Cited by 8Single-cell and spatial transcriptomicsGene expression and cancer classificationGenomics and Phylogenetic Studies

SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors

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Liu, Xinyi, Tang, Gongyu, Chen, Yuhao, Li, Yuanxiang, Li, Hua, Wang, Xiaowei·2025·Cancer Research

Published version of a preprint

Abstract

The rapid development of spatial transcriptomics (ST) technologies has enabled transcriptome-wide profiling of gene expression in tissue sections. Despite the emergence of single-cell resolution platforms, most ST sequencing studies still operate at a multicell resolution. Consequently, deconvolution of cell identities within the spatial spots has become imperative for characterizing cell-type–specific spatial organization. To this end, we developed Spatial Deconvolution Explorer (SpatialDeX), a regression model–based method for estimating cell-type proportions in tumor ST spots. SpatialDeX exhibited comparable performance to reference-based methods and outperformed other reference-free methods with simulated ST data. Using experimental ST data, SpatialDeX demonstrated superior performance compared with both reference-based and reference-free approaches. Additionally, a pan-cancer clustering analysis on tumor spots identified by SpatialDeX unveiled distinct tumor progression mechanisms both within and across diverse cancer types. Overall, SpatialDeX is a valuable tool for unraveling the spatial cellular organization of tissues from ST data without requiring single-cell RNA-seq references. Significance: The development of a reference-free method for deconvolving the identity of cells in spatial transcriptomics datasets enables exploration of tumor architecture to gain deeper insights into the dynamics of the tumor microenvironment.

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Crossref · 10.1158/0008-5472.can-24-1472

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Authors
Liu, X., Tang, G., Chen, Y., Li, Y., Li, H., & Wang, X.
Affiliations
  • Washington University in St. Louis (US)
  • University of Illinois Chicago (US)
  • University of Illinois Urbana-Champaign (US)
Corresponding author
Wang, Xiaowei
Venue
Cancer Research
Published
2024-10-10
Volume
85
Issue
1
Pages
171-182
ISSN
  • 0008-5472 (ISSN-L)
  • 1538-7445
OpenAlex
W4403300146
Cited by
8
References
37
Citations by year
2026: 6 · 2025: 2
FWCI
1.09
Type
journal article
Language
en
Open access
Locations
  • Cancer Research (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Institutes of Health (R01DE026471, R01CA233873, R56DE033344, R01CA287778 and R35GM141535)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
cell type deconvolution, spatial transcriptomics, solid tumors, tumor microenvironment, tumor progression
MeSH terms
Animals, Humans, Neoplasms, Gene Expression Regulation, Neoplastic, Cluster Analysis, Gene Expression Profiling, Mice, Single-Cell Analysis, Tumor Microenvironment, Transcriptome

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