Skip to main content

Calcium imaging analysis platform

Project description

banner

PyPI PyPI version Downloads Downloads
License License: GPL v3 Maintenance GitHub commit activity
Documentation Documentation Status Code analysis Maintainability
Chat & Help Gitter Issue tracker GitHub issues

Mesmerize is a platform for the annotation and analysis of neuronal calcium imaging data. Mesmerize encompasses the entire process of calcium imaging analysis from raw data to interactive visualizations. Mesmerize allows you to create FAIR-functionally linked datasets that are easy to share. The analysis tools are applicable for a broad range of biological experiments and come with GUI interfaces that can be used without requiring a programming background.

Associated bioRxiv paper: https://doi.org/10.1101/840488

manuscript on biorxiv

Video Tutorials!

We have recently created detailed video tutorials! The main tutorial series provides a quick overview that takes you from raw imaging data, to downstream analysis and interactive visualizations:
https://www.youtube.com/playlist?list=PLgofWiw2s4REPxH8bx8wZo_6ca435OKqg

Additional videos on specific aspects of Mesmerize will be posted here:
https://www.youtube.com/playlist?list=PLgofWiw2s4RF_RkGRUfflcj5k5KUTG3o_

More tutorial videos will be available soon.

Installation

If you're familiar with anaconda or virtual environments, installation is as simple as:

pip install mesmerize

After installation just call mesmerize from inside the virtual environment to launch it.

If you're unfamiliar with virtual environments, see the docs for more detailed instructions on all operating systems: http://docs.mesmerizelab.org/en/master/user_guides/installation.html

Caiman

In order to use CaImAn features you will need to have CaImAn installed into your environment. See the Mesmerize installation instructions linked above for more details: http://docs.mesmerizelab.org/en/master/user_guides/installation.html

Caiman is used for the following Viewer modules: CNMF, 3D CNMF, CNMFE, caiman motion correction and Detrend DFOF.

tslearn

In order to use tslearn features you will need tslearn. This can be installed via pip or conda, see the detailed installation instructions for more details: http://docs.mesmerizelab.org/en/master/user_guides/installation.html

tslearn is used for KShape clustering, cross-correlation analysis, and some of the flowchart nodes.

tensorflow

In order to use nuset segmentation you will need tensorflow v1.15. You can use either tensorflow (CPU bound) or tensorflow-gpu

pip install --upgrade pip setuptools
pip install tensorflow~=1.15

Documentation

Documentation is available here: http://docs.mesmerizelab.org/

Questions/Discussions

Feel free to ask questions or discuss things on gitter. For larger bugs/issues please use the issue tracker.

Gitter

Issue tracker: https://github.com/kushalkolar/MESmerize/issues

News

See the changelog for more details

November 2020

Changes:

  • Bokeh based plots that use a bokeh-based datapoint tracer, still in very early stages
  • k-Shape "gridsearch", select a n-partitions range and number of combinations, returns heatmap of all k-Shape runs with inertia values NOTE: The gridsearch is not saved when the plot is saved. Only the chosen kshape iteration will be saved. This will be fixed in a future release
  • Plot neural dynamics in PCA or LDA space
  • PadArrays flowchart node to pad dataframe arrays when sizes don't match, useful when splicing is undesired
  • View mean, max, or std projection of caiman motion correction outputs by selecting them from the batch manager

September 2020

Version 0.3 released

Changes:

  • Cross correlation plots with stimulus maps.
  • Support for Femtonics .mes and .mesc recordings.
  • Segmentation using deep learning via NuSeT.

July 2020

Changes:

  • Mesmerize can now be installed via pip.
  • Much easier to import imaging meta data from other sources.
  • Create stimulus tuning curve plots.
  • ΔF/F must now be extracted at the Viewer stage for caiman data, or set through other methods. Spikes and ΔF/F can be visualized in the Viewer.

June 2020

Version 0.2 released.

Changes:

  • Windows is now supported!
  • The Viewer can handle 3D data and 3D ROIs.
  • For development, classes are provided for creating Volumetic ROI types, and a Volumetic ROI manager.
  • Caiman 3D CNMF is supported.
  • Updated CNMF(E) and motion correction modules to use the latest release of CaImAn. Parameter entry GUIs are much more flexible now.
  • CNMF(E) data can be imported directly from hdf5 files from Caiman. Therefore you can use your own scripts/notebooks and existing CNMF hdf5 files for downstream analysis in Mesmerize.
  • More customizable support for use of caiman modules within the Mesmerize viewer's script editor.
  • Suite2p importer added, allowing you to perform downstream analysis of Suite2p output data in Mesmerize.
  • Some cleanup with the batch manager
  • bug fixes

Please note that batches from v0.1 and v0.2 are not inter-compatible. Use the v0.1 branch if you need v0.1

Nov 2019:

See our recent biorxiv manuscript where we use Mesmerize to analyze a calcium imaging dataset from Ciona intestinalis as well as other model organisms!

https://doi.org/10.1101/840488

manuscript on biorxiv

Upcoming

  • Experimental use of the bioformats importer for the Viewer.
  • Export lite versions of projects for easier sharing.
  • Browsers based visualizations for sharing analysis results.

Acknowledgements

  • pyqtgraph developers for creating such an expansive library, which we built upon to create many of the interactive elements of Mesmerize.
  • CaImAn developers have created a very robust library for pre-processing and signal extraction of calcium imaging data, which Mesmerize is able to interface with.
  • Simon Daste provided sample data and assistance which allowed for creation of the Suite2p importer module.
  • Jordi Zwiggelaar created the Mesmerize logo & banner.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mesmerize-0.8.0.tar.gz (1.1 MB view details)

Uploaded Source

Built Distribution

mesmerize-0.8.0-py3-none-any.whl (1.2 MB view details)

Uploaded Python 3

File details

Details for the file mesmerize-0.8.0.tar.gz.

File metadata

  • Download URL: mesmerize-0.8.0.tar.gz
  • Upload date:
  • Size: 1.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.6.0 pkginfo/1.7.1 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.1 CPython/3.8.12

File hashes

Hashes for mesmerize-0.8.0.tar.gz
Algorithm Hash digest
SHA256 510a749bd4c9595b04d227c8a647b884a833e12f9efd5807dd8b7d3f514f3186
MD5 5edb0defaff23b52b4b8c46b9b7eff21
BLAKE2b-256 f4d8c727e4b7583a7bc8e336a5178c907d578d7beb68d07f9c42da40e51b5d1b

See more details on using hashes here.

File details

Details for the file mesmerize-0.8.0-py3-none-any.whl.

File metadata

  • Download URL: mesmerize-0.8.0-py3-none-any.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.6.0 pkginfo/1.7.1 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.1 CPython/3.8.12

File hashes

Hashes for mesmerize-0.8.0-py3-none-any.whl
Algorithm Hash digest
SHA256 2f2ce46f0607d9b9391506e4734fe333c8781a8359fafbbd8e4ffce31f067944
MD5 e91b5bbffe51dc6e39006be2d13ae185
BLAKE2b-256 d9d7d87e106f2b0eafed7de777b8faa320d4d5ca35ba40d64a14faf5f80b0099

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page