Castl: A Consensus Framework for Robust Identification of Spatially Variable Genes in Spatial Transcriptomics

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.

Contents:

• Installation
• Tutorial 1: 10x Visium colorectal cancer liver metastasis datasets
• Tutorial 2: 10x Visium human dorsolateral prefrontal cortex (DLPFC) datasets
• Tutorial 3: Stereo-seq mouse olfactory bulb datasets
• Tutorial 4: Slide-seqV2 mouse olfactory bulb datasets
• Tutorial 5: MERFISH mouse hypothalamic preoptic region data