Alleviating batch effects in cell type deconvolution with SCCAF-D

Shuo Feng; Liangfeng Huang; Anna Vathrakokoili Pournara; Ziliang Huang; Xinlu Yang; Yongjian Zhang; Alvis Brazma; Ming Shi; Irene Papatheodorou; Zhichao Miao

doi:10.1038/s41467-024-55213-x

Alleviating batch effects in cell type deconvolution with SCCAF-D

Nat Commun. 2024 Dec 30;15(1):10867. doi: 10.1038/s41467-024-55213-x.

Authors

Shuo Feng^#^{1

2}, Liangfeng Huang^#^{1

3}, Anna Vathrakokoili Pournara⁴, Ziliang Huang¹, Xinlu Yang⁵, Yongjian Zhang⁶, Alvis Brazma⁴, Ming Shi⁷, Irene Papatheodorou^{8

9}, Zhichao Miao^{10

11

12}

Affiliations

¹ GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macao Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Laboratory, Guangzhou Medical University, Guangzhou, China.
² Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, China.
³ Translational Research Institute of Brain and Brain-Like Intelligence and Department of Anesthesiology, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, China.
⁴ European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Cambridge, CB10 1SD, UK.
⁵ Department of Obstetrics and Gynaecology, Harbin Red Cross Central Hospital, Harbin, 150001, China.
⁶ Harbin Medical University the Sixth Affiliated Hospital, Harbin, 150023, China.
⁷ School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China. [email protected].
⁸ Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ, UK. [email protected].
⁹ Medical School, University of East Anglia, Norwich Research Park, Norwich, NR4 7UA, UK. [email protected].
¹⁰ GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macao Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Laboratory, Guangzhou Medical University, Guangzhou, China. [email protected].
¹¹ Translational Research Institute of Brain and Brain-Like Intelligence and Department of Anesthesiology, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, China. [email protected].
¹² European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Cambridge, CB10 1SD, UK. [email protected].

^# Contributed equally.

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.

MeSH terms

Algorithms
Animals
Computational Biology / methods
Computer Simulation
Disease Progression
Gene Expression Profiling / methods
Humans
Mice
Non-alcoholic Fatty Liver Disease* / genetics
Non-alcoholic Fatty Liver Disease* / metabolism
Non-alcoholic Fatty Liver Disease* / pathology
Software
Transcriptome
Workflow