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

Spatially resolved transcriptomics (SRT) data contain intricate noise due to the diffusion of transcripts caused by tissue fixation, permeabilization, and cell lysis during the experiment. Here, we present a protocol for denoising SRT data using SpotGF, an optimal transport-based gene filtering algorithm, without modifying the raw gene expression. We describe steps for data preparation, SpotGF score calculation, filtering threshold determination, denoised data generation, and visualization. Our protocol enhances SRT quality and improves the performance of downstream analyses. For complete details on the use and execution of this protocol, please refer to Du et al.<sup>1</sup>.

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Publication Details

Journal
STAR Protocols
Published
2025-02-04
DOI
https://doi.org/10.1016/j.xpro.2025.103625
Citations
1
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.81

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article

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

Lin Du, Jingmin Kang, Yong Hou, Hai‐Xi Sun et al.
1 citations
STAR Protocols
Single-cell and spatial transcriptomics
0.81
article

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

Lin Du, Jingmin Kang, Yong Hou, Hai‐Xi Sun, Jie Li, Hua Qin
article en
1 citations

Abstract

Spatially resolved transcriptomics (SRT) data contain intricate noise due to the diffusion of transcripts caused by tissue fixation, permeabilization, and cell lysis during the experiment. Here, we present a protocol for denoising SRT data using SpotGF, an optimal transport-based gene filtering algorithm, without modifying the raw gene expression. We describe steps for data preparation, SpotGF score calculation, filtering threshold determination, denoised data generation, and visualization. Our protocol enhances SRT quality and improves the performance of downstream analyses. For complete details on the use and execution of this protocol, please refer to Du et al.<sup>1</sup>.

STAR ProtocolsVol. 6(1)
BGI Group (China) (CN), Beijing Graphene Institute (CN), University of Chinese Academy of Sciences (CN), BGI Research (CN)
Ministry of Science and Technology of the People's Republic of China
Openalex Percentile: Top 31%
Single-cell and spatial transcriptomics
0.81
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