Normalisation and artefact correction toolkit for fibre photometry data
Last updated on
Dec 14, 2022
Title
Normalisation and artefact correction toolkit for fibre photometry data
Leaders
Chris Nolan (Mattermost: @cnolan | Mastodon: @cnolan@fediscience.org)
Collaborators
Phil Jean-Richard Dit Bressel
Thomas Burton
J Bertran-Gonzalez
Chelsea Goulton
Brainhack Global 2022 Event
Brainhack Australasia
Project Description
Studies using optic fibres to record real-time fluorescent biosensors in-vivo are now commonplace, yet despite a large degree of overlap in the techniques used to filter and normalise this data, there is a surprising lack of open tooling around such analysis. This project is an effort to fill this gap by providing flexible Python-based implementations of common normalisation and artefact correction procedures for fluorescent biosensors, along with some basic analysis tools.
Link to project repository/sources
TBA
Goals for Brainhack Global
- Generate a comparison of normalisation methods for GCaMP and dLight data
- Create a semi-automated artefact rejection method if required (for uncorrectable artefacts)
- Create an interactive data viewer that can show raw and corrected / normalised data, and allows overlays of rejected signal periods
- Outline a standardised structure for raw and processed data along with the necessary associated metadata — ideally BIDS-friendly
Good first issues
- Install the skeleton package from Github repository (to be added)
- Test existing basic normalisation method using a variety of fibre photometry data
- Research and document typical biosensor normalisation methods
Communication channels
https://mattermost.brainhack.org/brainhack/channels/fibrepy
Skills
Primarily, some knowledge of fluorescent biosensor normalisation and analysis procedures will be useful. We’ll be predominantly working in Python, but there will be tasks for all levels of Python competency.
Bonus useful skills:
- Signal processing (we’ll be filtering and fitting timeseries data)
- Python interactive visualisation (bokeh / holoviews / vispy)
- BIDS experience - while we won’t be attempting to add an official BIDS extension for fibre photometry in this project, we will try to produce data structures that are broadly in line with the BIDS format
Onboarding documentation
No response
What will participants learn?
- Data manipulation in Python (numpy / pandas)
- Signal filtering in Python
- Basic GitHub collaboration techniques
Data to use
BYO fibre & behavioural data - we’ll create a repository of useful examples.
Number of collaborators
more
Credit to collaborators
Project contributors will be listed on the repository README.
Image
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Type
data_management, method_development, pipeline_development, visualization
Development status
1_basic structure
Topic
systems_neuroscience
BIDS
Programming language
Python
Modalities
behavioral, other
Git skills
0_no_git_skills, 1_commit_push, 2_branches_PRs
Anything else?
Modalities: fibre_photometry
Things to do after the project is submitted and ready to review.