Cargando…
Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA
Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality, and the prevalence of dropout eve...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9877026/ https://www.ncbi.nlm.nih.gov/pubmed/36697410 http://dx.doi.org/10.1038/s41467-023-36134-7 |
_version_ | 1784878295738744832 |
---|---|
author | Yu, Zhuohan Su, Yanchi Lu, Yifu Yang, Yuning Wang, Fuzhou Zhang, Shixiong Chang, Yi Wong, Ka-Chun Li, Xiangtao |
author_facet | Yu, Zhuohan Su, Yanchi Lu, Yifu Yang, Yuning Wang, Fuzhou Zhang, Shixiong Chang, Yi Wong, Ka-Chun Li, Xiangtao |
author_sort | Yu, Zhuohan |
collection | PubMed |
description | Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality, and the prevalence of dropout events. To address these concerns, we develop a deep graph learning method, scMGCA, for single-cell data analysis. scMGCA is based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments. We show that scMGCA is accurate and effective for cell segregation and batch effect correction, outperforming other state-of-the-art models across multiple platforms. In addition, we perform genomic interpretation on the key compressed transcriptomic space of the graph-embedding autoencoder to demonstrate the underlying gene regulation mechanism. We demonstrate that in a pancreatic ductal adenocarcinoma dataset, scMGCA successfully provides annotations on the specific cell types and reveals differential gene expression levels across multiple tumor-associated and cell signalling pathways. |
format | Online Article Text |
id | pubmed-9877026 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98770262023-01-27 Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA Yu, Zhuohan Su, Yanchi Lu, Yifu Yang, Yuning Wang, Fuzhou Zhang, Shixiong Chang, Yi Wong, Ka-Chun Li, Xiangtao Nat Commun Article Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality, and the prevalence of dropout events. To address these concerns, we develop a deep graph learning method, scMGCA, for single-cell data analysis. scMGCA is based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments. We show that scMGCA is accurate and effective for cell segregation and batch effect correction, outperforming other state-of-the-art models across multiple platforms. In addition, we perform genomic interpretation on the key compressed transcriptomic space of the graph-embedding autoencoder to demonstrate the underlying gene regulation mechanism. We demonstrate that in a pancreatic ductal adenocarcinoma dataset, scMGCA successfully provides annotations on the specific cell types and reveals differential gene expression levels across multiple tumor-associated and cell signalling pathways. Nature Publishing Group UK 2023-01-25 /pmc/articles/PMC9877026/ /pubmed/36697410 http://dx.doi.org/10.1038/s41467-023-36134-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Yu, Zhuohan Su, Yanchi Lu, Yifu Yang, Yuning Wang, Fuzhou Zhang, Shixiong Chang, Yi Wong, Ka-Chun Li, Xiangtao Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA |
title | Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA |
title_full | Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA |
title_fullStr | Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA |
title_full_unstemmed | Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA |
title_short | Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA |
title_sort | topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scmgca |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9877026/ https://www.ncbi.nlm.nih.gov/pubmed/36697410 http://dx.doi.org/10.1038/s41467-023-36134-7 |
work_keys_str_mv | AT yuzhuohan topologicalidentificationandinterpretationforsinglecellgeneregulationelucidationacrossmultipleplatformsusingscmgca AT suyanchi topologicalidentificationandinterpretationforsinglecellgeneregulationelucidationacrossmultipleplatformsusingscmgca AT luyifu topologicalidentificationandinterpretationforsinglecellgeneregulationelucidationacrossmultipleplatformsusingscmgca AT yangyuning topologicalidentificationandinterpretationforsinglecellgeneregulationelucidationacrossmultipleplatformsusingscmgca AT wangfuzhou topologicalidentificationandinterpretationforsinglecellgeneregulationelucidationacrossmultipleplatformsusingscmgca AT zhangshixiong topologicalidentificationandinterpretationforsinglecellgeneregulationelucidationacrossmultipleplatformsusingscmgca AT changyi topologicalidentificationandinterpretationforsinglecellgeneregulationelucidationacrossmultipleplatformsusingscmgca AT wongkachun topologicalidentificationandinterpretationforsinglecellgeneregulationelucidationacrossmultipleplatformsusingscmgca AT lixiangtao topologicalidentificationandinterpretationforsinglecellgeneregulationelucidationacrossmultipleplatformsusingscmgca |