Alleviating batch effects in cell type deconvolution with SCCAF-D
Abstract Cell type deconvolution methods can impute cell proportions from bulk transcriptomics data, revealing changes in disease progression or organ development. But benchmarking studies often use simulated bulk data from the same source as the reference, which limits its application scenarios. Th...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55213-x |
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| author | Shuo Feng Liangfeng Huang Anna Vathrakokoili Pournara Ziliang Huang Xinlu Yang Yongjian Zhang Alvis Brazma Ming Shi Irene Papatheodorou Zhichao Miao |
| author_facet | Shuo Feng Liangfeng Huang Anna Vathrakokoili Pournara Ziliang Huang Xinlu Yang Yongjian Zhang Alvis Brazma Ming Shi Irene Papatheodorou Zhichao Miao |
| author_sort | Shuo Feng |
| collection | DOAJ |
| description | Abstract Cell type deconvolution methods can impute cell proportions from bulk transcriptomics data, revealing changes in disease progression or organ development. But benchmarking studies often use simulated bulk data from the same source as the reference, which limits its application scenarios. This study examines batch effects in deconvolution and introduces SCCAF-D, a computational workflow that ensures a Pearson Correlation Coefficient above 0.75 across simulated and real bulk data for various tissue types. Applied to non-alcoholic fatty liver disease, SCCAF-D unveils meaningful insights into changes in cell proportions during disease progression. |
| format | Article |
| id | doaj-art-9453ce019c554fba905c4a3d4e25de75 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-9453ce019c554fba905c4a3d4e25de752025-08-20T02:26:35ZengNature PortfolioNature Communications2041-17232024-12-0115111410.1038/s41467-024-55213-xAlleviating batch effects in cell type deconvolution with SCCAF-DShuo Feng0Liangfeng Huang1Anna Vathrakokoili Pournara2Ziliang Huang3Xinlu Yang4Yongjian Zhang5Alvis Brazma6Ming Shi7Irene Papatheodorou8Zhichao Miao9GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macao Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Laboratory, Guangzhou Medical UniversityGMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macao Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Laboratory, Guangzhou Medical UniversityEuropean Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome CampusGMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macao Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Laboratory, Guangzhou Medical UniversityDepartment of Obstetrics and Gynaecology, Harbin Red Cross Central HospitalHarbin Medical University the Sixth Affiliated HospitalEuropean Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome CampusSchool of Life Science and Technology, Harbin Institute of TechnologyEarlham Institute, Norwich Research ParkGMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macao Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Laboratory, Guangzhou Medical UniversityAbstract Cell type deconvolution methods can impute cell proportions from bulk transcriptomics data, revealing changes in disease progression or organ development. But benchmarking studies often use simulated bulk data from the same source as the reference, which limits its application scenarios. This study examines batch effects in deconvolution and introduces SCCAF-D, a computational workflow that ensures a Pearson Correlation Coefficient above 0.75 across simulated and real bulk data for various tissue types. Applied to non-alcoholic fatty liver disease, SCCAF-D unveils meaningful insights into changes in cell proportions during disease progression.https://doi.org/10.1038/s41467-024-55213-x |
| spellingShingle | Shuo Feng Liangfeng Huang Anna Vathrakokoili Pournara Ziliang Huang Xinlu Yang Yongjian Zhang Alvis Brazma Ming Shi Irene Papatheodorou Zhichao Miao Alleviating batch effects in cell type deconvolution with SCCAF-D Nature Communications |
| title | Alleviating batch effects in cell type deconvolution with SCCAF-D |
| title_full | Alleviating batch effects in cell type deconvolution with SCCAF-D |
| title_fullStr | Alleviating batch effects in cell type deconvolution with SCCAF-D |
| title_full_unstemmed | Alleviating batch effects in cell type deconvolution with SCCAF-D |
| title_short | Alleviating batch effects in cell type deconvolution with SCCAF-D |
| title_sort | alleviating batch effects in cell type deconvolution with sccaf d |
| url | https://doi.org/10.1038/s41467-024-55213-x |
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