Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity
Abstract Advances in single-cell technology have enabled molecular dissection of heterogeneous biospecimens at unprecedented scales and resolutions. Cluster-centric approaches are widely applied in analyzing single-cell data, however they have limited power in dissecting and interpreting highly hete...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-12-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-44206-x |
| _version_ | 1797376949764489216 |
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| author | Qingnan Liang Yuefan Huang Shan He Ken Chen |
| author_facet | Qingnan Liang Yuefan Huang Shan He Ken Chen |
| author_sort | Qingnan Liang |
| collection | DOAJ |
| description | Abstract Advances in single-cell technology have enabled molecular dissection of heterogeneous biospecimens at unprecedented scales and resolutions. Cluster-centric approaches are widely applied in analyzing single-cell data, however they have limited power in dissecting and interpreting highly heterogenous, dynamically evolving data. Here, we present GSDensity, a graph-modeling approach that allows users to obtain pathway-centric interpretation and dissection of single-cell and spatial transcriptomics (ST) data without performing clustering. Using pathway gene sets, we show that GSDensity can accurately detect biologically distinct cells and reveal novel cell-pathway associations ignored by existing methods. Moreover, GSDensity, combined with trajectory analysis can identify curated pathways that are active at various stages of mouse brain development. Finally, GSDensity can identify spatially relevant pathways in mouse brains and human tumors including those following high-order organizational patterns in the ST data. Particularly, we create a pan-cancer ST map revealing spatially relevant and recurrently active pathways across six different tumor types. |
| first_indexed | 2024-03-08T19:45:58Z |
| format | Article |
| id | doaj.art-fb2a588565874c8da9a256fede687c77 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-08T19:45:58Z |
| publishDate | 2023-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-fb2a588565874c8da9a256fede687c772023-12-24T12:22:56ZengNature PortfolioNature Communications2041-17232023-12-0114111710.1038/s41467-023-44206-xPathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensityQingnan Liang0Yuefan Huang1Shan He2Ken Chen3Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer CenterDepartment of Bioinformatics and Computational Biology, UT MD Anderson Cancer CenterDepartment of Bioinformatics and Computational Biology, UT MD Anderson Cancer CenterDepartment of Bioinformatics and Computational Biology, UT MD Anderson Cancer CenterAbstract Advances in single-cell technology have enabled molecular dissection of heterogeneous biospecimens at unprecedented scales and resolutions. Cluster-centric approaches are widely applied in analyzing single-cell data, however they have limited power in dissecting and interpreting highly heterogenous, dynamically evolving data. Here, we present GSDensity, a graph-modeling approach that allows users to obtain pathway-centric interpretation and dissection of single-cell and spatial transcriptomics (ST) data without performing clustering. Using pathway gene sets, we show that GSDensity can accurately detect biologically distinct cells and reveal novel cell-pathway associations ignored by existing methods. Moreover, GSDensity, combined with trajectory analysis can identify curated pathways that are active at various stages of mouse brain development. Finally, GSDensity can identify spatially relevant pathways in mouse brains and human tumors including those following high-order organizational patterns in the ST data. Particularly, we create a pan-cancer ST map revealing spatially relevant and recurrently active pathways across six different tumor types.https://doi.org/10.1038/s41467-023-44206-x |
| spellingShingle | Qingnan Liang Yuefan Huang Shan He Ken Chen Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity Nature Communications |
| title | Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity |
| title_full | Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity |
| title_fullStr | Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity |
| title_full_unstemmed | Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity |
| title_short | Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity |
| title_sort | pathway centric analysis for single cell rna seq and spatial transcriptomics data with gsdensity |
| url | https://doi.org/10.1038/s41467-023-44206-x |
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