Methods for estimating changes in DNA methylation in the presence of cell type heterogeneity
Bibliographic record
Abstract
DNA methylation occurring at a cytosine-guanine (CpG) site blocks binding tothe DNA and hence can influence gene function and regulation. Therefore, it is oftenvaluable to investigate which methylation sites are associated with diseases or otherphenotypes of interest. Though a large proportion of CpG sites in mammals aremethylated, methylation signatures differ notably between cell types. Consequently,when measuring methylation levels on whole blood or other types of tissues involvingmultiple cell types, it can be difficult to distinguish the changes associated witha phenotype of interest from those occurring as a result of varying proportions ofdifferent cell types among subjects. This phenomenon is of concern when changesin cell type proportion are associated with the phenotype itself, thereby making celltype proportion a confounder. There are several recently developed methods thatattempt to correct for this confounding, including one method based on an externalvalidation data set (Houseman et al., BMC Bioinformatics 2012), a reference-freemethod (Houseman et al., Bioinformatics 2014), Surrogate Variable Analysis (Leekand Storey, PLoS Genetics 2007), Independent Surrogate Variable Analysis (Teschendorff,Bioinformatics 2011), the FAST-LMM-EWASher method (Zou, Nature Methods2014), Deconfounding (Repsilber, BMC Bioinformatics 2010), and CellCDecon(Wagner, PhD Thesis 2014). In order to compare the performance of each method, wehave artificially re-combined measures of methylation obtained from cell-separatedanalysis of whole blood. Specifically, methylation measures are available for monocytesand CD4 T-cells. We randomly chose a subset of the samples to be disease cases, then we designated a set of CpG sites to be associated with the disease. Anew artificial set of methylation measurements was generated by combining the valuesfrom each cell type with variable proportions of each cell type in each subject.We uncovered notable differences between the methods in terms of statistical power,reduction in false discovery rate, the extent to which the confounding has been corrected,and in computational performance. The reference-based method, due to itsease of use and generally good performance, was selected as the best method underthe specified circumstances. ISVA was selected as the best alternative if no externaldata set were available.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".