Alleviating batch effects in cell type deconvolution with SCCAF-D.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 39738054.
- Also identified by DOI 10.1038/s41467-024-55213-x and PMC identifier 11686230.
- Licence recorded as CC BY-NC-ND.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
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.
Medical subject headings
- Non-alcoholic Fatty Liver Disease