Unravelling spatial gene associations with SEAGAL : a Python package for spatial transcriptomics data analysis and visualization
© The Author(s) 2023. Published by Oxford University Press..
SUMMARY: In the era where transcriptome profiling moves toward single-cell and spatial resolutions, the traditional co-expression analysis lacks the power to fully utilize such rich information to unravel spatial gene associations. Here, we present a Python package called Spatial Enrichment Analysis of Gene Associations using L-index (SEAGAL) to detect and visualize spatial gene correlations at both single-gene and gene-set levels. Our package takes spatial transcriptomics datasets with gene expression and the aligned spatial coordinates as input. It allows for analyzing and visualizing genes' spatial correlations and cell types' colocalization within the precise spatial context. The output could be visualized as volcano plots and heatmaps with a few lines of code, thus providing an easy-yet-comprehensive tool for mining spatial gene associations.
AVAILABILITY AND IMPLEMENTATION: The Python package SEAGAL can be installed using pip: https://pypi.org/project/seagal/. The source code and step-by-step tutorials are available at: https://github.com/linhuawang/SEAGAL.
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:39 |
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| Enthalten in: |
Bioinformatics (Oxford, England) - 39(2023), 7 vom: 01. Juli |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Linhua [VerfasserIn] |
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Journal Article |
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| Anmerkungen: |
Date Completed 26.07.2023 Date Revised 19.03.2024 published: Print UpdateOf: bioRxiv. 2023 Feb 13;:. - PMID 36824948 Citation Status MEDLINE |
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| doi: |
10.1093/bioinformatics/btad431 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM359375472 |
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| 100 | 1 | |a Wang, Linhua |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Unravelling spatial gene associations with SEAGAL |b a Python package for spatial transcriptomics data analysis and visualization |
| 264 | 1 | |c 2023 | |
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| 500 | |a Date Completed 26.07.2023 | ||
| 500 | |a Date Revised 19.03.2024 | ||
| 500 | |a published: Print | ||
| 500 | |a UpdateOf: bioRxiv. 2023 Feb 13;:. - PMID 36824948 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © The Author(s) 2023. Published by Oxford University Press. | ||
| 520 | |a SUMMARY: In the era where transcriptome profiling moves toward single-cell and spatial resolutions, the traditional co-expression analysis lacks the power to fully utilize such rich information to unravel spatial gene associations. Here, we present a Python package called Spatial Enrichment Analysis of Gene Associations using L-index (SEAGAL) to detect and visualize spatial gene correlations at both single-gene and gene-set levels. Our package takes spatial transcriptomics datasets with gene expression and the aligned spatial coordinates as input. It allows for analyzing and visualizing genes' spatial correlations and cell types' colocalization within the precise spatial context. The output could be visualized as volcano plots and heatmaps with a few lines of code, thus providing an easy-yet-comprehensive tool for mining spatial gene associations | ||
| 520 | |a AVAILABILITY AND IMPLEMENTATION: The Python package SEAGAL can be installed using pip: https://pypi.org/project/seagal/. The source code and step-by-step tutorials are available at: https://github.com/linhuawang/SEAGAL | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Liu, Chaozhong |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Yang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xiang H-F |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Zhandong |e verfasserin |4 aut | |
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