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.
Project description
STEP: Spatial Transcriptomics Embedding Procedure
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
for more details!
License
step is licensed under LICENSE
Project details
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