Skip to main content

Single-cell Variational Inference

Project description

scVI - Single cell Variational Inference

Stars PyPI BioConda Documentation Status Build Status Coverage Code Style Downloads

scVI is a package for end-to-end analysis of single-cell omics data. The package is composed of several deep generative models for omics data analysis, namely:

  • scVI for analysis of single-cell RNA-seq data, as well as its improved differential expression framework

  • scANVI for cell annotation of scRNA-seq data using semi-labeled examples

  • totalVI for analysis of CITE-seq data

  • gimVI for imputation of missing genes in spatial transcriptomics from scRNA-seq data

  • AutoZI for assessing gene-specific levels of zero-inflation in scRNA-seq data

  • LDVAE for an interpretable linear factor model version of scVI

Tutorials and API reference are available in the documentation. Please use the issues here to discuss usage, or submit bug reports. If you’d like to contribute, please check out our contributing guide. If you find a model useful for your research, please consider citing the corresponding publication (linked above).

Package transition

scvi is transitioning to scvi-tools. If you’re looking for scvi source code, please use the branch tags. scvi is still available via pypi and bioconda, but there will be no future releases past 0.6.8. An alpha-release of scvi-tools will be available shortly.

History

0.6.8 (2020-9-16)

  • scvi is now deprecated, please uninstall and install scvi-tools (available shortly)

0.6.7 (2020-8-05)

  • downgrade anndata>=0.7 and scanpy>=1.4.6 @galen

  • make loompy optional, raise sckmisc import error @adam

  • fix PBMCDataset download bug @galen

  • fix AnnDatasetFromAnnData _X in adata.obs bug @galen

0.6.6 (2020-7-08)

  • add tqdm to within cluster DE genes @adam

  • restore tqdm to use simple bar instead of ipywidget @adam

  • move to numpydoc for doctstrings @adam

  • update issues templates @adam

  • Poisson variable gene selection @valentine-svensson

  • BrainSmallDataset set defualt save_path_10X @gokcen-eraslan

  • train_size must be float between 0.0 and 1.0 @galen

  • bump dependency versions @galen

  • remove reproducibility notebook @galen

  • fix scanVI dataloading @pierre

0.6.5 (2020-5-10)

  • updates to totalVI posterior functions and notebooks @adam

  • update seurat v3 HVG selection now using skmisc loess @adam

0.6.4 (2020-4-14)

  • add back Python 3.6 support @adam

  • get_sample_scale() allows gene selection @valentine-svensson

  • bug fix to the dataset to anndata method with how cell measurements are stored @adam

  • fix requirements @adam

0.6.3 (2020-4-01)

  • bug in version for Louvian in setup.py @adam

0.6.2 (2020-4-01)

  • update highly variable gene selection to handle sparse matrices @adam

  • update DE docstrings @pierre

  • improve posterior save load to also handle subclasses @pierre

  • Create NB and ZINB distributions with torch and refactor code accordingly @pierre

  • typos in autozivae @achille

  • bug in csc sparse matrices in anndata data loader @adam

0.6.1 (2020-3-13)

  • handles gene and cell attributes with the same name @han-yuan

  • fixes anndata overwriting when loading @adam, @pierre

  • formatting in basic tutorial @adam

0.6.0 (2020-2-28)

  • updates on TotalVI and LDVAE @adam

  • fix documentation, compatibility and diverse bugs @adam, @pierre @romain

  • fix for external module on scanpy @galen

0.5.0 (2019-10-17)

0.4.1 (2019-08-03)

0.4.0 (2019-07-25)

  • gimVI @achille

  • synthetic correlated datasets, fixed bug in marginal log likelihood @oscar

  • autotune, dataset enhancements @gabriel

  • documentation @jeff

  • more consistent posterior API, docstring, validation set @adam

  • fix anndataset @michael-raevsky

  • linearly decoded VAE @valentine-svensson

  • support for scanpy, fixed bugs, dataset enhancements @achille

  • fix filtering bug, synthetic correlated datasets, docstring, differential expression @pierre

  • better docstring @jamie-morton

  • classifier based on library size for doublet detection @david-kelley

0.3.0 (2019-05-03)

0.2.4 (2018-12-20)

0.2.2 (2018-11-08)

  • added baselines and datasets for sMFISH imputation @jules

  • added harmonization content @chenling

  • fixing bugs on DE @romain

0.2.0 (2018-09-04)

0.1.6 (2018-08-08)

  • MMD and adversarial inference wrapper @eddie

  • Documentation @jeff

  • smFISH data imputation @max

0.1.5 (2018-07-24)

0.1.3 (2018-06-22)

0.1.2 (2018-06-13)

0.1.0 (2017-09-05)

  • First scVI TensorFlow version @romain

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

scvi-0.6.8.tar.gz (26.3 MB view details)

Uploaded Source

Built Distribution

scvi-0.6.8-py2.py3-none-any.whl (158.8 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file scvi-0.6.8.tar.gz.

File metadata

  • Download URL: scvi-0.6.8.tar.gz
  • Upload date:
  • Size: 26.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.6.0 requests-toolbelt/0.9.1 tqdm/4.49.0 CPython/3.7.6

File hashes

Hashes for scvi-0.6.8.tar.gz
Algorithm Hash digest
SHA256 962d894a68dc3739ad8977f6450a265e527bc55a7b963cb838dcbebb759c74ba
MD5 468a7fd0ee8bc61ab850e9ccf6e0b334
BLAKE2b-256 b75385660542029144e875e904cfd71969de4d53908fb48dd1efc7de269763e8

See more details on using hashes here.

File details

Details for the file scvi-0.6.8-py2.py3-none-any.whl.

File metadata

  • Download URL: scvi-0.6.8-py2.py3-none-any.whl
  • Upload date:
  • Size: 158.8 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.6.0 requests-toolbelt/0.9.1 tqdm/4.49.0 CPython/3.7.6

File hashes

Hashes for scvi-0.6.8-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 e0ea6120b4ed8293021d44271eb20d8ac0695d481b77e79bebf37d7949eaaf22
MD5 be21043515d3416b3401aeddd4801ca4
BLAKE2b-256 c34f5816086c1d8af359dfc1f50257e7d915a1ca007be0d9b70f1e23d5e5ac49

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page