Data

Tools

Indexes

Applications

Experiments

Open Source Science

https://github.com/alleninstitute/cplae_met

Coupled autoencoder extension for MET analysis.

https://github.com/alleninstitute/cplae_met

Science Score: 23.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ✓
    Academic publication links
    Links to: sciencedirect.com, nature.com
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (8.3%) to scientific vocabulary
Last synced: 9 months ago · JSON representation

Repository

Coupled autoencoder extension for MET analysis.

Basic Info
  • Host: GitHub
  • Owner: AllenInstitute
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 133 MB
Statistics
  • Stars: 1
  • Watchers: 5
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created about 5 years ago · Last pushed about 1 year ago
Metadata Files
Readme

README.md

Coupled autoencoders for M, E, and T analysis

Objectives: - Joint analysis of Morphology, Electrophysiology, and Transcriptomic data from Patch-seq experiments. - Extending results from Patch-seq dataset to EM reconstructions

Data

  • Patch-seq dataset for V1 cortical interneurons (Gala et al. 2021: 3411 cells in T and E)
  • Patch-seq dataset for V1 cortical interneurons (Gouwens et al. 2020: 3819 cells in T and E)
  • Density representations for morphology (721 cells)

Environment

  1. Navigate to the cplAE_MET folder with the setup.py file.
  2. Create the conda environment with the main dependencies. bash conda create -n cplAE_MET conda activate cplAE_MET conda install python=3.8 conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch #see system specific instructions pip install scikit-learn jupyterlab seaborn pandas rich tqdm timebudget statsmodels umap-learn pip install tensorboard
  3. Install the development version of this repository bash pip install -e .
  4. Install the cplAE_TE repository after cloning it. bash # can do this within any directory on local machine git clone https://github.com/AllenInstitute/coupledAE-patchseq cd coupledAE-patchseq pip install -e .

Experiments

"TMEaT5-0aM5-0asd1-0aE5-0aME5-0lambdaMET1-0lambdatuneMET0-75lambdaMEM1-0lambdaMEE1-0augdec1Enoise0-05Mnoise0-0scale0-3ld5ne50000ri0fold_2.pkl"

Additional repositories

Config

```toml

config.toml contents

packagedir = '/Local/code/cplAEMET/' METinhdata = '/Local/data/inhMETmodelinputmat.mat' ```

```

config_preproc.toml contents

packagedir = '/Users/fahimehb/Documents/git-workspace/cplAEMET/'

For T

specimenidsfile = "excinhspecimenids30Mar22.txt" genefile = "goodgenesbetascore.csv" tdataoutputfile = "Tdata30Mar22.csv" tannooutputfile = "Tanno30Mar22.csv" geneidoutputfile = "geneids_30Mar22.csv"

For M

mdatafolder = 'mdata' manno = 'manno.csv' hist2d120x4folder = 'hist2d120x4' moutputfile = 'Mdata30Mar22.mat'

For E

Etimeseriesfile = "fvEphystimeseries30Mar22.h5" ipfxfeaturesfile = "ipfxfeatures30Mar22.csv" eoutputfile = "Edata_30Mar22.csv"

For MET

metoutputfile = "METdata30Mar22.mat" ```

Contributors

Fahimeh Baftizadeh, Rohan Gala

Owner

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

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

GitHub Events

Total
  • Delete event: 1
  • Push event: 20
  • Create event: 2
Last Year
  • Delete event: 1
  • Push event: 20
  • Create event: 2

Issues and Pull Requests

Last synced: about 1 year ago

All Time
  • Total issues: 0
  • Total pull requests: 6
  • Average time to close issues: N/A
  • Average time to close pull requests: about 2 months
  • Total issue authors: 0
  • Total pull request authors: 2
  • Average comments per issue: 0
  • Average comments per pull request: 0.33
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
Pull Request Authors
  • Fahimeh1983 (5)
  • rhngla (1)
Top Labels
Issue Labels
Pull Request Labels

Dependencies

setup.py pypi