Search engine for discovering works of Art, research articles, and books related to Art and Culture
Javascript must be enabled to continue!
PyIOmica: longitudinal omics analysis and trend identification
View through CrossRef
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
Title: PyIOmica: longitudinal omics analysis and trend identification
Description:
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.
Related Results
PyIOmica: Longitudinal Omics Analysis and Classification
PyIOmica: Longitudinal Omics Analysis and Classification
Abstract
Summary
PyIOmica is an open-source Python package focusing on integrating longitudinal multiple omics datasets, charac...
Benchmarking multi-omics integrative clustering methods for subtype identification in colorectal cancer
Benchmarking multi-omics integrative clustering methods for subtype identification in colorectal cancer
Abstract
Background and objectives
Colorectal cancer (CRC) represents a heterogeneous malignancy that has concerned global burden of incidence and mortality. The tradition...
Exploring the classification of cancer cell lines from multiple omic views
Exploring the classification of cancer cell lines from multiple omic views
Background
Cancer classification is of great importance to understanding its pathogenesis, making diagnosis and developing treatment. The accumulation of extensive o...
Profiling Osteoporosis via Integrated Multi-Omics Technologies
Profiling Osteoporosis via Integrated Multi-Omics Technologies
Background: Osteoporosis is a complex disorder involving bone loss and muscle degeneration. Multi-omics technologies provide novel insights into its molecular mechanisms and may su...
Muon: multimodal omics analysis framework
Muon: multimodal omics analysis framework
AbstractAdvances in multi-omics technologies have led to an explosion of multimodal datasets to address questions ranging from basic biology to translation. While these rich data p...
scAGCI: an anchor graph-based method for cell clustering from integrated scRNA-seq and scATAC-seq data
scAGCI: an anchor graph-based method for cell clustering from integrated scRNA-seq and scATAC-seq data
Abstract
Cell clustering plays a crucial role in the analysis of single-cell multi-omics research. Despite many methods for multi-omics integrated clustering, chall...
Multi-omics Data Integration by Generative Adversarial Network
Multi-omics Data Integration by Generative Adversarial Network
Accurate disease phenotype prediction plays an important role in the treatment of heterogeneous diseases like cancer in the era of precision medicine. With the advent of high throu...
MOPower: an R-shiny application for the simulation and power calculation of multi-omics studies
MOPower: an R-shiny application for the simulation and power calculation of multi-omics studies
Abstract
Background
Multi-omics studies are increasingly used to help understand the underlying mechanisms of clinical phenotyp...