Systematic evaluation of the isolated effect of tissue environment on the transcriptome using a single-cell RNA-seq atlas dataset
Abstract Background Understanding cellular diversity throughout the body is essential for elucidating the complex functions of biological systems. Recently, large-scale single-cell omics datasets, known as omics atlases, have become available. These atlases encompass data from diverse tissues and ce...
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BMC
2025-04-01
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| Series: | BMC Genomics |
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| Online Access: | https://doi.org/10.1186/s12864-025-11614-w |
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| author | Daigo Okada Jianshen Zhu Kan Shota Yuuki Nishimura Kazuya Haraguchi |
| author_facet | Daigo Okada Jianshen Zhu Kan Shota Yuuki Nishimura Kazuya Haraguchi |
| author_sort | Daigo Okada |
| collection | DOAJ |
| description | Abstract Background Understanding cellular diversity throughout the body is essential for elucidating the complex functions of biological systems. Recently, large-scale single-cell omics datasets, known as omics atlases, have become available. These atlases encompass data from diverse tissues and cell-types, providing insights into the landscape of cell-type-specific gene expression. However, the isolated effect of the tissue environment has not been thoroughly investigated. Evaluating this isolated effect is challenging due to statistical confounding with cell-type effects, which arises from the highly limited subset of tissue-cell-type combinations that are biologically realized compared to the vast number of theoretical possibilities. Results This study introduces a novel data analysis framework, named the Combinatorial Sub-dataset Extraction for Confounding Reduction (COSER), which addresses statistical confounding by using graph theory to enumerate appropriate sub-datasets. COSER enables the assessment of isolated effects of discrete variables in single cells. Applying COSER to the Tabula Muris Senis single-cell transcriptome atlas, we characterized the isolated impact of tissue environments. Our findings demonstrate that some genes are markedly affected by the tissue environment, particularly in modulating intercellular diversity in immune responses and their age-related changes. Conclusion COSER provides a robust, general-purpose framework for evaluating the isolated effects of discrete variables from large-scale data mining. This approach reveals critical insights into the interplay between tissue environments and gene expression. |
| format | Article |
| id | doaj-art-c68ea5cd482d4414939465741b2b0680 |
| institution | DOAJ |
| issn | 1471-2164 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj-art-c68ea5cd482d4414939465741b2b06802025-08-20T02:55:31ZengBMCBMC Genomics1471-21642025-04-0126111410.1186/s12864-025-11614-wSystematic evaluation of the isolated effect of tissue environment on the transcriptome using a single-cell RNA-seq atlas datasetDaigo Okada0Jianshen Zhu1Kan Shota2Yuuki Nishimura3Kazuya Haraguchi4Center for Genomic Medicine, Graduate School of Medicine, Kyoto UniversityDiscrete Mathematics Laboratory, Applied Mathematics and Physics Course, Graduate School of Informatics, Kyoto UniversityDiscrete Mathematics Laboratory, Applied Mathematics and Physics Course, Graduate School of Informatics, Kyoto UniversityDiscrete Mathematics Laboratory, Applied Mathematics and Physics Course, Graduate School of Informatics, Kyoto UniversityDiscrete Mathematics Laboratory, Applied Mathematics and Physics Course, Graduate School of Informatics, Kyoto UniversityAbstract Background Understanding cellular diversity throughout the body is essential for elucidating the complex functions of biological systems. Recently, large-scale single-cell omics datasets, known as omics atlases, have become available. These atlases encompass data from diverse tissues and cell-types, providing insights into the landscape of cell-type-specific gene expression. However, the isolated effect of the tissue environment has not been thoroughly investigated. Evaluating this isolated effect is challenging due to statistical confounding with cell-type effects, which arises from the highly limited subset of tissue-cell-type combinations that are biologically realized compared to the vast number of theoretical possibilities. Results This study introduces a novel data analysis framework, named the Combinatorial Sub-dataset Extraction for Confounding Reduction (COSER), which addresses statistical confounding by using graph theory to enumerate appropriate sub-datasets. COSER enables the assessment of isolated effects of discrete variables in single cells. Applying COSER to the Tabula Muris Senis single-cell transcriptome atlas, we characterized the isolated impact of tissue environments. Our findings demonstrate that some genes are markedly affected by the tissue environment, particularly in modulating intercellular diversity in immune responses and their age-related changes. Conclusion COSER provides a robust, general-purpose framework for evaluating the isolated effects of discrete variables from large-scale data mining. This approach reveals critical insights into the interplay between tissue environments and gene expression.https://doi.org/10.1186/s12864-025-11614-wSingle cell RNA-seqEffect of tissue environmentGraph theoryMaximal biclique enumeration |
| spellingShingle | Daigo Okada Jianshen Zhu Kan Shota Yuuki Nishimura Kazuya Haraguchi Systematic evaluation of the isolated effect of tissue environment on the transcriptome using a single-cell RNA-seq atlas dataset BMC Genomics Single cell RNA-seq Effect of tissue environment Graph theory Maximal biclique enumeration |
| title | Systematic evaluation of the isolated effect of tissue environment on the transcriptome using a single-cell RNA-seq atlas dataset |
| title_full | Systematic evaluation of the isolated effect of tissue environment on the transcriptome using a single-cell RNA-seq atlas dataset |
| title_fullStr | Systematic evaluation of the isolated effect of tissue environment on the transcriptome using a single-cell RNA-seq atlas dataset |
| title_full_unstemmed | Systematic evaluation of the isolated effect of tissue environment on the transcriptome using a single-cell RNA-seq atlas dataset |
| title_short | Systematic evaluation of the isolated effect of tissue environment on the transcriptome using a single-cell RNA-seq atlas dataset |
| title_sort | systematic evaluation of the isolated effect of tissue environment on the transcriptome using a single cell rna seq atlas dataset |
| topic | Single cell RNA-seq Effect of tissue environment Graph theory Maximal biclique enumeration |
| url | https://doi.org/10.1186/s12864-025-11614-w |
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