PyIOmica: longitudinal omics analysis and trend identification

Domanskyi, Sergii; Piermarocchi, Carlo; Mias, George I.

doi:10.1093/bioinformatics/btz896

Cited by 13 publications

(26 citation statements)

References 12 publications

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“…We then constructed the paired difference time series ∆, where for each timepoint i for each gene α, ∆ αSal (t i ) = Sal 2α (t i ) − Sal 1α (t i ). We carried out a categorization into groups and subgroups of gene expression based on these data (see online Python notebook, and previous discussion using PyIOmica (Domanskyi et al, 2019;Mias et al, 2016;Mias and Zheng, 2020)). For a given subgroup of genes, we constructed the mean time series across the members of this subgroup.…”

Section: Experimental Biological Time-series Applicationsmentioning

confidence: 99%

“…The rapidly increasing availability of biological time series requires new methods to integrate different types of data, analyze them, and interpret the results in a fast and informative way. Many platforms for multi-biological and multi-omics data integration have been developed, including software such as DAVID (Sherman et al, 2009), Galaxy (Giardine et al, 2005) and GenePattern (Reich et al, 2006), our recent frameworks MathIOmica (Mias et al, 2016) and PyIOmica (Domanskyi et al, 2019), which incorporate time-series categorization, and many more. Network-based methods have been shown to be effective in transforming time series into graph objects and capturing their characteristics, potentially allowing for faster learning approaches (Yang and Yang, 2008;Zou et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Our comparison of our method to traditional community detection methods, such as Girvan-Newman (Girvan and Newman, 2002) and Louvain (Blondel et al, 2008), indicated that our approach is more suitable for VGs. To show that our method can capture biological processes we also applied it to several experimentally obtained time series, longitudinal multi-omics data from blood components in prediabetics (cytokines, glucose and haemoglobin A1c) (Zhou et al, 2019), saliva omics data (mean gene expression) (Domanskyi et al, 2019) and signals from wearable biosensors (radiation exposure) (Li et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…The methods of building WDPVG and visibility graph based community detection are available as a module of the open source Python package PyIOmica (Domanskyi et al, 2019). The dataset and codes we used as described below are available with a Python notebook, publicly available online at https://doi.org/10.5281/zenodo.3693984.…”

Section: Introductionmentioning

confidence: 99%

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Visibility Graph Based Community Detection for Biological Time Series

Min

Domanskyi

Piermarocchi

et al. 2020

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MotivationTemporal behavior is an essential aspect of all biological systems. Time series have been previously represented as networks. Such representations must address two fundamental problems: (i) How to create the appropriate network to reflect the characteristics of biological time series. (ii) How to detect characteristic temporal patterns or events as network communities. General methods to detect communities have used metrics to compare the connectivity within a community to the connectivity one would expect in a random model, or assumed a known number of communities, or are based on the betweenness centrality of edges or nodes. However, such methods were not specifically designed for network representations of time series. We introduce a visibility-graph-based method to build networks from different kinds of biological time series and detect temporal communities within these networks.ResultsTo characterize the uneven sampling of typical experimentally obtained biological time series, and simultaneously capture events associated to peaks and troughs, we introduce the Weighted Dual-Perspective Visibility Graph (WDPVG) for time series. To detect communities, we first find the shortest path of the network between start and end nodes to identify nodes which have high intensities. This identifies the main stem of our community detection algorithm. Then, we aggregate nodes outside the shortest path to the nodes found on the main stem based on the closest path length. Through simulation, we demonstrate the validity of our method in detecting community structures on various networks derived from simulated time series. We also confirm its effectiveness in revealing temporal communities in experimental biological time series. Our results suggest our method of visibility graph based community detection can be effective in detecting temporal biological patterns.AvailabilityThe methods of building WDPVG and visibility graph based community detection are available as a module of the open source Python package PyIOmica (https://doi.org/10.5281/zenodo.3691912) with documentation at https://pyiomica.readthedocs.io/en/latest/. The dataset and codes we used in this manuscript are publicly available at https://doi.org/10.5281/zenodo.3693984.Contactgmias@msu.edu

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Section: Experimental Biological Time-series Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Visibility Graph Based Community Detection for Biological Time Series

Min

Domanskyi

Piermarocchi

et al. 2020

Preprint

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“…One way to address the periodic nature of biological functions is to perform temporal studies where multiple samples are collected per participant. Several methods have been developed to discover and describe periodicity in systems and gene expression networks when time data or cell cycle phase is available [3][4][5].The downside of this approach is that without a priori knowing the frequency of the cycle one does not know the number of samples necessary to determine the periodicity of the function. Additionally, if the periodicity is short (< day) then the researcher must collect many samples in a day.…”

Section: Introductionmentioning

confidence: 99%

The Phase Shift Correlation function uncovers periodic Ulcerative Colitis microbiome relationships without temporal sampling

Brown

Arlova

Gillevet

2020

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AbstractCorrelation analysis is a fundamental technique to determine potential relationships within biological processes. However, many biological processes have been shown to function in a periodic manner. When modeling correlations, the fluctuations that are associated with periodicity cause significant issues. We have implemented a Phase Shift Correlation (PSC) algorithm, with a corresponding value PSCrho, to address the periodicity and phase variance associated with features that vary with the same frequency -- but are phase shifted. The phase shift could well indicate causality with one feature’s quantitative change leading to the change in the other feature.We applied the PSC algorithm to Ulcerative Colitis (UC) microbiome data and compared the resulting feature relationships with the equivalent Spearman correlation function results. PSC located many instances of higher phase shifted correlations, where the corresponding Pearson correlation was low.

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