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Related Experiment Video

Updated: Nov 9, 2025

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion.

Zhuliu Li¹, Tianci Song¹, Jeongsik Yong²

¹Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America.

Plos Computational Biology

|April 7, 2021

Summary

This summary is machine-generated.

FIST, a novel graph-regularized tensor completion model, effectively imputes missing gene expressions in spatial transcriptomics RNA sequencing (sptRNA-seq) data. This method enhances spatial gene expression profiling by integrating protein-protein interaction networks and spatial graphs.

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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:

Computational Biology
Genomics
Bioinformatics

Background:

Spatial transcriptomics RNA sequencing (sptRNA-seq) provides spatially resolved gene expression data.
Low RNA capture efficiency and complex tissue preparation lead to incomplete sptRNA-seq data.
Accurate imputation of missing gene expression data is crucial for spatial analysis.

Purpose of the Study:

To develop a novel computational model for imputing missing mRNA expressions in sptRNA-seq data.
To enhance the accuracy and completeness of spatial gene expression profiling.
To leverage biological networks and spatial information for improved imputation.

Main Methods:

Modeled sptRNA-seq data as a 3-way sparse tensor (genes, x-coordinates, y-coordinates).
Employed a graph-regularized tensor completion model (FIST) using Canonical Polyadic Decomposition (CPD).
Integrated protein-protein interaction (PPI) networks and spatial graphs to regularize the tensor completion.

Main Results:

FIST significantly outperformed existing methods in imputing sptRNA-seq data across ten datasets.
Demonstrated the importance of both spatial graphs and PPI networks for imputation accuracy.
Case study showed FIST imputations capture spatial characteristics and reveal tissue-relevant functions in mouse kidney.

Conclusions:

FIST provides a robust and effective solution for imputing missing gene expression data in sptRNA-seq.
The integration of biological networks and spatial information is key to improving imputation accuracy.
Accurate imputation facilitates deeper understanding of spatial gene expression patterns and tissue biology.