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SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes
Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identificat...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10296445/ https://www.ncbi.nlm.nih.gov/pubmed/37371475 http://dx.doi.org/10.3390/biom13060895 |
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author | Meng-Lin, Kevin Ung, Choong-Yong Zhang, Cheng Weiskittel, Taylor M. Wisniewski, Philip Zhang, Zhuofei Tan, Shyang-Hong Yeo, Kok-Siong Zhu, Shizhen Correia, Cristina Li, Hu |
author_facet | Meng-Lin, Kevin Ung, Choong-Yong Zhang, Cheng Weiskittel, Taylor M. Wisniewski, Philip Zhang, Zhuofei Tan, Shyang-Hong Yeo, Kok-Siong Zhu, Shizhen Correia, Cristina Li, Hu |
author_sort | Meng-Lin, Kevin |
collection | PubMed |
description | Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of differentially expressed genes and permits us to understand how genes and associated molecular processes are spatially distributed within cellular niches. However, the expression activities of SVGs fail to encode all information inherent in the spatial distribution of cells. Here, we devised a deep learning model, Spatially Informed Artificial Intelligence (SPIN-AI), to identify spatially predictive genes (SPGs), whose expression can predict how cells are organized in space. We used SPIN-AI on spatial transcriptomic data from squamous cell carcinoma (SCC) as a proof of concept. Our results demonstrate that SPGs not only recapitulate the biology of SCC but also identify genes distinct from SVGs. Moreover, we found a substantial number of ribosomal genes that were SPGs but not SVGs. Since SPGs possess the capability to predict spatial cellular organization, we reason that SPGs capture more biologically relevant information for a given cellular niche than SVGs. Thus, SPIN-AI has broad applications for detecting SPGs and uncovering which biological processes play important roles in governing cellular organization. |
format | Online Article Text |
id | pubmed-10296445 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102964452023-06-28 SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes Meng-Lin, Kevin Ung, Choong-Yong Zhang, Cheng Weiskittel, Taylor M. Wisniewski, Philip Zhang, Zhuofei Tan, Shyang-Hong Yeo, Kok-Siong Zhu, Shizhen Correia, Cristina Li, Hu Biomolecules Article Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of differentially expressed genes and permits us to understand how genes and associated molecular processes are spatially distributed within cellular niches. However, the expression activities of SVGs fail to encode all information inherent in the spatial distribution of cells. Here, we devised a deep learning model, Spatially Informed Artificial Intelligence (SPIN-AI), to identify spatially predictive genes (SPGs), whose expression can predict how cells are organized in space. We used SPIN-AI on spatial transcriptomic data from squamous cell carcinoma (SCC) as a proof of concept. Our results demonstrate that SPGs not only recapitulate the biology of SCC but also identify genes distinct from SVGs. Moreover, we found a substantial number of ribosomal genes that were SPGs but not SVGs. Since SPGs possess the capability to predict spatial cellular organization, we reason that SPGs capture more biologically relevant information for a given cellular niche than SVGs. Thus, SPIN-AI has broad applications for detecting SPGs and uncovering which biological processes play important roles in governing cellular organization. MDPI 2023-05-27 /pmc/articles/PMC10296445/ /pubmed/37371475 http://dx.doi.org/10.3390/biom13060895 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Meng-Lin, Kevin Ung, Choong-Yong Zhang, Cheng Weiskittel, Taylor M. Wisniewski, Philip Zhang, Zhuofei Tan, Shyang-Hong Yeo, Kok-Siong Zhu, Shizhen Correia, Cristina Li, Hu SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes |
title | SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes |
title_full | SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes |
title_fullStr | SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes |
title_full_unstemmed | SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes |
title_short | SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes |
title_sort | spin-ai: a deep learning model that identifies spatially predictive genes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10296445/ https://www.ncbi.nlm.nih.gov/pubmed/37371475 http://dx.doi.org/10.3390/biom13060895 |
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