Metadata-Version: 2.1
Name: step-kit
Version: 0.1.0
Summary: STEP, an acronym for Spatial Transcriptomics Embedding Procedure, is a deep learning-based tool for the analysis of single-cell RNA (scRNA-seq) and spatially resolved transcriptomics (SRT) data. STEP introduces a unified approach to process and analyze multiple samples of scRNA-seq data as well as align several sections of SRT data, disregarding location relationships. Furthermore, STEP conducts integrative analysis across different modalities like scRNA-seq and SRT.
License: Apache-2.0
Author: SGGb0nd
Author-email: lilounan1997@gmail.com
Requires-Python: >=3.10,<4.0
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Provides-Extra: docs
Provides-Extra: ipykernel
Provides-Extra: r
Requires-Dist: einops (>=0.6.0,<0.7.0)
Requires-Dist: esda (==2.5.1)
Requires-Dist: ipykernel ; extra == "ipykernel"
Requires-Dist: leidenalg (>=0.9.0,<0.10.0)
Requires-Dist: libpysal (==4.8.1)
Requires-Dist: nbsphinx ; extra == "docs"
Requires-Dist: rpy2 ; extra == "r"
Requires-Dist: scanpy (>=1.10.0rc2,<2.0.0)
Requires-Dist: scikit-misc
Requires-Dist: seaborn
Requires-Dist: sphinx ; extra == "docs"
Requires-Dist: sphinx-autodoc-typehints ; extra == "docs"
Requires-Dist: sphinx-book-theme ; extra == "docs"
Requires-Dist: sphinxcontrib-bibtex ; extra == "docs"
Requires-Dist: torch (==1.13.0)
Description-Content-Type: text/markdown

# STEP: Spatial Transcriptomics Embedding Procedure
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STEP, an acronym for Spatial Transcriptomics Embedding Procedure, is a deep learning-based tool for the analysis of single-cell RNA (scRNA-seq) and spatially resolved transcriptomics (SRT) data. step introduces a unified approach to stepcess and analyze multiple samples of scRNA-seq data as well as align several sections of SRT data, disregarding location relationships. Furthermore, step conducts integrative analysis across different modalities like scRNA-seq and SRT.

## Key Features

-  Integration of multiple scRNA-seq and single-cell resolution SRT samples to reveal cell-type level heterogeneities
-  Alignment of various SRT data sections contiguous or non-contiguous to identify spatial domains across sections
-  Performance of integrative analysis across modalities (scRNA-seq and SRT) and cell-type deconvolution for the non-single-cell resolution SRT data.

## Installation
`pip install step`
require python version 3.10+


## Contribution

We welcome contributions! Please see [`CONTRIBUTING.md`](./CONTRIBUTING.md) for more details!

## License

step is licensed under [LICENSE](./LICENSE)

