Geographically weighted linear combination test for gene-set analysis of a continuous spatial phenotype as applied to intratumor heterogeneity Article Swipe
Payam Amini
,
Morteza Hajihosseini
,
Saumyadipta Pyne
,
Irina Dinu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fcell.2023.1065586
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fcell.2023.1065586
Background: The impact of gene-sets on a spatial phenotype is not necessarily uniform across different locations of cancer tissue. This study introduces a computational platform, GWLCT, for combining gene set analysis with spatial data modeling to provide a new statistical test for location-specific association of phenotypes and molecular pathways in spatial single-cell RNA-seq data collected from an input tumor sample. Methods: The main advantage of GWLCT consists of an analysis beyond global significance, allowing the association between the gene-set and the phenotype to vary across the tumor space. At each location, the most significant linear combination is found using a geographically weighted shrunken covariance matrix and kernel function. Whether a fixed or adaptive bandwidth is determined based on a cross-validation cross procedure. Our proposed method is compared to the global version of linear combination test (LCT), bulk and random-forest based gene-set enrichment analyses using data created by the Visium Spatial Gene Expression technique on an invasive breast cancer tissue sample, as well as 144 different simulation scenarios. Results: In an illustrative example, the new geographically weighted linear combination test, GWLCT, identifies the cancer hallmark gene-sets that are significantly associated at each location with the five spatially continuous phenotypic contexts in the tumors defined by different well-known markers of cancer-associated fibroblasts. Scan statistics revealed clustering in the number of significant gene-sets. A spatial heatmap of combined significance over all selected gene-sets is also produced. Extensive simulation studies demonstrate that our proposed approach outperforms other methods in the considered scenarios, especially when the spatial association increases. Conclusion: Our proposed approach considers the spatial covariance of gene expression to detect the most significant gene-sets affecting a continuous phenotype. It reveals spatially detailed information in tissue space and can thus play a key role in understanding the contextual heterogeneity of cancer cells.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fcell.2023.1065586
- OA Status
- gold
- Cited By
- 2
- References
- 103
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4323667862Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fcell.2023.1065586Digital Object Identifier
- Title
-
Geographically weighted linear combination test for gene-set analysis of a continuous spatial phenotype as applied to intratumor heterogeneityWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-03-09Full publication date if available
- Authors
-
Payam Amini, Morteza Hajihosseini, Saumyadipta Pyne, Irina DinuList of authors in order
- Landing page
-
https://doi.org/10.3389/fcell.2023.1065586Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3389/fcell.2023.1065586Direct OA link when available
- Concepts
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Cluster analysis, Computational biology, Statistical hypothesis testing, Linear model, Set (abstract data type), Phenotype, Spatial analysis, Computer science, Biology, Data mining, Gene, Mathematics, Genetics, Statistics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
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2024: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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