Single-nucleus gene and gene set expression-based similarity network fusion identifies autism molecular subtypes
Abstract Background Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is highly phenotypically and genetically heterogeneous. With the accumulation of biological sequencing data, more and more studies shift to molecular subtype-first approach, from identifying molecular su...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-04-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-023-05278-0 |
| _version_ | 1797845719376199680 |
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| author | Junjie Zhang Guoli Ji Xilin Gao Jinting Guan |
| author_facet | Junjie Zhang Guoli Ji Xilin Gao Jinting Guan |
| author_sort | Junjie Zhang |
| collection | DOAJ |
| description | Abstract Background Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is highly phenotypically and genetically heterogeneous. With the accumulation of biological sequencing data, more and more studies shift to molecular subtype-first approach, from identifying molecular subtypes based on genetic and molecular data to linking molecular subtypes with clinical manifestation, which can reduce heterogeneity before phenotypic profiling. Results In this study, we perform similarity network fusion to integrate gene and gene set expression data of multiple human brain cell types for ASD molecular subtype identification. Then we apply subtype-specific differential gene and gene set expression analyses to study expression patterns specific to molecular subtypes in each cell type. To demonstrate the biological and practical significance, we analyze the molecular subtypes, investigate their correlation with ASD clinical phenotype, and construct ASD molecular subtype prediction models. Conclusions The identified molecular subtype-specific gene and gene set expression may be used to differentiate ASD molecular subtypes, facilitating the diagnosis and treatment of ASD. Our method provides an analytical pipeline for the identification of molecular subtypes and even disease subtypes of complex disorders. |
| first_indexed | 2024-04-09T17:43:30Z |
| format | Article |
| id | doaj.art-0b8b2550faac411587090ed5c0820d49 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-09T17:43:30Z |
| publishDate | 2023-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-0b8b2550faac411587090ed5c0820d492023-04-16T11:26:46ZengBMCBMC Bioinformatics1471-21052023-04-0124111610.1186/s12859-023-05278-0Single-nucleus gene and gene set expression-based similarity network fusion identifies autism molecular subtypesJunjie Zhang0Guoli Ji1Xilin Gao2Jinting Guan3Department of Automation, Xiamen UniversityDepartment of Automation, Xiamen UniversityXiamen Humanity Hospital, Fujian Medical UniversityDepartment of Automation, Xiamen UniversityAbstract Background Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is highly phenotypically and genetically heterogeneous. With the accumulation of biological sequencing data, more and more studies shift to molecular subtype-first approach, from identifying molecular subtypes based on genetic and molecular data to linking molecular subtypes with clinical manifestation, which can reduce heterogeneity before phenotypic profiling. Results In this study, we perform similarity network fusion to integrate gene and gene set expression data of multiple human brain cell types for ASD molecular subtype identification. Then we apply subtype-specific differential gene and gene set expression analyses to study expression patterns specific to molecular subtypes in each cell type. To demonstrate the biological and practical significance, we analyze the molecular subtypes, investigate their correlation with ASD clinical phenotype, and construct ASD molecular subtype prediction models. Conclusions The identified molecular subtype-specific gene and gene set expression may be used to differentiate ASD molecular subtypes, facilitating the diagnosis and treatment of ASD. Our method provides an analytical pipeline for the identification of molecular subtypes and even disease subtypes of complex disorders.https://doi.org/10.1186/s12859-023-05278-0Single-nucleus RNA-seq dataGene setSimilarity network fusionAutismMolecular subtype |
| spellingShingle | Junjie Zhang Guoli Ji Xilin Gao Jinting Guan Single-nucleus gene and gene set expression-based similarity network fusion identifies autism molecular subtypes BMC Bioinformatics Single-nucleus RNA-seq data Gene set Similarity network fusion Autism Molecular subtype |
| title | Single-nucleus gene and gene set expression-based similarity network fusion identifies autism molecular subtypes |
| title_full | Single-nucleus gene and gene set expression-based similarity network fusion identifies autism molecular subtypes |
| title_fullStr | Single-nucleus gene and gene set expression-based similarity network fusion identifies autism molecular subtypes |
| title_full_unstemmed | Single-nucleus gene and gene set expression-based similarity network fusion identifies autism molecular subtypes |
| title_short | Single-nucleus gene and gene set expression-based similarity network fusion identifies autism molecular subtypes |
| title_sort | single nucleus gene and gene set expression based similarity network fusion identifies autism molecular subtypes |
| topic | Single-nucleus RNA-seq data Gene set Similarity network fusion Autism Molecular subtype |
| url | https://doi.org/10.1186/s12859-023-05278-0 |
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