step-kit 0.2.1

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.

STEP: Spatial Transcriptomics Embedding Procedure

Introduction

STEP, an acronym for Spatial Transcriptomics Embedding Procedure, is a foundation deep learning/AI architecture for the analysis of spatially resolved transcriptomics (SRT) data, and is also compatible with scRNA-seq data. STEP roots on the precise captures of three major varitions occured in the SRT (and scRNA-seq) data: Transcriptional Variations, Batch Variations and Spatial Variations with the correponding modular designs: Backbone model: a Transformer based model togther with gene module seqeunce mapping; Batch-effect model: A pair of inverse transformations utilizing the batch-embedding conception for the decoupled batch-effect elimination; Spatial model: a GCN-based spatial filter/smoother working on the extracted embedding from the Backbone model, different from the usage of GCN in other methods as a feature extractor. Thus, with the proper combinations of these models, STEP introduces a unified approach to systematically process and analyze single or multiple samples of SRT data, disregarding location relationships between sections (meaning both contiguous and non-contiguous sections), to reveal multi-scale bilogical heterogeneities (cell types and spatial domains) in multi-resolution SRT data. Furthermore, STEP can also conduct integrative analysis on scRNA-seq and SRT data.

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.
• Scalable to different data resolutions, i.e., wild range of technologies and platforms of SRT data, including Visium HD, Visum, MERFISH, STARmap, Stereo-seq, ST, etc.
• Scalable to large datasets with a high number of cells and spatial locations.
• Performance of integrative analysis across modalities (scRNA-seq and SRT) and cell-type deconvolution for the non-single-cell resolution SRT data.

Other Capabilities

• Capability to produce not only the batch-corrected embeddings but also batch-corrected gene expression profiles for scRNA-seq data.
• Capability to perform spatial mapping of reference scRNA-seq data points to the spatial locations of SRT data based on the learned co-embeddings and kNN.

Installation

pip install step-kit

require python version 3.10+. Documentation and tutorials are available at https://sggb0nd.github.io/step/

Contribution

We welcome contributions! Please see CONTRIBUTING.md for more details!

License

step is licensed under LICENSE