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dca

Deep count autoencoder for denoising scRNA-seq data

https://github.com/theislab/dca

Science Score: 33.0%

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

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    CITATION.cff file
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    codemeta.json file
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    .zenodo.json file
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    Academic publication links
    Links to: nature.com
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    2 of 9 committers (22.2%) from academic institutions
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    Scientific vocabulary similarity
    Low similarity (12.1%) to scientific vocabulary

Keywords from Contributors

bioinformatics anndata scanpy scverse transcriptomics visualize-data closember human-cell-atlas single-cell-genomics single-cell-rna-seq
Last synced: 10 months ago · JSON representation

Repository

Deep count autoencoder for denoising scRNA-seq data

Basic Info
  • Host: GitHub
  • Owner: theislab
  • License: apache-2.0
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 593 MB
Statistics
  • Stars: 261
  • Watchers: 10
  • Forks: 72
  • Open Issues: 32
  • Releases: 1
Created over 9 years ago · Last pushed over 2 years ago
Metadata Files
Readme License

README.md

Deep count autoencoder for denoising scRNA-seq data

A deep count autoencoder network to denoise scRNA-seq data and remove the dropout effect by taking the count structure, overdispersed nature and sparsity of the data into account using a deep autoencoder with zero-inflated negative binomial (ZINB) loss function.

See our manuscript and tutorial for more details.

Installation

pip

For a traditional Python installation of the count autoencoder and the required packages, use

$ pip install dca

conda

Another approach for installing count autoencoder and the required packages is to use Conda (most easily obtained via the Miniconda Python distribution). Afterwards run the following commands.

$ conda install -c bioconda dca

Usage

You can run the autoencoder from the command line:

dca matrix.csv results

where matrix.csv is a CSV/TSV-formatted raw count matrix with genes in rows and cells in columns. Cell and gene labels are mandatory.

Results

Output folder contains the main output file (representing the mean parameter of ZINB distribution) as well as some additional matrices in TSV format:

  • mean.tsv is the main output of the method which represents the mean parameter of the ZINB distribution. This file has the same dimensions as the input file (except that the zero-expression genes or cells are excluded). It is formatted as a gene x cell matrix. Additionally, mean_norm.tsv file contains the library size-normalized expressions of each cell and gene. See normalize_total function from Scanpy for the details about the default library size normalization method used in DCA.

  • pi.tsv and dispersion.tsv files represent dropout probabilities and dispersion for each cell and gene. Matrix dimensions are same as mean.tsv and the input file.

  • reduced.tsv file contains the hidden representation of each cell (in a 32-dimensional space by default), which denotes the activations of bottleneck neurons.

Use -h option to see all available parameters and defaults.

Hyperparameter optimization

You can run the autoencoder with --hyper option to perform hyperparameter search.

Owner

  • Name: Theis Lab
  • Login: theislab
  • Kind: organization
  • Email: icb.office@helmholtz-muenchen.de
  • Location: Munich

Institute of Computational Biology

GitHub Events

Total
  • Issues event: 1
  • Watch event: 16
  • Issue comment event: 4
  • Fork event: 3
Last Year
  • Issues event: 1
  • Watch event: 16
  • Issue comment event: 4
  • Fork event: 3

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 369
  • Total Committers: 9
  • Avg Commits per committer: 41.0
  • Development Distribution Score (DDS): 0.491
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Gökcen Eraslan g****n@h****e 188
Gökcen Eraslan g****n@g****m 158
Gokcen Eraslan g****n@b****g 13
Ersin e****w@g****m 3
timoast 4****t 3
Bérénice Batut b****t@g****m 1
Felix Raimundo f****o@g****m 1
milescsmith 3****h 1
Philipp A f****p@w****e 1
Committer Domains (Top 20 + Academic)
google.com: 1 broadinstitute.org: 1 helmholtz-muenchen.de: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 60
  • Total pull requests: 11
  • Average time to close issues: 4 months
  • Average time to close pull requests: 4 months
  • Total issue authors: 53
  • Total pull request authors: 9
  • Average comments per issue: 1.32
  • Average comments per pull request: 1.09
  • Merged pull requests: 8
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 1
  • Pull request authors: 0
  • Average comments per issue: 1.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • radio1988 (4)
  • HelloWorldLTY (2)
  • Winnie09 (2)
  • Yolanda-HT (2)
  • ajrzepiela (2)
  • ScarletChieftain (1)
  • MakikoJuan (1)
  • WenzhuoTang (1)
  • frenkiboy (1)
  • LeahBriscoe (1)
  • 11051911 (1)
  • bauerbach95 (1)
  • zhqu1148980644 (1)
  • shuyizzz (1)
  • crawlerWPS (1)
Pull Request Authors
  • ersinpw (3)
  • rahul-ohlan (2)
  • flying-sheep (1)
  • timoast (1)
  • bebatut (1)
  • scottgigante-immunai (1)
  • gokceneraslan (1)
  • gamazeps (1)
  • milescsmith (1)
Top Labels
Issue Labels
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 547 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 10
  • Total versions: 12
  • Total maintainers: 1
pypi.org: dca

Count autoencoder for scRNA-seq denoising

  • Versions: 12
  • Dependent Packages: 0
  • Dependent Repositories: 10
  • Downloads: 547 Last month
Rankings
Stargazers count: 4.4%
Dependent repos count: 4.6%
Forks count: 5.3%
Average: 7.6%
Dependent packages count: 10.0%
Downloads: 13.6%
Maintainers (1)
gokcen
Last synced: 11 months ago

Dependencies

setup.py pypi
  • h5py *
  • keras >=2.4,<2.6
  • kopt *
  • numpy >=1.7
  • pandas *
  • scanpy *
  • scikit-learn *
  • six >=1.10.0
  • tensorflow >=2.0,<2.5