Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates

ABSTRACT Performance of a bioreactor is affected by complex microbial consortia that regulate system functional processes. Studies so far, however, have mainly emphasized the selective pressures imposed by operational conditions (i.e., deterministic external physicochemical variables) on the microbial community as well as system performance, but have overlooked direct effects of the microbial community on system functioning. Here, using a bioreactor with ammonium as the sole substrate under controlled operational settings as a model system, we investigated succession of the bacterial community after a disturbance and its impact on nitrification and anammox (anaerobic ammonium oxidation) processes with fine-resolution time series data. System performance was quantified as the ratio of the fed ammonium converted to anammox-derived nitrogen gas (N2) versus nitrification-derived nitrate (npNO3−). After the disturbance, the N2/npNO3− ratio first decreased, then recovered, and finally stabilized until the end. Importantly, the dynamics of N2/npNO3− could not be fully explained by physicochemical variables of the system. In comparison, the proportion of variation that could be explained substantially increased (tripled) when the changes in bacterial composition were taken into account. Specifically, distinct bacterial taxa tended to dominate at different successional stages, and their relative abundances could explain up to 46% of the variation in nitrogen removal efficiency. These findings add baseline knowledge of microbial succession and emphasize the importance of monitoring the dynamics of microbial consortia for understanding the variability of system performance. IMPORTANCE Dynamics of microbial communities are believed to be associated with system functional processes in bioreactors. However, few studies have provided quantitative evidence. The difficulty of evaluating direct microbe-system relationships arises from the fact that system performance is affected by convolved effects of microbiota and bioreactor operational parameters (i.e., deterministic external physicochemical forcing). Here, using fine-resolution time series data (daily sampling for 2 months) under controlled operational settings, we performed an in-depth analysis of system performance as a function of the microbial community in the context of bioreactor physicochemical conditions. We obtained statistically evaluated results supporting the idea that monitoring microbial community dynamics could improve the ability to predict system functioning, beyond what could be explained by operational physicochemical variables. Moreover, our results suggested that considering the succession of multiple bacterial taxa would account for more system variation than focusing on any particular taxon, highlighting the need to integrate microbial community ecology for understanding system functioning.

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A novel method to accurately calculate statistical significance of local similarity analysis for high-throughput time series

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2018-0019 ◽

2018 ◽

Vol 17 (6) ◽

Cited By ~ 1

Author(s):

Fang Zhang ◽

Ang Shan ◽

Yihui Luan

Keyword(s):

Time Series ◽

Microbial Community ◽

Permutation Test ◽

Statistical Significance ◽

Theoretical Method ◽

Similarity Analysis ◽

Type I ◽

Local Similarity ◽

Biological Studies ◽

Wide Range

Abstract In recent years, a large number of time series microbial community data has been produced in molecular biological studies, especially in metagenomics. Among the statistical methods for time series, local similarity analysis is used in a wide range of environments to capture potential local and time-shifted associations that cannot be distinguished by traditional correlation analysis. Initially, the permutation test is popularly applied to obtain the statistical significance of local similarity analysis. More recently, a theoretical method has also been developed to achieve this aim. However, all these methods require the assumption that the time series are independent and identically distributed. In this paper, we propose a new approach based on moving block bootstrap to approximate the statistical significance of local similarity scores for dependent time series. Simulations show that our method can control the type I error rate reasonably, while theoretical approximation and the permutation test perform less well. Finally, our method is applied to human and marine microbial community datasets, indicating that it can identify potential relationship among operational taxonomic units (OTUs) and significantly decrease the rate of false positives.

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Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality

Genes ◽

10.3390/genes10030216 ◽

2019 ◽

Vol 10 (3) ◽

pp. 216 ◽

Cited By ~ 3

Author(s):

Dongmei Ai ◽

Xiaoxin Li ◽

Gang Liu ◽

Xiaoyi Liang ◽

Li Xia

Keyword(s):

Time Series ◽

Microbial Community ◽

Granger Causality ◽

Large Scale ◽

Community Dynamics ◽

Time Series Data ◽

Series Data ◽

Association Network ◽

Causal Relationships ◽

Network Analyses

The increasing availability of large-scale time series data allows the inference of microbial community dynamics by association network analysis. However, correlation-based association network analyses are noninformative of causal, mediating and time-dependent relationships between microbial community functional factors. To address this insufficiency, we introduced the Granger causality model to the analysis of a recent marine microbial time series dataset. We systematically constructed a directed acyclic network, representing both internal and external causal relationships among the microbial and environmental factors. We further optimized the network by removing false causal associations using the conditional Granger causality. The final network was visualized as a Granger graph, which was analyzed to identify causal relationships driven by key functional operators in the environment, such as Gammaproteobacteria, which was Granger caused by total organic nitrogen and primary production (p < 0.05 and Q < 0.05).

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EEG-riculture: Sustainability and Butterfly-Effects

10.37281/drcsf/1.1.10 ◽

2020 ◽

Vol 1 (1) ◽

Keyword(s):

Time Series ◽

Cognitive Science ◽

Time Series Data ◽

Series Data ◽

Similarity Analysis ◽

Data Series ◽

Human Beings ◽

Eeg Data ◽

Science Approach ◽

Butterfly Effect

The motivational background of this paper is to shed new light on the phenomena of butterfly effect and sustainability from a scientific-philosophical and mathematical point of view. We aim to reveal the connection between butterfly effect and sustainability by observing the observer him- or herself and exploring the most significant errors of thinking and operation of the subject, while analyzing the peculiarities of the butterfly effect. Our reasoning is based on cognitive science approach, agricultural scientific experiments, and on parallel EEG (electroencephalogram) measurements. The latter, emerged from the research area of Innoria’s Team Flow Research Team, is a completely new methodological approach in the field of cognitive science on the basis of previous comparative behavioral scientific results1, but built up on new technological opportunities and professional standpoint2,3. As a result, we can see a new contexts and define problems in measurement methodology, while researching the interactions of human minds. These EEG measurements are part of an extensive research, which focuses on the identification of the parallel perception of reality and the synchronized perception-reaction relation of human beings. In the philosophy of science approach the butterfly effect is always provided by the observer by using in his/her rationing the indicator 'small' or 'seemingly insignificant', while one finds that the effect is not linearly related to such approximate (quantitative) attributes of the cause. The consequence is unexpectedly, unpredictably large, as compared to the observer's expectations. Therefore, the problem requires a change of perspective, namely, one needs to confer much greater importance to small causes. To discover these causes, we need to explore the mechanism of human observation much more intensively. The mathematical objective of the paper is to demonstrate an explored butterfly-effect process, based on a real, but anonymous parallel measured EEG data asset, where each step is reproducible. The problems that need to be solved are: (i) How can we classify correctly over EEG measurements the personal time series data (raw individual EEG data series with 0.25 second sampling) within the frame of similarity analysis? (ii) How to deal with the butterfly effect? (iii) How to step forward on the theoretical path of chaotic systems4 designated by Edward N. Lorenz? The butterfly-effect is the unexpected difference between the result of a classification based on a given data asset and the result of another classification, based on a data asset, having just one additional record as the input; in this case, we have data at about every 0.25 s, where the used length of the time series can be over 100 or 1000. Differences will be derived by means of ranked inputs – especially in case of data having the same value. Similarity analysis is a typical ranking-oriented modelling scheme, where these special effects can be detected at once, without the need for any further manipulations. Since similarity analysis produces model chains, symmetry-driven similarity analyses can have, as well, butterfly-effects in a consistence-oriented model structure. Sustainability can be regarded as a mathematical issue5, being a dynamic phenomenon. Sustainability may be redefined as a capability of forecasting system behavior. Random-like, not-planned incidents cannot be accepted as sustainable and realized plan values. The most trivial usage of the ‘here and now’ characterized sustainability approach is precision farming and its analogy, the EEG-riculture, as such.

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Analysis of the Turkish Islamic banking sector using CAMEL and Similarity Analysis methods

Acta Oeconomica ◽

10.1556/032.2020.00014 ◽

2020 ◽

Vol 70 (2) ◽

pp. 275-296

Author(s):

József Varga ◽

Gyöngyi Bánkuti ◽

Rita Kovács-Szamosi

Keyword(s):

Time Series ◽

Banking Sector ◽

Time Series Data ◽

Banking System ◽

Series Data ◽

Similarity Analysis ◽

Islamic Banking ◽

Analytic Hierarchy ◽

Policy Makers ◽

Hierarchy Process

AbstractRating the reliability of banks has always been an important practical problem for businesses and the economic policy makers. The best way to do this is the CAMEL analysis. The aim of this paper was to create a bank-rating indicator from the five fields of the CAMEL analysis using two-two indicators for each field for the Turkish Islamic banking system. According to the results of the analysis, we could rank the Turkish Islamic banks. Beside the widespread use of the CAMEL analysis, we applied the Similarity Analysis as a new method. We compared the results from the two methods and came to the conclusion that the CAMEL analysis does not adequately provide a fairly shaded picture about the banks. The Component-based Object Comparison for Objectivity (COCO) method gave us the yearly results in time series form. The comparison of the time series data leads to the problem of deciding about what is more important for us – average, standard deviation or the slope. For handling this problem, we used Analytic Hierarchy Process, which gave weights to these indicators.

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SIMILARITY ANALYSIS OF TIME SERIES DATA BY WISAM

Journal of the Japanese Society of Computational Statistics ◽

10.5183/jjscs1988.19.15 ◽

2006 ◽

Vol 19 (1) ◽

pp. 15-26

Author(s):

Takakazu Mori ◽

Tetsuya Misawa

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

Similarity Analysis ◽

Analysis Of Time Series

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

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

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

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

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.

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