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Cell-type deconvolution in epigenome-wide association studies: a review and recommendations
Andrew E Teschendorff1,2,3, Shijie C Zheng1,4
1CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
Epigenomics
|May 19, 2017
Summary
Cell-type heterogeneity is a key challenge in epigenome-wide association studies (EWAS). This review critically examines statistical algorithms for correcting cell-type composition in EWAS using Illumina Beadarrays.
Area of Science:
- Epigenetics
- Genomics
- Bioinformatics
Background:
- Cell-type heterogeneity poses a significant challenge in epigenome-wide association studies (EWAS).
- Accurate adjustment for cell-type composition is crucial for interpreting EWAS findings.
- Numerous statistical algorithms have been developed to address this issue.
Purpose of the Study:
- To critically review major statistical algorithms for correcting cell-type composition in EWAS.
- To provide recommendations for the EWAS community on adjusting for cell-type heterogeneity.
- To focus the review on algorithms applicable to Illumina Infinium Methylation Beadarrays.
Main Methods:
- Literature review of existing statistical algorithms for EWAS cell-type correction.
- Categorization of algorithms into 'reference-based' and 'reference-free' methods.
- Critical evaluation of algorithm performance and applicability.
Main Results:
- The study reviews both reference-based and reference-free algorithms for cell-type correction.
- The optimal method for adjusting cell-type heterogeneity may depend on tissue type and phenotype.
- Recommendations are provided to guide the EWAS community.
Conclusions:
- Choosing the appropriate method for cell-type correction in EWAS is complex and context-dependent.
- Further research and community consensus are needed to establish best practices.
- This review aims to aid researchers in selecting suitable algorithms for their EWAS.
