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Transcriptome and DNA methylation cell-type deconvolutions produce similar estimates of bulk differential gene expression and differential methylation [RNA-Seq]

ABSTRACT: Motivation: Changing cell-type proportions can confound studies of differential gene expression or DNA methylation (DNAm) from peripheral blood mononuclear cells (PBMCs). We examined how cell-type proportions derived from the transcriptome versus the methylome (DNAm) influence estimates of differentially expressed genes (DEGs) and differentially methylated positions (DMPs). Methods: Transcriptome and DNAm data were obtained from PBMC RNA and DNA of Kenyan children before, during, and 6 weeks following uncomplicated malaria. DEGs and DMPs between time points were detected using cell-type adjusted modelling with Cibersortx or IDOL, respectively. Results: Most major cell types and principal components had moderate to high correlation between the two deconvolution methods (r = 0.60-0.96). Estimates of cell-type proportions and DEGs or DMPs were largely unaffected by the method, with the greatest discrepancy in the estimation of neutrophils. Conclusion: Variation in cell-type proportions is captured similarly by both transcriptomic and methylome deconvolution methods for most major cell types.

ORGANISM(S): Homo sapiens

PROVIDER: GSE255053 | GEO | 2024/02/09

REPOSITORIES: GEO

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Transcriptome and DNA methylation cell-type deconvolutions produce similar estimates of bulk differential gene expression and differential methylation [bead array]

Project description:Motivation: Changing cell-type proportions can confound studies of differential gene expression or DNA methylation (DNAm) from peripheral blood mononuclear cells (PBMCs). We examined how cell-type proportions derived from the transcriptome versus the methylome (DNAm) influence estimates of differentially expressed genes (DEGs) and differentially methylated positions (DMPs). Methods: Transcriptome and DNAm data were obtained from PBMC RNA and DNA of Kenyan children before, during, and 6 weeks following uncomplicated malaria. DEGs and DMPs between time points were detected using cell-type adjusted modelling with Cibersortx or IDOL, respectively. Results: Most major cell types and principal components had moderate to high correlation between the two deconvolution methods (r = 0.60-0.96). Estimates of cell-type proportions and DEGs or DMPs were largely unaffected by the method, with the greatest discrepancy in the estimation of neutrophils. Conclusion: Variation in cell-type proportions is captured similarly by both transcriptomic and methylome deconvolution methods for most major cell types.

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