Normalisation and artefact correction toolkit for fibre photometry data

[crnolan]

Title

Normalisation and artefact correction toolkit for fibre photometry data

Leaders

Chris Nolan (Mattermost: @cnolan | Mastodon: @[email protected])

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

1. Install the skeleton package from Github repository (to be added)
2. Test existing basic normalisation method using a variety of fibre photometry data
3. 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

Tools

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.

• Add a comment below the main post of your issue saying: Hi @brainhackorg/project-monitors my project is ready!
• Twitter-sized summary of your project pitch.

[crnolan]

Hi @brainhackorg/project-monitors my project is ready!

[Remi-Gau]

Adding a link to the ephys bids extension in case this can help prevent wheel reinventions.

https://docs.google.com/document/u/0/d/1oG-C8T-dWPqfVzL2W8HO3elWK8NIh2cOCPssRGv23n0/mobilebasic

[crnolan]

I've been loosely tracking that project, might be some overlap, but the file formats etc. are all quite different.

[Remi-Gau]

yup I would not expect file formats to be the same but some metadata may. :-)