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. 2020 Apr 1;36(7):2306-2307.
doi: 10.1093/bioinformatics/btz896.

PyIOmica: longitudinal omics analysis and trend identification

Sergii Domanskyi  1 , Carlo Piermarocchi  1 , George I Mias  1   2   3
Affiliations

PyIOmica: longitudinal omics analysis and trend identification

Sergii Domanskyi et al. Bioinformatics. .

Abstract

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.

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Figures

Fig. 1.
Fig. 1.
Example PyIOmica data visualization. (a) Dendrogram with heatmap of automatically categorized longitudinal gene expression data. Autocorrelations are used to identify temporal trends in the data. Subgroups are determined based on similar collective behavior over time. (b) Visibility graph of median signal intensity from group G1S2 from (a)
See this image and copyright information in PMC

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