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Related Experiment Video

Updated: Mar 7, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

Castl: robust identification of spatially variable genes in spatial transcriptomics via an ensemble-based framework.

Yiyi Yu¹, Jiyuan Yang², Ping-An He¹

¹Department of Mathematics, College of Science, Zhejiang Sci-Tech University, 928 2nd Avenue, Qiantang District, Hangzhou, Zhejiang 310018, China.

Briefings in Bioinformatics

|March 6, 2026

Summary

This summary is machine-generated.

Castl is a new computational framework that integrates multiple methods to identify spatially variable genes (SVGs) in tissues. It accurately detects spatial patterns and controls false discoveries across diverse datasets.

Keywords:

consensus framework spatial resolved transcriptomics spatially variable genes

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Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Spatially resolved transcriptomics enables the study of tissue organization.
Identifying spatially variable genes (SVGs) is crucial for this field.
Existing SVG identification methods have limitations, including sensitivity variability and high false discovery rates (FDRs) due to algorithm-specific assumptions.

Purpose of the Study:

To develop a robust and flexible framework for identifying spatially variable genes (SVGs).
To address the limitations of existing SVG detection methods, such as assumption-dependency and inconsistent performance.
To provide a standardized approach for feature discovery in complex biological systems using spatial transcriptomics data.

Main Methods:

Developed Castl, an ensemble-based computational framework for SVG identification.
Integrated multiple SVG detection methods using statistically designed aggregation modules.
Evaluated Castl's performance on both simulated and real-world spatial transcriptomics datasets.

Main Results:

Castl consistently identifies biologically meaningful spatial gene expression patterns.
The framework effectively mitigates biases inherent in individual detection methods.
Castl demonstrates robust control of false discovery rates (FDRs) across diverse biological contexts, resolutions, and spatial technologies.
Comprehensive evaluations confirmed Castl's superior performance compared to existing methods.

Conclusions:

Castl offers a flexible, assumption-free framework for reliable SVG identification.
This ensemble approach provides a standardized foundation for spatially informed feature discovery.
Castl enhances the analysis of complex biological systems through accurate spatial transcriptomics data interpretation.