Confounds: Python library to handle confounds/covariates; focus on ComBat

[raamana]

Title: Confounds

Project lead: Pradeep Reddy Raamana, @raamana

**[Timezone] UTC-4

Hub: The Americas

Description: Develop a python library of methods to handle confounds in various neuroscientific analyses, esp. statistics and predictive modeling. More info and slides here: https://crossinvalidation.com/2020/03/04/conquering-confounds-and-covariates-in-machine-learning/

Link to project: https://github.com/raamana/confounds

Mattermost handle: @raamana

Goals for the OHBM Brainhack

To beef up and add various methods and statistics needed for typical analyses involving confounds:

• testing and validation of ComBat implementation
• Add tutorial notebooks, with few example use-cases
• Implement metrics, other statistics, related utilities: visualization helpers etc

All contributors will be authors in the paper to be published, describing this library and effort.

Good first issues:

• Add a simple test to target a specific issue or behaviour of ComBat
• Implement metrics to quantify confound to target relationships
• Add tutorial notebooks, with few example use-cases
• Implement metrics to quantify the level of confounding in a given a sample
• Add tests for Residualize() with non-linear models
• Add tests for DummyDeconfounding() method

Skills:

• python programming (intermediate)
• some statistics
• documentation ability

Chat channel: ~confounds

Video channel:

Please have a look at the Mattermost channel (pinned posts) for the URL of the video channel, or alternatively, please contact to @raamana

Project submission

Submission checklist

Once the issue is submitted, please check items in this list as you add under 'Additional project info'

Please include the following above (all required):

• [x] Link to your project: could be a code repository, a shared document, etc. See here
• [x] Include your Mattermost handle (i.e. your username). If you do not have an account, please sign up here.
• [x] Goals for the OHBM Brainhack: describe what you want to achieve during this brainhack. See here.
• [x] Flesh out at least 2 "good first issues": those are tasks that do not require any prior knowledge about your project, could be defined as issues in a GitHub repository, or in a shared document, cf here.
• [x] Skills: list skills that would be particularly suitable for your project. We ask you to include at least one non-coding skill, cf. here.
• [x] Chat channel: A link to a chat channel that will be used during the OHBM Brainhack. This can be an existing channel or a new one. We recommend using the Brainhack space on mattermost, cf. here.
• [x] Video channel: Please create a video channel that will be used during the OHBM Brainhack and share it in your chat channel above. This can be an existing channel or a new one. For instance a jitsi meet room, cf. here.

You can also include information about (all optional):

• [x] Number of participants

1-3 for ComBat 2-3 for the simple/first-time issues mentioned More are welcome if they are already knowledgeable in this area and sufficiently independent

• [x] Twitter-size summary of your project pitch Python library to handle #confounds/covariates in #machinelearning and #neuroscience, contribute to a great #openscience cause! github.com/raamana/confounds Pradeep Reddy Raamana @raamana_

  OHBMHackathon #Brainhack #OHBM2021

• [x] Provide an image of your project for the OHBM brainhack website

We would like to think about how you will credit and onboard new members to your project. We recommend reading references from this section. If you'd like to share your thoughts with future project participants, you can include information about (recommended):

• [x] Specify how will you acknowledge contributions (e.g. listing members on a contributing page).
• [x] Provide links to onboarding documents if you have some.