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TChIP-Seq: Cell-Type-Specific Epigenome Profiling
Published on: January 23, 2019
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Using R for Cell-Type Composition Imputation in Epigenome-Wide Association Studies.
1Department of Statistics, Florida State University, Tallahassee, FL, USA. cwu3@fsu.edu.
Methods in Molecular Biology (Clifton, N.J.)
|May 3, 2022
Summary
This chapter details methods for imputing cell type composition in whole blood, crucial for epigenome-wide association studies (EWAS). We explain R-based reference and reference-free approaches for accurate analysis.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Epigenome-wide association studies (EWAS) are powerful tools for understanding disease mechanisms.
- Accurate adjustment for cell type composition is essential for reliable EWAS results.
- Existing methods for cell type deconvolution can be complex and computationally intensive.
Purpose of the Study:
- To provide accessible R-based methods for imputing cell type composition in whole blood samples.
- To compare reference-based and reference-free approaches for cell type deconvolution.
- To facilitate the application of EWAS in diverse research settings.
Main Methods:
- Implementation of reference-based deconvolution algorithms in R.
- Application of reference-free deconvolution algorithms in R.
- Utilizing whole blood samples for method validation.
Main Results:
- Demonstration of accurate cell type composition imputation using both reference-based and reference-free methods.
- Comparison of the performance and applicability of the two approaches.
- R code provided for easy implementation by researchers.
Conclusions:
- Imputing cell type composition is feasible and critical for robust EWAS.
- Both reference-based and reference-free methods offer viable solutions for whole blood samples.
- The described R packages and methods empower researchers to perform accurate cell type deconvolution.
