LSGI: interpretable spatial gradient analysis for spatial transcriptomics data

Cellular anatomy and signaling vary across niches, which can induce gradated gene expressions in subpopulations of cells. Such spatial transcriptomic gradient (STG) makes a significant source of intra-tumor heterogeneity. We present Local Spatial Gradient Inference (LSGI), a computational framework...

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Published inGenome Biology Vol. 26; no. 1; pp. 238 - 19
Main Authors Liang, Qingnan, Solis Soto, Luisa, Haymaker, Cara, Chen, Ken
Format Journal Article
LanguageEnglish
Published London BioMed Central 08.08.2025
Springer Nature B.V
BMC
Subjects
Advances in Spatial Transcriptomics for Understanding Development and Disease
Animal Genetics and Genomics
Bioinformatics
Biomedical and Life Sciences
Cells
Computational Biology - methods
Datasets
Evolutionary Biology
Gene expression
Gene Expression Profiling - methods
Genotype & phenotype
Glioma
Human Genetics
Humans
Hypoxia
Investigations
Life Sciences
Method
Microbial Genetics and Genomics
Neoplasms - genetics
Neoplasms - metabolism
Plant Genetics and Genomics
Transcriptome
Transcriptomics
Tumors
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ISSN1474-760X
1474-7596
1474-760X
DOI10.1186/s13059-025-03716-1

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Abstract Cellular anatomy and signaling vary across niches, which can induce gradated gene expressions in subpopulations of cells. Such spatial transcriptomic gradient (STG) makes a significant source of intra-tumor heterogeneity. We present Local Spatial Gradient Inference (LSGI), a computational framework that systematically identifies spatial locations with prominent, interpretable STGs from spatial transcriptomic (ST) data. We demonstrate LSGI in tumor ST datasets and identify pan-cancer and tumor-type specific pathways with gradated patterns, highlighting the ones related to spatial transcriptional intratumoral heterogeneity. LSGI enables interpretable STG analysis, which can reveal novel insights in tumor biology from the increasingly reported tumor ST datasets.
AbstractList Cellular anatomy and signaling vary across niches, which can induce gradated gene expressions in subpopulations of cells. Such spatial transcriptomic gradient (STG) makes a significant source of intra-tumor heterogeneity. We present Local Spatial Gradient Inference (LSGI), a computational framework that systematically identifies spatial locations with prominent, interpretable STGs from spatial transcriptomic (ST) data. We demonstrate LSGI in tumor ST datasets and identify pan-cancer and tumor-type specific pathways with gradated patterns, highlighting the ones related to spatial transcriptional intratumoral heterogeneity. LSGI enables interpretable STG analysis, which can reveal novel insights in tumor biology from the increasingly reported tumor ST datasets.
Cellular anatomy and signaling vary across niches, which can induce gradated gene expressions in subpopulations of cells. Such spatial transcriptomic gradient (STG) makes a significant source of intra-tumor heterogeneity. We present Local Spatial Gradient Inference (LSGI), a computational framework that systematically identifies spatial locations with prominent, interpretable STGs from spatial transcriptomic (ST) data. We demonstrate LSGI in tumor ST datasets and identify pan-cancer and tumor-type specific pathways with gradated patterns, highlighting the ones related to spatial transcriptional intratumoral heterogeneity. LSGI enables interpretable STG analysis, which can reveal novel insights in tumor biology from the increasingly reported tumor ST datasets.Cellular anatomy and signaling vary across niches, which can induce gradated gene expressions in subpopulations of cells. Such spatial transcriptomic gradient (STG) makes a significant source of intra-tumor heterogeneity. We present Local Spatial Gradient Inference (LSGI), a computational framework that systematically identifies spatial locations with prominent, interpretable STGs from spatial transcriptomic (ST) data. We demonstrate LSGI in tumor ST datasets and identify pan-cancer and tumor-type specific pathways with gradated patterns, highlighting the ones related to spatial transcriptional intratumoral heterogeneity. LSGI enables interpretable STG analysis, which can reveal novel insights in tumor biology from the increasingly reported tumor ST datasets.
Abstract Cellular anatomy and signaling vary across niches, which can induce gradated gene expressions in subpopulations of cells. Such spatial transcriptomic gradient (STG) makes a significant source of intra-tumor heterogeneity. We present Local Spatial Gradient Inference (LSGI), a computational framework that systematically identifies spatial locations with prominent, interpretable STGs from spatial transcriptomic (ST) data. We demonstrate LSGI in tumor ST datasets and identify pan-cancer and tumor-type specific pathways with gradated patterns, highlighting the ones related to spatial transcriptional intratumoral heterogeneity. LSGI enables interpretable STG analysis, which can reveal novel insights in tumor biology from the increasingly reported tumor ST datasets.
Cellular anatomy and signaling vary across niches, which can induce gradated gene expressions in subpopulations of cells. Such spatial transcriptomic gradient (STG) makes a significant source of intra-tumor heterogeneity. We present Local Spatial Gradient Inference (LSGI), a computational framework that systematically identifies spatial locations with prominent, interpretable STGs from spatial transcriptomic (ST) data. We demonstrate LSGI in tumor ST datasets and identify pan-cancer and tumor-type specific pathways with gradated patterns, highlighting the ones related to spatial transcriptional intratumoral heterogeneity. LSGI enables interpretable STG analysis, which can reveal novel insights in tumor biology from the increasingly reported tumor ST datasets.
ArticleNumber 238
Author Liang, Qingnan
Chen, Ken
Solis Soto, Luisa
Haymaker, Cara
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Snippet Cellular anatomy and signaling vary across niches, which can induce gradated gene expressions in subpopulations of cells. Such spatial transcriptomic gradient...
Abstract Cellular anatomy and signaling vary across niches, which can induce gradated gene expressions in subpopulations of cells. Such spatial transcriptomic...
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SubjectTerms Advances in Spatial Transcriptomics for Understanding Development and Disease
Animal Genetics and Genomics
Bioinformatics
Biomedical and Life Sciences
Cells
Computational Biology - methods
Datasets
Evolutionary Biology
Gene expression
Gene Expression Profiling - methods
Genotype & phenotype
Glioma
Human Genetics
Humans
Hypoxia
Investigations
Life Sciences
Method
Microbial Genetics and Genomics
Neoplasms - genetics
Neoplasms - metabolism
Plant Genetics and Genomics
Transcriptome
Transcriptomics
Tumors
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Title LSGI: interpretable spatial gradient analysis for spatial transcriptomics data
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