Welcome to STEP’s documentation!¶
Overview¶
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.
Contents:¶
- Installation
- Usage
- Tutorials
- Spatial domain identification on 10x Visium DLFPC data
- Performing integrative analysis on scRNA-seq data and 10x Visium data of Human Lymph Node with STEP
- Reveal multiple level of biological heterogeneities in Mouse Hypothalamus by MERFISH-seq
- Integrate Human Immune Cell datasets with STEP and generate batch-corrected embeddings and gene expressions.
- Visium HD Human Colorectal Cancer (16 um) cell type clustering & spatial domain identification
- Visium HD Mouse Small Intestine (8 um and 16 um) cell type clustering & spatial domain identification
- API Reference