Skip to main content

Calcium Imaging Pipeline Tool

Project description

OptiNiSt optinist

PYPI PYPI

OptiNiSt(Optical Neuroimage Studio) is a GUI based workflow pipeline tools for processing two-photon calcium imaging data.

OptiNiSt helps researchers try multiple data analysis methods, visualize the results, and construct the data analysis pipelines easily and quickly on GUI. OptiNiSt's data-saving format follows NWB standards.

OptiNiSt also supports reproducibility of scientific research, standardization of analysis protocols, and developments of novel analysis tools as plug-in.

Support Library

ROI detection

Postprocessing

  • Basic Neural Analysis (Event Trigger Average...)
  • Dimension Reduction (PCA...)
  • Neural Decoding (LDA...)
  • Neural Population Analysis (Correlation...)

Saving Format

Key Features

:beginner: Easy-To-Create Workflow

  • zero-knowledge of coding: OptiNiSt allows you to create analysis pipelines easily on the GUI.

:zap: Visualizing analysis results

  • quick visualization: OptiNiSt supports you visualize the analysis results by plotly.

:rocket: Managing Workflows

  • recording and reproducing: OptiNiSt records and reproduces the workflow pipelines easily.

Installation

Need anaconda or miniconda or miniforge environment.

conda create -n optinist python=3.9
conda activate optinist

Install from pip.

pip install optinist

launch.

run_optinist

Open browser. http://localhost:8000

Documentation

https://optinist.readthedocs.io/en/latest/

Using GUI

Workflow

  • OptiNiSt allows you to make your analysis pipelines by graph style using nodes and edges on GUI. Parameters for each analysis are easily changeable.

workflow

Visualize

  • OptiNiSt allows you to visualize the analysis results with one click by plotly. It supports a variety of plotting styles.

visualize

Record

  • OptiNiSt supports you in recording and reproducing workflow pipelines in an organized manner.

record

Contributors

Proposers

Kenji Doya, Yukako Yamane OIST Neural Computation Unit

Main Developers

Shogo Akiyama, Yoshifumi Takeshima

Support Developers

Tatsuya Tanabe, Yosuke Kaneko, Syuya Saeki

References

[Suite2p] Marius Pachitariu, Carsen Stringer, Mario Dipoppa, Sylvia Schröder, L. Federico Rossi, Henry Dalgleish, Matteo Carandini, Kenneth D. Harris. "Suite2p: beyond 10,000 neurons with standard two-photon microscopy". 2017 [CaImAn] Andrea Giovannucci Is a corresponding author, Johannes Friedrich, Pat Gunn, Jérémie Kalfon, Brandon L Brown, Sue Ann Koay, Jiannis Taxidis, Farzaneh Najafi, Jeffrey L Gauthier, Pengcheng Zhou, Baljit S Khakh, David W Tank, Dmitri B Chklovskii, Eftychios A Pnevmatikakis. "CaImAn: An open source tool for scalable Calcium Imaging data Analysis". 2019 [LCCD] Tsubasa Ito, Keisuke Ota, Kanako Ueno, Yasuhiro Oisi, Chie Matsubara, Kenta Kobayashi, Masamichi Ohkura, Junichi Nakai, Masanori Murayama, Toru Aonishi, "Low computational-cost cell detection method for calcium imaging data", 2022 [PyNWB] Oliver Rübel, Andrew Tritt, Ryan Ly, Benjamin K. Dichter, Satrajit Ghosh, Lawrence Niu, Ivan Soltesz, Karel Svoboda, Loren Frank, Kristofer E. Bouchard, "The Neurodata Without Borders ecosystem for neurophysiological data science", bioRxiv 2021.03.13.435173, March 15, 2021

Citation

If you use this software, please cite our paper: https://www.biorxiv.org/content/10.1101/2024.09.17.613603v1 Read our paper
@misc{OptiNiSt,
	author = {Yamane, Yukako and Li, Yuzhe and Matsumoto, Keita and Kanai, Ryota and Desforges, Miles and Gutierrez, Carlos Enrique and Doya, Kenji},
	title = {Optical Neuroimage Studio (OptiNiSt): intuitive, scalable, extendable framework for optical neuroimage data analysis},
	elocation-id = {2024.09.17.613603},
	year = {2024},
	doi = {10.1101/2024.09.17.613603},
  journal = {bioRxiv}
	publisher = {Cold Spring Harbor Laboratory},
}

Join Our User Community on Slack

We've launched a Slack workspace to provide a more casual space for discussions and interaction among users.

Join the Optinist User Community on Slack

Feel free to use it as a space for casual conversations, product questions, requests, and feedback.

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

optinist-2.0.3.tar.gz (79.5 MB view details)

Uploaded Source

Built Distribution

optinist-2.0.3-py3-none-any.whl (79.7 MB view details)

Uploaded Python 3

File details

Details for the file optinist-2.0.3.tar.gz.

File metadata

  • Download URL: optinist-2.0.3.tar.gz
  • Upload date:
  • Size: 79.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.0.1 CPython/3.9.21 Darwin/23.6.0

File hashes

Hashes for optinist-2.0.3.tar.gz
Algorithm Hash digest
SHA256 0b37c91f8ad3651098d677e6b8877103dfc470fc1ec74e7cb54fb1e8129edfe9
MD5 870bc898f31ccb693d69e8e529111b13
BLAKE2b-256 e935da5cfa6cf02d0352be8ea8be7aad5ffe1deda382cc4b2b093381edcda42c

See more details on using hashes here.

File details

Details for the file optinist-2.0.3-py3-none-any.whl.

File metadata

  • Download URL: optinist-2.0.3-py3-none-any.whl
  • Upload date:
  • Size: 79.7 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.0.1 CPython/3.9.21 Darwin/23.6.0

File hashes

Hashes for optinist-2.0.3-py3-none-any.whl
Algorithm Hash digest
SHA256 7d8df23c0585f3dbba8ddd21f9c3099a299112d8afb67d1d82006cfcd40a9ee0
MD5 ba198c0db010599d1674e47d5df20170
BLAKE2b-256 6fb1254141db613495a503c75ab8ec070491d04e18981a007f7054ae4390b3bc

See more details on using hashes here.

Supported by

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