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Alleviating batch effects in cell type deconvolution with SCCAF-D

delete2024-12-30
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OA
AI
S
Shuo Feng
L
Liangfeng Huang
A
Anna Vathrakokoili Pournara
Z
Ziliang Huang
X
Xinlu Yang
Y
Yongjian Zhang
A
Alvis Brāzma
时
时明 (Ming Shi) *
DOI：10.1038/s41467-024-55213-xdelete
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Abstract

Abstract

En 中文
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.
Keywords:
HUMAN PANCREATIC-ISLETS
GENE-EXPRESSION
REVEALS
ASSOCIATION
FIBROSIS
ATLAS

Journal

Nature Communications cover
Nature Communications
IF:
15.7
Papers:
9.3W
Citations:
91.2W

Organization

No organization information available