CVAM: CNA Profile Inference of the Spatial Transcriptome Based on the VGAE and HMM

Tumors are often polyclonal due to copy number alteration (CNA) events. Through the CNA profile, we can understand the tumor heterogeneity and consistency. CNA information is usually obtained through DNA sequencing. However, many existing studies have shown a positive correlation between the gene ex...

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Veröffentlicht in:	Biomolecules (Basel, Switzerland) Jg. 13; H. 5; S. 767
Hauptverfasser:	Ma, Jian, Guo, Jingjing, Fan, Zhiwei, Zhao, Weiling, Zhou, Xiaobo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 28.04.2023 MDPI
Schlagworte:	Algorithms Analysis Chromosomes Cloning Copy number copy number alteration Copy number variations DNA Copy Number Variations - genetics DNA sequencing DNA testing Gene Dosage Gene expression Genetic aspects Genomes Genomics Health aspects HMM Humans Machine learning Methods Mutation Neoplasms - genetics Neural networks Normal distribution Spatial distribution spatial transcriptome Transcriptome - genetics Transcriptomes Tumors variational graph convolutional autoencoder China HMM spatial transcriptome variational graph convolutional autoencoder copy number alteration
ISSN:	2218-273X, 2218-273X
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Abstract	Tumors are often polyclonal due to copy number alteration (CNA) events. Through the CNA profile, we can understand the tumor heterogeneity and consistency. CNA information is usually obtained through DNA sequencing. However, many existing studies have shown a positive correlation between the gene expression and gene copy number identified from DNA sequencing. With the development of spatial transcriptome technologies, it is urgent to develop new tools to identify genomic variation from the spatial transcriptome. Therefore, in this study, we developed CVAM, a tool to infer the CNA profile from spatial transcriptome data. Compared with existing tools, CVAM integrates the spatial information with the spot’s gene expression information together and the spatial information is indirectly introduced into the CNA inference. By applying CVAM to simulated and real spatial transcriptome data, we found that CVAM performed better in identifying CNA events. In addition, we analyzed the potential co-occurrence and mutual exclusion between CNA events in tumor clusters, which is helpful to analyze the potential interaction between genes in mutation. Last but not least, Ripley’s K-function is also applied to CNA multi-distance spatial pattern analysis so that we can figure out the differences of different gene CNA events in spatial distribution, which is helpful for tumor analysis and implementing more effective treatment measures based on spatial characteristics of genes.
AbstractList	Tumors are often polyclonal due to copy number alteration (CNA) events. Through the CNA profile, we can understand the tumor heterogeneity and consistency. CNA information is usually obtained through DNA sequencing. However, many existing studies have shown a positive correlation between the gene expression and gene copy number identified from DNA sequencing. With the development of spatial transcriptome technologies, it is urgent to develop new tools to identify genomic variation from the spatial transcriptome. Therefore, in this study, we developed CVAM, a tool to infer the CNA profile from spatial transcriptome data. Compared with existing tools, CVAM integrates the spatial information with the spot’s gene expression information together and the spatial information is indirectly introduced into the CNA inference. By applying CVAM to simulated and real spatial transcriptome data, we found that CVAM performed better in identifying CNA events. In addition, we analyzed the potential co-occurrence and mutual exclusion between CNA events in tumor clusters, which is helpful to analyze the potential interaction between genes in mutation. Last but not least, Ripley’s K-function is also applied to CNA multi-distance spatial pattern analysis so that we can figure out the differences of different gene CNA events in spatial distribution, which is helpful for tumor analysis and implementing more effective treatment measures based on spatial characteristics of genes. Tumors are often polyclonal due to copy number alteration (CNA) events. Through the CNA profile, we can understand the tumor heterogeneity and consistency. CNA information is usually obtained through DNA sequencing. However, many existing studies have shown a positive correlation between the gene expression and gene copy number identified from DNA sequencing. With the development of spatial transcriptome technologies, it is urgent to develop new tools to identify genomic variation from the spatial transcriptome. Therefore, in this study, we developed CVAM, a tool to infer the CNA profile from spatial transcriptome data. Compared with existing tools, CVAM integrates the spatial information with the spot's gene expression information together and the spatial information is indirectly introduced into the CNA inference. By applying CVAM to simulated and real spatial transcriptome data, we found that CVAM performed better in identifying CNA events. In addition, we analyzed the potential co-occurrence and mutual exclusion between CNA events in tumor clusters, which is helpful to analyze the potential interaction between genes in mutation. Last but not least, Ripley's K-function is also applied to CNA multi-distance spatial pattern analysis so that we can figure out the differences of different gene CNA events in spatial distribution, which is helpful for tumor analysis and implementing more effective treatment measures based on spatial characteristics of genes.Tumors are often polyclonal due to copy number alteration (CNA) events. Through the CNA profile, we can understand the tumor heterogeneity and consistency. CNA information is usually obtained through DNA sequencing. However, many existing studies have shown a positive correlation between the gene expression and gene copy number identified from DNA sequencing. With the development of spatial transcriptome technologies, it is urgent to develop new tools to identify genomic variation from the spatial transcriptome. Therefore, in this study, we developed CVAM, a tool to infer the CNA profile from spatial transcriptome data. Compared with existing tools, CVAM integrates the spatial information with the spot's gene expression information together and the spatial information is indirectly introduced into the CNA inference. By applying CVAM to simulated and real spatial transcriptome data, we found that CVAM performed better in identifying CNA events. In addition, we analyzed the potential co-occurrence and mutual exclusion between CNA events in tumor clusters, which is helpful to analyze the potential interaction between genes in mutation. Last but not least, Ripley's K-function is also applied to CNA multi-distance spatial pattern analysis so that we can figure out the differences of different gene CNA events in spatial distribution, which is helpful for tumor analysis and implementing more effective treatment measures based on spatial characteristics of genes.
Audience	Academic
Author	Guo, Jingjing Fan, Zhiwei Zhou, Xiaobo Zhao, Weiling Ma, Jian
AuthorAffiliation	4 West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610040, China 2 West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, China 6 McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA 1 College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China 5 Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA 7 School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA 3 Med-X Center for Informatics, Sichuan University, Chengdu 610041, China
AuthorAffiliation_xml	– name: 1 College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China – name: 2 West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, China – name: 3 Med-X Center for Informatics, Sichuan University, Chengdu 610041, China – name: 4 West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610040, China – name: 5 Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA – name: 7 School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA – name: 6 McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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Cites_doi	10.1038/s41592-019-0701-7 10.1038/s41586-021-04217-4 10.1158/2767-9764.CRC-22-0299 10.1038/s41592-022-01409-2 10.1038/s41467-019-13779-x 10.1093/nar/gky131 10.1126/science.aaf2403 10.1038/nrc3893 10.1101/gr.269381.120 10.1088/1478-3975/abbe99 10.1016/j.molcel.2021.03.016 10.1158/2326-6066.CIR-13-0129 10.1038/s41576-019-0150-2 10.1038/s41586-019-1773-3 10.1038/nmeth.3370 10.1016/j.cell.2022.02.015 10.1093/bib/bbaa414 10.1016/j.jsg.2022.104531 10.1016/j.ccell.2018.03.007 10.1038/s41422-019-0195-y 10.1126/science.1254257 10.1038/s41587-019-0392-8 10.1093/nar/gkac947 10.1083/jcb.200307098 10.1016/j.cell.2020.05.039 10.1038/s41587-020-00795-2 10.1093/biostatistics/kxh008 10.1080/01621459.2017.1285773 10.1186/s12881-019-0909-5 10.1158/2159-8290.CD-12-0095
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Snippet	Tumors are often polyclonal due to copy number alteration (CNA) events. Through the CNA profile, we can understand the tumor heterogeneity and consistency. CNA...
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SubjectTerms	Algorithms Analysis Chromosomes Cloning Copy number copy number alteration Copy number variations DNA Copy Number Variations - genetics DNA sequencing DNA testing Gene Dosage Gene expression Genetic aspects Genomes Genomics Health aspects HMM Humans Machine learning Methods Mutation Neoplasms - genetics Neural networks Normal distribution Spatial distribution spatial transcriptome Transcriptome - genetics Transcriptomes Tumors variational graph convolutional autoencoder
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Title	CVAM: CNA Profile Inference of the Spatial Transcriptome Based on the VGAE and HMM
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