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https://github.com/alleninstitute/coupledae-patchseq

Multimodal data alignment and cell type analysis with coupled autoencoders.

https://github.com/alleninstitute/coupledae-patchseq

Science Score: 36.0%

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    Links to: nature.com
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    Low similarity (13.0%) to scientific vocabulary

Keywords

autoencoders celltypes multimodal patchseq representation-learning
Last synced: 6 months ago · JSON representation

Repository

Multimodal data alignment and cell type analysis with coupled autoencoders.

Basic Info
  • Host: GitHub
  • Owner: AllenInstitute
  • License: other
  • Language: Jupyter Notebook
  • Default Branch: cplAE-TE
  • Homepage:
  • Size: 71.7 MB
Statistics
  • Stars: 9
  • Watchers: 4
  • Forks: 1
  • Open Issues: 2
  • Releases: 0
Topics
autoencoders celltypes multimodal patchseq representation-learning
Created about 6 years ago · Last pushed over 1 year ago
Metadata Files
Readme License

README.md

Consistent cross-modal identification of cortical neurons with coupled autoencoders

bibtex @article{gala2021consistent, title={Consistent cross-modal identification of cortical neurons with coupled autoencoders}, author={Gala, Rohan and Budzillo, Agata and Baftizadeh, Fahimeh and Miller, Jeremy and Gouwens, Nathan and Arkhipov, Anton and Murphy, Gabe and Tasic, Bosiljka and Zeng, Hongkui and Hawrylycz, Michael and S{\"u}mb{\"u}l, Uygar}, journal={Nature Computational Science}, volume={1}, number={2}, pages={120--127}, year={2021}, publisher={Nature Publishing Group} }

Abstract

Consistent identification of neurons in different experimental modalities is a key problem in neuroscience. While methods to perform multimodal measurements in the same set of single neurons have become available, parsing complex relationships across different modalities to uncover neuronal identity is a growing challenge. Here, we present an optimization framework to learn coordinated representations of multimodal data, and apply it to a large multimodal dataset profiling mouse cortical interneurons. Our approach reveals strong alignment between transcriptomic and electrophysiological characterizations, enables accurate cross-modal data prediction, and identifies cell types that are consistent across modalities.

Data

  • Allen Institute Patch-seq data browser
  • data/proc/ contains the processed dataset used for Gala et al. 2021.
  • see notebooks/data_proc_T.ipynb and notebooks/data_proc_E.ipynb for pre-processing steps.

Code

  • create a conda environment, and install depencies (see requirements.yml). The models can be run with tensorflow versions 2.1 to 2.5
  • clone this repository.
  • navigate to the location with setup.py in this reposiory, and use pip install -e .
  • use cplAE_TE/train.py to start training a model.

You can also play around with a minimal version of the coupled autoencoders code (see minimal folder in this repository) hosted on a cloud environment at CodeOcean.

See also:

A coupled autoencoder approach for multi-modal analysis of cell types, Gala R. et al, Advances in Neural Information Processing Systems 32, 9267--9276, 2019.

The main points covered by earlier work: - We described the problem of collapsing representations encountered when maximizing correlation between representations of coupled autoencoders. - We showed that our solution is an efficient way to effectively whiten the representations. - We used this model to relate transcriptomic and physiological profiles obtained with patch-seq technology.

Owner

  • Name: Allen Institute
  • Login: AllenInstitute
  • Kind: organization
  • Location: Seattle, WA

Please visit http://alleninstitute.github.io/ for more information.

GitHub Events

Total
  • Issues event: 3
  • Issue comment event: 5
  • Push event: 1
Last Year
  • Issues event: 3
  • Issue comment event: 5
  • Push event: 1

Committers

Last synced: 10 months ago

All Time
  • Total Commits: 142
  • Total Committers: 1
  • Avg Commits per committer: 142.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 2
  • Committers: 1
  • Avg Commits per committer: 2.0
  • Development Distribution Score (DDS): 0.0
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Rohan r****a@g****m 142

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 7
  • Total pull requests: 0
  • Average time to close issues: about 1 month
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  • Total issue authors: 4
  • Total pull request authors: 0
  • Average comments per issue: 2.14
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 3
  • Pull requests: 0
  • Average time to close issues: 5 days
  • Average time to close pull requests: N/A
  • Issue authors: 2
  • Pull request authors: 0
  • Average comments per issue: 2.67
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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  • hongruhu (3)
  • beilouer (2)
  • EronHou (1)
  • big-rain (1)
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Dependencies

setup.py pypi