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Reliability and correlation of mixture cell correction in methylomic and transcriptomic blood data. | LitMetric

AI Article Synopsis

  • The study investigates how DNA methylation and RNA transcriptome data can be affected by the mixture of cell types in blood samples, revealing the importance of considering cell composition in such analyses.
  • Researchers tested two datasets from blood samples taken at different times, using specialized algorithms to assess cell type composition.
  • The findings show a reliable method for correcting for cell mixture issues, but highlight that the correlation between datasets is strongest when the samples are collected simultaneously.

Article Abstract

Objectives: The number of DNA methylome and RNA transcriptome studies is growing, but investigators have to consider the cell type composition of tissues used. In blood samples, the data reflect the picture of a mixture of different cells. Specialized algorithms can address the cell-type heterogeneity issue. We tested if these corrections are correlated between two heterogeneous datasets.

Results: We used methylome and transcriptome datasets derived from a cohort of ten individuals whose blood was sampled at two different timepoints. We examined how the cell composition derived from these omics correlated with each other using "CIBERSORT" for the transcriptome and "estimateCellCounts function" in R for the methylome. The correlation coefficients between the two omic datasets ranged from 0.45 to 0.81 but correlations were minimal between two different timepoints. Our results suggest that a posteriori correction of a mixture of cells present in blood samples is reliable. Using an omic dataset to correct a second dataset for relative fractions of cells appears to be applicable, but only when the samples are simultaneously collected. This could be beneficial when there are difficulties to control the cell types in the second dataset, even when the sample size is limited.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7017605PMC
http://dx.doi.org/10.1186/s13104-020-4936-2DOI Listing

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