A Consensus Framework for Robust Identification of Spatially Variable Genes in Spatial Transcriptomics
Project description
Castl: A Consensus Framework for Robust Identification of Spatially Variable Genes in Spatial Transcriptomics
1 Overview
Castl is a novel consensus-based analytical framework designed to enhance the accuracy and robustness of spatially variable genes identification for spatially resolved transcriptomics through statistically rigorous algorithms, including rank aggregation, p-value aggregation, and Stabl aggregation. Comprehensive evaluations on both simulated and real-world data demonstrate that Castl consistently identifies biologically meaningful spatial expression patterns, mitigates method-specific biases and effectively controls FDRs across various biological contexts, resolutions, and spatial technologies. This flexible, assumption-free framework offers a robust and standardized foundation for spatially informed feature discovery in complex biological systems.
2 System Requirements
Python
- Python >= 3.9.5
- pandas >= 1.3.0
- numpy >= 1.21.0
- rpy2 >= 3.5.0
- scipy >= 1.7.0
- statsmodels >= 0.13.0
- anndata >= 0.8.0
- scanpy >= 1.9.0
- matplotlib >= 3.5.0
- seaborn >= 0.12.0
- scikit-learn >= 1.0.0
R
- R >= 4.0.5
- dplyr >= 1.0.0
- tidyverse >= 1.3.0
- clusterProfiler >= 3.18.0
- org.Hs.eg.db >= 3.12.0
- patchwork >= 1.1.0
- ggplot2 >= 3.3.0
- TissueEnrich >= 1.8.0
- SummarizedExperiment >= 1.20.0
3 Installation
Python
Castl can be installed directly from PyPI:
pip install STCastl
or download from Github and install it:
git clone https://github.com/TheY11/Castl
cd Castl
pip install -e .
R
We also provide the R package castlRUtils for calculating quality scores (QS) of SVGs.
library(devtools)
devtools::install_github("TheY11/Castl", subdir = "Castl/r_utils", force = TRUE)
library(castlRUtils)
4 Tutorials
Detailed usage instructions and tutorials for Castl are available at: https://castl-analysis.readthedocs.io/en/latest/
5 Improvements
For questions or issues, please open an issue.
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