PyIOmica: longitudinal omics analysis and trend identification
Open accessAbstract
Summary: PyIOmica is an open-source Python package focusing on integrating longitudinal multiple omics datasets, characterizing and categorizing temporal trends. The package includes multiple bioinformatics tools including data normalization, annotation, categorization, visualization and enrichment analysis for gene ontology terms and pathways. Additionally, the package includes an implementation of visibility graphs to visualize time series as networks. Availability and implementation: PyIOmica is implemented as a Python package (pyiomica), available for download and installation through the Python Package Index (https://pypi.python.org/pypi/pyiomica), and can be deployed using the Python import function following installation. PyIOmica has been tested on Mac OS X, Unix/Linux and Microsoft Windows. The application is distributed under an MIT license. Source code for each release is also available for download on Zenodo (https://doi.org/10.5281/zenodo.3548040). Supplementary information: Supplementary data are available at Bioinformatics
AI snapshot
Written from the paper’s full text.
TL;DR
PyIOmica is an open-source Python package for longitudinal multi-omics analysis, temporal trend categorization, visualization, and enrichment. It adapts MathIOmica functionality to Python and adds graphical and network-based tools such as visibility graphs.
Key findings
- PyIOmica provides an automated pipeline for longitudinal omics data, including normalization, annotation, clustering, temporal trend categorization, visualization, and GO/KEGG enrichment.
- Temporal trends are identified using periodograms and autocorrelations based on a Lomb-Scargle transformation, which accounts for missing points and unevenly sampled data.
- The package implements visibility graphs to represent time series as networks; in the example, RNA-sequencing gene expression from a 24-h time series was clustered into two groups based on autocorrelations, and Group 1, Subgroup 2 containing 191 genes was visualized as a visibility graph.
- PyIOmica is distributed as a Python package (`pyiomica`) under an MIT license, installable via PyPI, and tested on Mac OS X, Unix/Linux, and Microsoft Windows.
Methods
- Design: open-source Python software package with a single module containing functions for preprocessing, post-processing, clustering, visualization, visibility graph generation, and enrichment analysis.
- Data/example: an illustrative RNA-sequencing gene expression dataset from a 24-h time series, clustered into two autocorrelation-based groups; one subgroup with 191 genes was visualized as a circular-layout visibility graph.
- Main analysis: periodogram and autocorrelation calculations using a Lomb-Scargle transformation to identify dominant frequencies and temporal dependencies, followed by clustering and GO/KEGG pathway enrichment for non-temporal data and automatically categorized time-series clusters.
Limitations
- The paper states no limitations.
- Evident from the text: as an application note, it reports no quantitative performance metrics, benchmarking against other tools, or validation beyond the illustrative 24-h RNA-sequencing example.
- The authors anticipate future versions to expand genomic, differential omics, and graph construction/characterization tools, indicating current scope is limited to the described functionality.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
5 of 5 cited sources checked · 3 supported · 0 flagged · accuracy not yet measured
- Premisecitation
Temporal trends are automatically discovered using periodogram and autocorrelation calculations based on a Lomb-Scargle transformation algorithm, which properly accounts for missing points and/or unevenly sampled data.
“Temporal trends are automatically discovered using periodogram and autocorrelation calculations based on a Lomb-Scargle transformation algorithm (Mias et al., 2016), which properly accounts for missing points and/or unevenly sampled data.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Mias et al., 2016 source
- Interpretationreasoning
A biological event deviating from baseline is likely to appear in one or more nodes within the shortest path.
“A biological event deviating from baseline is likely to appear in one or more nodes within the shortest path.”
Quote in the paperSubstantial claim
- May combine two claims. 11
- Classified as probably not a substantial claim. 25
- The quote only partly states this claim. 1
May combine two claims.
PremisecitationTime series are transformed into networks that conserve their topology, and allow the identification of varying temporal structures.
“Time series are transformed into networks that conserve their topology, and allow the identification of varying temporal structures.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Lacasa et al., 2008 source
Classified as probably not a substantial claim.
PremisecitationRecent work on characterizing complex events focuses on using network/graph methodology that can capture non-linear behavior.
“Recent work on characterizing complex events focuses on using network/graph methodology that can capture non-linear behavior (Lacasa et al., 2008).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Lacasa et al., 2008 source
The Resumen (pp. 1, 6) presents a graph-based method for characterizing time series and shows that graph topology distinguishes periodic, random, and fractal series. It does not establish the broader claim about characterizing complex events or demonstrate that the method captures nonlinear behavior; detecting features of chaotic systems is identified as a subject for future investigation (p. 6). (LLM, conf=0.82)
Experimental — accuracy not yet measured
May combine two claims.
PremisecitationThe resulting visibility graph has characteristics that reflect the equivalent time series temporal structure and can be used to identify trends.
“The resulting visibility graph has characteristics that reflect the equivalent time series temporal structure and can be used to identify trends.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Lacasa et al., 2008 source
The summary (p. 6) says the graph topology conserves time-series structure, supporting the first part of the claim. But the Figure 2 caption (p. 8) says the graph remains unchanged when a linear trend is added, so the paper does not support using the graph to identify such trends. (LLM, conf=0.87)
Experimental — accuracy not yet measured
May combine two claims.
FindingcitationPyIOmica includes algorithms adapted from MathIOmica, now made available as Python open source code, and expands graphical utilities for visualization of categorized temporal data and network representation of time series, extending previous developments to an open-source, community-accessible platform for data science.
“PyIOmica includes optimized new algorithms adapted from MathIOmica (Mias et al., 2016; which runs on the proprietary Mathematica platform), now made available as Python open source code for all users, and additionally expands extensively graphical utilities for visualization of categorized temporal data, and network representation of time series.”
- Also stated in: § 3 Conclusion · plain
“PyIOmica also offers broad bioinformatics functionality, including clustering, visualization and enrichment, and extends previous developments (Mias et al., 2016) to an open-source, community-accessible platform for data science.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Mias et al., 2016 source
- Rests on an assumption: No tools with the functionality of PyIOmica are currently available in Python.
- Also stated in: § 3 Conclusion · plain
May combine two claims. The quote only partly states this claim.
Findingnone givenPyIOmica includes multiple bioinformatics tools including data normalization, annotation, categorization, visualization and enrichment analysis for gene ontology terms and pathways, and offers broad bioinformatics functionality including clustering, visualization and enrichment.
“The package includes multiple bioinformatics tools including data normalization, annotation, categorization, visualization and enrichment analysis for gene ontology terms and pathways.”
- Also stated in: § 3 Conclusion · plain
“PyIOmica also offers broad bioinformatics functionality, including clustering, visualization and enrichment, and extends previous developments (Mias et al., 2016) to an open-source, community-accessible platform for data science.”
Quote in the paperNot a substantial claim- Rests on an assumption: No tools with the functionality of PyIOmica are currently available in Python.
- Also stated in: § 3 Conclusion · plain
May combine two claims.
Findingnone givenPyIOmica is an open-source Python package for integrating longitudinal multiple omics datasets and characterizing and categorizing temporal trends with a streamlined automated pipeline based on spectral analysis.
“PyIOmica is an open-source Python package focusing on integrating longitudinal multiple omics datasets, characterizing and categorizing temporal trends.”
- Also stated in: § 3 Conclusion · plain
“The open source PyIOmica Python package characterizes time series from multiple omics and categorizes temporal trends with a streamlined automated pipeline based on spectral analysis.”
Quote in the paperSubstantial claim- Rests on an assumption: No tools with the functionality of PyIOmica are currently available in Python.
- Also stated in: § 3 Conclusion · plain
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenPyIOmica includes an implementation of visibility graphs to visualize time series as networks.
“Additionally, the package includes an implementation of visibility graphs to visualize time series as networks.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone givenFunction documentation for PyIOmica is embedded in the module, and is easily accessible at runtime (and also at https://pyiomica.readthedocs.io).
“Function documentation is embedded in the module, and is easily accessible at runtime (and also at https://pyiomica.readthedocs.io).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenAn extensive set of PyIOmica pre-processing functions enables filtering low-quality signals, tagging missing or low values, normalization, standardization, merging and comparison of the datasets.
“An extensive set of PyIOmica pre-processing functions enables filtering low-quality signals, tagging missing or low values, normalization, standardization, merging and comparison of the datasets.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenPyIOmica is distributed under an MIT license.
“The application is distributed under an MIT license.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone givenPyIOmica is implemented as a Python package (pyiomica), available for download and installation through the Python Package Index, and can be deployed using the Python import function following installation.
“PyIOmica is implemented as a Python package (pyiomica), available for download and installation through the Python Package Index (https://pypi.python.org/pypi/pyiomica), and can be deployed using the Python import function following installation.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenTemporal events are detected and indicated with solid blue lines encompassing groups of points, or communities.
“Temporal events are detected and indicated with solid blue lines encompassing groups of points, or communities.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Findingnone givenThe periodogram is used to identify each time series' underlying dominant frequencies.
“The periodogram is used to identify each time series’ underlying dominant frequencies.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
FindingreasoningMultiple omics (genes, proteins and metabolites) that show similar trends in time are identified by clustering, and can be biologically evaluated through pathway and GO analyses.
“Multiple omics (genes, proteins and metabolites) that show similar trends in time are identified by clustering, and can be biologically evaluated through pathway and GO analyses.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenThe post-processing functions of PyIOmica, such as temporal trends categorization of power spectrum and spikes, are built on using the SciPy and scikit-learn Python toolkits.
“The post-processing functions, such as temporal trends categorization of power spectrum and spikes, are built on using the SciPy and scikit-learn Python toolkits.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenPyIOmica's codebase is a single Python module containing multiple groups of functions designed for annotations and enumerations, pre- and post-processing, clustering-related purposes, visualizations (heatmaps and categorization), normal and horizontal visibility graphs generation and other core and utility components.
“PyIOmica’s codebase is a single Python module containing multiple groups of functions designed for annotations and enumerations, pre- and post-processing, clustering-related purposes, visualizations (heatmaps and categorization), normal and horizontal visibility graphs generation and other core and utility components.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenAdditional functionality of PyIOmica includes gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for both non-temporal data, as well as for clusters identified through the automated time series categorization.
“Additional functionality includes gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for both non-temporal data, as well as for clusters identified through the automated time series categorization.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe data from Group 1, Subgroup 2 containing 191 genes is visualized in Figure 1b as a visibility graph on a circular layout.
“The data from Group 1, Subgroup 2 containing 191 genes is visualized in Figure 1b as a visibility graph on a circular layout.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Findingnone givenWith PyIOmica, any results can be visualized, exported and analyzed for gene enrichment by means of a user-friendly Python interface.
“With PyIOmica, any results can be visualized, exported and analyzed for gene enrichment by means of a user-friendly Python interface.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
FindingreasoningSignals showing statistically significant trends are identified for downstream analysis.
“Signals showing statistically significant trends are identified for downstream analysis.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenPyIOmica provides a complete workflow for time series processing.
“PyIOmica provides a complete workflow for time series processing, illustrated in the Supplementary Figure S1.”
Quote in the paperNot a substantial claim- Rests on an assumption: No tools with the functionality of PyIOmica are currently available in Python.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Findingnone givenThe modular nature of PyIOmica allows for smooth integration with any future and existing Python tools.
“The modular nature of PyIOmica allows for smooth integration with any future and existing Python tools.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone givenInstallation of PyIOmica is performed using pip install pyiomica, and package dependencies are automatically addressed directly from Python package index (PyPI).
“Installation is simply performed using pip install pyiomica, and package dependencies are automatically addressed directly from Python package index (PyPI).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenSource code for each release of PyIOmica is available for download on Zenodo.
“Source code for each release is also available for download on Zenodo (https://doi.org/10.5281/zenodo.3548040).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenSubgroups were determined from the gene expression in each autocorrelation group.
“Subgroups were determined from the gene expression in each autocorrelation group.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenAutocorrelations are used to identify how measured intensities within each time series may depend on previous measurements, by correlating a time series with delayed versions of itself.
“Autocorrelations are also used to identify how measured intensities within each time series may depend on previous measurements, by correlating a time series with delayed versions of itself.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenPyIOmica uses Matplotlib plotting functions to visualize histograms, dendrograms, heatmaps and visibility graphs.
“PyIOmica uses Matplotlib plotting functions to visualize histograms, dendrograms, heatmaps and visibility graphs.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataFigure 1a shows example RNA-sequencing gene expression data from a 24-h time series, clustered into two groups based on autocorrelations.
“Figure 1a shows example RNA-sequencing gene expression data from a 24-h time series, clustered into two groups based on autocorrelations.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
InterpretationreasoningThe shortest path identifies nodes (i.e. timepoints) that display high intensity, and thus dominate the global signal profile, are robust to noise, and are likely drivers of the global temporal behavior.
“The shortest path identifies nodes (i.e. timepoints) that display high intensity, and thus dominate the global signal profile, are robust to noise, and are likely drivers of the global temporal behavior.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenPyIOmica has been tested on Mac OS X, Unix/Linux and Microsoft Windows.
“PyIOmica has been tested on Mac OS X, Unix/Linux and Microsoft Windows.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
No tools with the functionality of PyIOmica are currently available in Python.
“To our knowledge, there are no tools with the functionality of PyIOmica currently available in Python.”
Cites
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- Authors
- Domanskyi, S., Piermarocchi, C., & Mias, G. I.
- Affiliations
- Michigan State University (US)
- Corresponding author
- Mias, George I
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-11-28
- Volume
- 36
- Issue
- 7
- Pages
- 2306-2307
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC7141865
- OpenAlex
- W2991167490
- Semantic Scholar
- e4df1ebf097b03452b24dfd073e16dc339281012
- Cited by
- 10
- References
- 12
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 3 · 2021: 3 · 2020: 2
- FWCI
- 0.57
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- Funders
- Translational Research Institute for Space Health
- National Aeronautics and Space Administration
- NASA (NNX16AO69A)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- multi-omics integration, time trend analysis, visibility graph, time series visualization, gene ontology enrichment, pathway enrichment, data normalization
- MeSH terms
- Software, Computational Biology, Gene Ontology
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