Exploratory data analysis and visualization tool for time-series multi-omics data sets.
This tool is created for the following paper:
"MOVIS: A Multi-Omics Software Solution for Multi-modal Time-Series Clustering, Embedding, and Visualizing Tasks" by Aleksandar Anžel, Dominik Heider, and Georges Hattab
Please cite the paper as:
@article{ANZEL20221044,
title = {MOVIS: A multi-omics software solution for multi-modal time-series clustering, embedding, and visualizing tasks},
journal = {Computational and Structural Biotechnology Journal},
volume = {20},
pages = {1044-1055},
year = {2022},
issn = {2001-0370},
doi = {https://doi.org/10.1016/j.csbj.2022.02.012},
url = {https://www.sciencedirect.com/science/article/pii/S2001037022000526},
author = {Aleksandar Anžel and Dominik Heider and Georges Hattab},
keywords = {Time-series, Multi-omics, Visualization, Data exploration, Temporal multi-omics, Longitudinal multi-omics},
abstract = {Thanks to recent advances in sequencing and computational technologies, many researchers with biological and/or medical backgrounds are now producing multiple data sets with an embedded temporal dimension. Multi-modalities enable researchers to explore and investigate different biological and physico-chemical processes with various technologies. Motivated to explore multi-omics data and time-series multi-omics specifically, the exploration process has been hindered by the separation introduced by each omics-type. To effectively explore such temporal data sets, discover anomalies, find patterns, and better understand their intricacies, expertise in computer science and bioinformatics is required. Here we present MOVIS, a modular time-series multi-omics exploration tool with a user-friendly web interface that facilitates the data exploration of such data. It brings into equal participation each time-series omic-type for analysis and visualization. As of the time of writing, two time-series multi-omics data sets have been integrated and successfully reproduced. The resulting visualizations are task-specific, reproducible, and publication-ready. MOVIS is built on open-source software and is easily extendable to accommodate different analytical tasks. An online version of MOVIS is available under https://movis.mathematik.uni-marburg.de/ and on Docker Hub (https://hub.docker.com/r/aanzel/movis).}
}
Abstract:
Thanks to recent advances in sequencing and computational technologies, many researchers with biological and/or medical backgrounds are now producing multiple data sets with an embedded temporal dimension. Multi-modalities enable researchers to explore and investigate different biological and physico-chemical processes with various technologies. Motivated to explore multi-omics data and time-series multi-omics specifically, the exploration process has been hindered by the separation introduced by each omics-type. To effectively explore such temporal data sets, discover anomalies, find patterns, and better understand their intricacies, expertise in computer science and bioinformatics is required. Here we present MOVIS, a modular time-series multi-omics exploration tool with a user-friendly web interface that facilitates the data exploration of such data. It brings into equal participation each time-series omic-type for analysis and visualization. As of the time of writing, two time-series multi-omics data sets have been integrated and successfully reproduced. The resulting visualizations are task-specific, reproducible, and publication-ready. MOVIS is built on open-source software and is easily extendable to accommodate different analytical tasks. An online version of MOVIS is available under https://movis.mathematik.uni-marburg.de/ and on Docker Hub (https://hub.docker.com/r/aanzel/movis).
The code is written in Python 3.8.11 and tested on Linux with the following libraries installed:
| Library | Version |
|---|---|
| altair | 4.1.0 |
| altair_saver | 0.5.0 |
| biopython | 1.78 |
| gensim | 4.0.1 |
| numpy | 1.21.2 |
| pandas | 1.3.5 |
| scikit-learn | 1.0.2 |
| scipy | 1.7.3 |
| streamlit | 1.5.1 |
| protobuf | 3.19.1 |
| python-levenshtein | 0.12.2 |
| click | 7.1.2 |
The data used in the Example 1 comes from the following paper:
Integration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance, Herold, M., Martínez Arbas, S., Narayanasamy, S. et al. Nat Commun 11, 5281(2020). https://doi.org/10.1038/s41467-020-19006-2.
It is stored at Data/cached/example_1/ in either a raw format or as a pickle object.
The data used in the Example 2 comes from the following paper:
Short- and Long-Term Transcriptomic Responses of Escherichia coli to Biocides: a Systems Analysis, Merchel Piovesan Pereira, B., Wang, X., & Tagkopoulos, I. (2020). Applied and environmental microbiology, 86(14), e00708-20. https://doi.org/10.1128/AEM.00708-20.
It is stored at Data/cached/example_2/ in a raw format.
| Script | Description |
|---|---|
| Source/ | contains all scripts necessary to run the tool. |
| Source/main.py | contains the code that builds the main layout and connects all pages. |
| Source/home.py | contains the code that builds the home page. |
| Source/example_1.py | contains the code that builds the example 1 page. |
| Source/example_2.py | contains the code that builds the example 2 page. |
| Source/case_study.py | contains the code that builds the case study page. |
| Source/upload.py | contains the code that builds the upload page. |
| Source/common.py | contains the code with functions shared by all pages. |
| Source/visualize.py | contains the code with functions that create various visualizations present in this tool. |
Check out our Wiki page for detailed information about MOVIS and how to use it.
The easiest way to install the tool is to use our latest Docker image:
docker pull aanzel/movis:latest
docker run --publish 8501:8501 --detach --name movis aanzel/movis:latest
You can start using the tool by opening a web browser and typing in http://localhost:8501/ as the address. If you run the docker container, you have to use the IP address or hostname instead of localhost.
Caution! Use at your own risk!
You could also clone this repository, build a docker container yourself, and run it locally. This is not recommended as we might introduce unstable features that might not end in the next release of MOVIS. Below is a sequence of instructions (for Linux-based systems) to run the unstable version of MOVIS:
git clone https://github.com/AAnzel/MOVIS.git
cd MOVIS
docker build -t movis-local:unstable .
docker run --publish 8501:8501 --detach --name movis movis-local:unstable
You can start using the tool by opening a web browser and typing in http://localhost:8501/ as the address. If you run the docker container, you have to use the IP address or hostname instead of localhost.
Licensed under the GNU General Public License, Version 3.0 (LICENSE or https://www.gnu.org/licenses/gpl-3.0.en.html)
Any contribution intentionally submitted for inclusion in the work by you, shall be licensed under the GNU GPLv3.