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Related Experiment Video

Updated: May 29, 2025

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

Protocol to denoise spatially resolved transcriptomics data utilizing optimal transport-based gene filtering

Lin Du¹, Jingmin Kang², Jie Li³

¹College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI Research, Beijing 102601, China.

|February 6, 2025

Summary

This summary is machine-generated.

This study introduces SpotGF, a novel gene filtering method to reduce noise in spatially resolved transcriptomics (SRT) data. The protocol enhances SRT data quality and downstream analysis performance without altering raw gene expression.

Keywords:

Bioinformatics Computer sciences Health Sciences Systems biology

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Last Updated: May 29, 2025

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

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Published on: July 6, 2022

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

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Published on: March 14, 2022

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

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Published on: March 5, 2022

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Spatially resolved transcriptomics (SRT) data are susceptible to noise from experimental procedures like tissue fixation and cell lysis.
Transcript diffusion during sample processing introduces artifacts, complicating accurate spatial gene expression analysis.

Purpose of the Study:

To present a protocol for denoising SRT data using an optimal transport-based gene filtering algorithm called SpotGF.
To improve the quality and reliability of SRT data for enhanced downstream biological interpretation.

Main Methods:

The protocol details data preparation steps for SRT datasets.
It involves calculating SpotGF scores for gene filtering based on optimal transport principles.
Methods for determining filtering thresholds and generating denoised data are described.

Main Results:

The SpotGF algorithm effectively denoises SRT data by filtering noisy gene expression signals.
The denoising process does not modify the original raw gene expression values.
The protocol leads to enhanced SRT data quality.

Conclusions:

The presented protocol using SpotGF offers an effective strategy for denoising SRT data.
Improved SRT data quality facilitates more accurate and robust downstream analyses.
This method provides a valuable tool for researchers working with spatial transcriptomics.