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Alleviating batch effects in cell type deconvolution with SCCAF-D

Year: 2024
Type: article
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. Cell type deconvolution is crucial for studying disease and development. Here, the authors present SCCAF-D, a workflow that reduces batch effects and achieves high correlation across tissues, offering key insights into cell proportion changes in non-alcoholic fatty liver disease.
DOI: 10.1038/s41467-024-55213-x
Authors: Shuo Feng, Liangfeng Huang, Anna Vathrakokoili Pournara, Ziliang Huang, Xinlu Yang, Yongjian Zhang, Alvis Brazma, Ming Shi, Irene Papatheodorou, Zhichao Miao
Institutions: University of Science and Technology of China, Guangzhou Laboratory, Guangzhou Medical University, Tongji University, European Bioinformatics Institute, Harbin Medical University, Harbin Institute of Technology, University of East Anglia, Norwich Research Park, Earlham Institute
Language: English
Open Access status: gold
Estimated APC paid: $6,790
OpenAlex ID: W4405894606
FWCI: 0.808
Cites: 96
Cited by: 7
Related to: 10