Frequency-independent biological signal identification (FIBSI): A free program that simplifies intensive analysis of non-stationary time series data

AbstractExtracting biological signals from non-linear, dynamic and stochastic experimental data can be challenging, especially when the signal is non-stationary. Many currently available methods make assumptions about the data structure (e.g., signal is periodic, sufficient recording time) and modify the raw data in pre-processing using filters and/or transformations. With an agnostic approach to biological data analysis as a goal, we implemented a signal detection algorithm in Python that quantifies the dimensional properties of waveform deviations from baseline via a running fit function. We call the resulting free program frequency-independent biological signal identification (FIBSI). We demonstrate the utility of FIBSI on two disparate types of experimental data: in vitro whole-cell current-clamp electrophysiological recordings of rodent sensory neurons (i.e., nociceptors) and in vivo fluorescence image time-lapse movies capturing gastrointestinal motility in larval zebrafish. In rodent nociceptors, depolarizing fluctuations in membrane potential are irregular in shape and difficult to distinguish from noise. Using FIBSI, we determined that nociceptors from naïve mice generate larger, more frequent fluctuations compared to naïve rats, suggesting species-specific specializations in rodent nociceptors. In zebrafish, measuring gut motility is a useful tool for addressing developmental and disease-related mechanisms associated with gut function. However, available methods are laborious, technically complex, and/or not cost-effective. We developed and tested a novel assay that can characterize intestinal peristalsis using imaging time series datasets. We used FIBSI to identify muscle contractions in the fluorescence signals and compared their frequencies in unfed and fed larvae. Additionally, FIBSI allowed us to discriminate between peristalsis and oscillatory sphincter-like movements in functionally distinct gut segments (foregut, midgut, and cloaca). We conclude that FIBSI, which is freely available via GitHub, is widely useful for the unbiased analysis of non-stationary signals and extraction of biologically meaningful information from experimental time series data and can be employed for both descriptive and hypothesis-driven investigations.Author SummaryBiologists increasingly work with large, complex experimental datasets. Those datasets often encode biologically meaningful signals along with background noise that is recorded along with the biological data during experiments. Background noise masks the real signal but originates from other sources, for example from the equipment used to perform the measurements or environmental disturbances. When it comes to analyzing the data, distinguishing between the real biological signals and the background noise can be very challenging. Many existing programs designed to help scientists with this problem are either difficult to use, not freely available, or only appropriate to use on very specific types of datasets. The research presented here embodies our goal of helping others to analyze their data by employing a powerful but novice-friendly program that describes multiple features of biological activity in its raw form without abstract transformations. We show the program’s applicability using two different kinds of biological activity measured in our labs. It is our hope that this will help others to analyze complex datasets more easily, thoroughly, and rigorously.

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Intra-Annual Patterns in the Benthic-Pelagic Fluxes of Dissolved and Particulate Matter

Frontiers in Marine Science ◽

10.3389/fmars.2020.567193 ◽

2020 ◽

Vol 7 ◽

Author(s):

Saskia Rühl ◽

Charlie E. L. Thompson ◽

Ana M. Queirós ◽

Stephen Widdicombe

Keyword(s):

Time Series ◽

Biological Activity ◽

Particulate Matter ◽

Time Series Data ◽

Temporal Variations ◽

Series Data ◽

Downward Flux ◽

Dissolved Matter ◽

Upward Flux

In coastal temperate environments, many processes known to affect the exchange of particulate and dissolved matter between the seafloor and the water column follow cyclical patterns of intra-annual variation. This study assesses the extent to which these individual short term temporal variations affect specific direct drivers of seafloor-water exchanges, how they interact with one another throughout the year, and what the resulting seasonal variation in the direction and magnitude of benthic-pelagic exchange is. Existing data from a multidisciplinary long-term time-series from the Western Channel Observatory, United Kingdom, were combined with new experimental and in situ data collected throughout a full seasonal cycle. These data, in combination with and contextualized by time-series data, were used to define an average year, split into five ‘periods’ (winter, pre-bloom, bloom, post-bloom, and autumn) based around the known importance of pelagic primary production and hydrodynamically active phases of the year. Multivariate analyses were used to identify specific sub-sets of parameters that described the various direct drivers of seafloor-water exchanges. Both dissolved and particulate exchange showed three distinct periods of significant flux during the year, although the specific timings of these periods and the cause-effect relationships to the direct and indirect drivers differed between the two types of flux. Dissolved matter exchange was dominated by an upward flux in the pre-bloom period driven by diffusion, then a biologically induced upward flux during the bloom and an autumn downward flux. The latter was attributable to the interactions of hydrodynamic and biological activity on the seafloor. Particulate matter exchanges exhibited a strongly hydrologically influenced upward flux during the winter, followed by a biologically induced downward flux during the bloom and a second period of downward flux throughout post-bloom and autumn periods. This was driven primarily through interactions between biological activity, and physical and meteorological drivers. The integrated, holistic and quantitative data-based analysis of intra-annual variability in benthic/pelagic fluxes presented in this study in a representative temperate coastal environment, demonstrates not only the various process’ inter-connectivity, but also their relative importance to each other. Future investigations or modeling efforts of similar systems will benefit greatly from the relationships and baseline rules established in this study.

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Evaluating the Within-Host Dynamics of Ranavirus Infection with Mechanistic Disease Models and Experimental Data

Viruses ◽

10.3390/v11050396 ◽

2019 ◽

Vol 11 (5) ◽

pp. 396 ◽

Cited By ~ 1

Author(s):

Joseph R. Mihaljevic ◽

Amy L. Greer ◽

Jesse L. Brunner

Keyword(s):

Experimental Data ◽

Time Series ◽

Immune System ◽

Viral Replication ◽

Time Series Data ◽

Immune Defense ◽

Series Data ◽

System Response ◽

Mechanistic Models ◽

Viral Titer

Mechanistic models are critical for our understanding of both within-host dynamics (i.e., pathogen replication and immune system processes) and among-host dynamics (i.e., transmission). Within-host models, however, are not often fit to experimental data, which can serve as a robust method of hypothesis testing and hypothesis generation. In this study, we use mechanistic models and empirical, time-series data of viral titer to better understand the replication of ranaviruses within their amphibian hosts and the immune dynamics that limit viral replication. Specifically, we fit a suite of potential models to our data, where each model represents a hypothesis about the interactions between viral replication and immune defense. Through formal model comparison, we find a parsimonious model that captures key features of our time-series data: The viral titer rises and falls through time, likely due to an immune system response, and that the initial viral dosage affects both the peak viral titer and the timing of the peak. Importantly, our model makes several predictions, including the existence of long-term viral infections, which can be validated in future studies.

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A SIMPLE TEST FOR HIDDEN PERIODICITY IN TIME SERIES DATA

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127495000211 ◽

1995 ◽

Vol 05 (01) ◽

pp. 265-269 ◽

Cited By ~ 1

Author(s):

MICHAEL ROSENBLUM ◽

JÜRGEN KURTHS

Keyword(s):

Experimental Data ◽

Time Series ◽

Spectral Analysis ◽

Additive Noise ◽

Time Series Data ◽

Series Data ◽

Simple Test ◽

Data Set ◽

Dynamical Characteristics

We would like to draw the attention of specialists in time series analysis to a simple but efficient algorithm for the determination of hidden periodic regimes in complex time series. The algorithm is stable towards additive noise and allows one to detect periodicity even if the examined data set contains only a few periods. In such cases it is more suitable than other techniques, such as spectral analysis or recurrence map. We recommend the use of this test prior to the evaluation of attractor dimensions and other dynamical characteristics from experimental data.

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Inference of gene regulatory networks using pseudo-time series data

Bioinformatics ◽

10.1093/bioinformatics/btab099 ◽

2021 ◽

Author(s):

Yuelei Zhang ◽

Xiao Chang ◽

Xiaoping Liu

Keyword(s):

Time Series ◽

Gene Regulatory Networks ◽

Biological Networks ◽

Gene Networks ◽

Regulatory Networks ◽

Time Series Data ◽

Biological Data ◽

Supplementary Information ◽

Series Data ◽

Gene Regulatory

Abstract Motivation Inferring gene regulatory networks (GRNs) from high-throughput data is an important and challenging problem in systems biology. Although numerous GRN methods have been developed, most have focused on the verification of the specific dataset. However, it is difficult to establish directed topological networks that are both suitable for time-series and non-time-series datasets due to the complexity and diversity of biological networks. Results Here, we proposed a novel method, GNIPLR (Gene networks inference based on projection and lagged regression) to infer GRNs from time-series or non-time-series gene expression data. GNIPLR projected gene data twice using the LASSO projection (LSP) algorithm and the linear projection (LP) approximation to produce a linear and monotonous pseudo-time series, and then determined the direction of regulation in combination with lagged regression analyses. The proposed algorithm was validated using simulated and real biological data. Moreover, we also applied the GNIPLR algorithm to the liver hepatocellular carcinoma (LIHC) and bladder urothelial carcinoma (BLCA) cancer expression datasets. These analyses revealed significantly higher accuracy and AUC values than other popular methods. Availabilityand implementation The GNIPLR tool is freely available at https://github.com/zyllluck/GNIPLR. Supplementary information Supplementary data are available at Bioinformatics online.

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TimeCycle: Topology Inspired MEthod for the Detection of Cycling Transcripts in Circadian Time-Series Data

10.1101/2020.11.19.389981 ◽

2020 ◽

Author(s):

Elan Ness-Cohn ◽

Rosemary Braun

Keyword(s):

Time Series ◽

Time Series Data ◽

Detection Method ◽

Persistent Homology ◽

Null Distribution ◽

Algebraic Method ◽

Optimal Choice ◽

Biological Data ◽

Series Data ◽

Topological Features

AbstractThe circadian rhythm drives the oscillatory expression of thousands of genes across all tissues. The recent revolution in high-throughput transcriptomics, coupled with the significant implications of the circadian clock for human health, has sparked an interest in circadian profiling studies to discover genes under circadian control. Here we present TimeCycle: a topology-based rhythm detection method designed to identify cycling transcripts. For a given time-series, the method reconstructs the state space using time-delay embedding, a data transformation technique from dynamical systems. In the embedded space, Takens’ theorem proves that the dynamics of a rhythmic signal will exhibit circular patterns. The degree of circularity of the embedding is calculated as a persistence score using persistent homology, an algebraic method for discerning the topological features of data. By comparing the persistence scores to a bootstrapped null distribution, cycling genes are identified. Results in both synthetic and biological data highlight TimeCycle’s ability to identify cycling genes across a range of sampling schemes, number of replicates, and missing data. Comparison to competing methods highlights their relative strengths, providing guidance as to the optimal choice of cycling detection method.

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Study of Time Series Data Mining for the Real Time Hydrological Forecasting: A Review

International Journal of Computer Applications ◽

10.5120/20692-3581 ◽

2015 ◽

Vol 117 (23) ◽

pp. 6-17 ◽

Cited By ~ 1

Author(s):

Satanand Mishra ◽

C. Saravanan ◽

V. K. Dwivedi

Keyword(s):

Data Mining ◽

Time Series ◽

Real Time ◽

Time Series Data ◽

Series Data ◽

The Real ◽

Time Series Data Mining ◽

Hydrological Forecasting

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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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