guide with step-by-step instructions for using the package is provided at https://blekhmanlab.github.io/biomehorizon/. The guide also provides code to reproduce all plots in this [email protected], [email protected], [email protected] informationNone

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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