CNTools: A computational toolbox for cellular neighborhood analysis from multiplexed images.
Recent studies show that cellular neighborhoods play an important role in evolving biological events such as cancer and diabetes. Therefore, it is critical to accurately and efficiently identify cellular neighborhoods from spatially-resolved single-cell transcriptomic data or single-cell resolution...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-08-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012344 |
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| author | Yicheng Tao Fan Feng Xin Luo Conrad V Reihsmann Alexander L Hopkirk Jean-Philippe Cartailler Marcela Brissova Stephen C J Parker Diane C Saunders Jie Liu |
| author_facet | Yicheng Tao Fan Feng Xin Luo Conrad V Reihsmann Alexander L Hopkirk Jean-Philippe Cartailler Marcela Brissova Stephen C J Parker Diane C Saunders Jie Liu |
| author_sort | Yicheng Tao |
| collection | DOAJ |
| description | Recent studies show that cellular neighborhoods play an important role in evolving biological events such as cancer and diabetes. Therefore, it is critical to accurately and efficiently identify cellular neighborhoods from spatially-resolved single-cell transcriptomic data or single-cell resolution tissue imaging data. In this work, we develop CNTools, a computational toolbox for end-to-end cellular neighborhood analysis on annotated cell images, comprising both the identification and analysis steps. It includes state-of-the-art cellular neighborhood identification methods and post-identification smoothing techniques, with our newly proposed Cellular Neighbor Embedding (CNE) method and Naive Smoothing technique, as well as several established downstream analysis approaches. We applied CNTools on three real-world CODEX datasets and evaluated identification methods with smoothing techniques quantitatively and qualitatively. It shows that CNE with Naive Smoothing overall outperformed other methods and revealed more convincing biological insights. We also provided suggestions on how to choose proper identification methods and smoothing techniques according to input data. |
| format | Article |
| id | doaj-art-fa5d8a4580c940c98d6914fa30f9c20e |
| institution | Matheson Library |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-fa5d8a4580c940c98d6914fa30f9c20e2025-07-24T05:30:59ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-08-01208e101234410.1371/journal.pcbi.1012344CNTools: A computational toolbox for cellular neighborhood analysis from multiplexed images.Yicheng TaoFan FengXin LuoConrad V ReihsmannAlexander L HopkirkJean-Philippe CartaillerMarcela BrissovaStephen C J ParkerDiane C SaundersJie LiuRecent studies show that cellular neighborhoods play an important role in evolving biological events such as cancer and diabetes. Therefore, it is critical to accurately and efficiently identify cellular neighborhoods from spatially-resolved single-cell transcriptomic data or single-cell resolution tissue imaging data. In this work, we develop CNTools, a computational toolbox for end-to-end cellular neighborhood analysis on annotated cell images, comprising both the identification and analysis steps. It includes state-of-the-art cellular neighborhood identification methods and post-identification smoothing techniques, with our newly proposed Cellular Neighbor Embedding (CNE) method and Naive Smoothing technique, as well as several established downstream analysis approaches. We applied CNTools on three real-world CODEX datasets and evaluated identification methods with smoothing techniques quantitatively and qualitatively. It shows that CNE with Naive Smoothing overall outperformed other methods and revealed more convincing biological insights. We also provided suggestions on how to choose proper identification methods and smoothing techniques according to input data.https://doi.org/10.1371/journal.pcbi.1012344 |
| spellingShingle | Yicheng Tao Fan Feng Xin Luo Conrad V Reihsmann Alexander L Hopkirk Jean-Philippe Cartailler Marcela Brissova Stephen C J Parker Diane C Saunders Jie Liu CNTools: A computational toolbox for cellular neighborhood analysis from multiplexed images. PLoS Computational Biology |
| title | CNTools: A computational toolbox for cellular neighborhood analysis from multiplexed images. |
| title_full | CNTools: A computational toolbox for cellular neighborhood analysis from multiplexed images. |
| title_fullStr | CNTools: A computational toolbox for cellular neighborhood analysis from multiplexed images. |
| title_full_unstemmed | CNTools: A computational toolbox for cellular neighborhood analysis from multiplexed images. |
| title_short | CNTools: A computational toolbox for cellular neighborhood analysis from multiplexed images. |
| title_sort | cntools a computational toolbox for cellular neighborhood analysis from multiplexed images |
| url | https://doi.org/10.1371/journal.pcbi.1012344 |
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