Alleviating batch effects in cell type deconvolution with SCCAF-D

Le résumé fourni par la source

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.

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