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Reliability and correlation of mixture cell correction in methylomic and transcriptomic blood data
Boris Chaumette1,2,3,4, Oussama Kebir5,6,7, Patrick A Dion8
1Department of Psychiatry, McGill University, Montreal, Canada. boris.chaumette@inserm.fr.
BMC Research Notes
|February 14, 2020
Summary
Cell type correction in blood omics data is reliable when samples are collected simultaneously. This method helps account for cellular heterogeneity in DNA methylome and RNA transcriptome studies, even with limited sample sizes.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Growing number of DNA methylome and RNA transcriptome studies.
- Blood samples contain a mixture of cell types, complicating data interpretation.
- Cell-type heterogeneity is a critical factor to consider in omics research.
Purpose of the Study:
- To test the correlation between cell-type composition corrections in heterogeneous omics datasets.
- To evaluate the reliability of correcting for cell-type heterogeneity in blood samples.
- To determine if omics data can be used to correct other omics datasets for cell fractions.
Main Methods:
- Utilized methylome and transcriptome datasets from ten individuals sampled at two timepoints.
- Employed CIBERSORT for transcriptome cell-type deconvolution.
- Used the estimateCellCounts function in R for methylome cell-type deconvolution.
Main Results:
- Correlation coefficients between omics datasets ranged from 0.45 to 0.81.
- Correlations were minimal between different timepoints.
- A posteriori correction for cell mixtures in blood samples was found to be reliable.
Conclusions:
- Correcting omics data for cell-type composition is reliable, especially when samples are collected simultaneously.
- Using one omics dataset to correct another for cell fractions is applicable under simultaneous collection.
- This approach can aid in controlling for cell types in second datasets, even with small sample sizes.
