DNA Methylation introduction section

content/04.study.md

@@ -49,6 +49,26 @@ For example, the effects of cellular heterogeneity on basic biology and disease
 
 ### DNA methylation
 
+DNA methylation, is the process of adding a methyl group to a cytosine in the context of a CpG dinucleotide. 
+This DNA-level epigenetic modification regulates gene transcription, is critical in development, and alterations to DNA methylation are well-established as contributing to pathophysiology of many diseases including cancers [@tag:Roberston2005],[@tag:Feinberg2018]. 
+Studies of DNA methylation have demonstrated the fundamental role of DNA methylation in cell lineage specification starting with stem cell differentiation [@tag:Meissner2008], [@tag:Nazor2012], as well as a strong relationship of DNA methylation with aging phenotypes [@tag:Kwabi-Addo2007], [@tag:Fraga2005], and pathogenesis in response to environmental exposures [@tag:Christensen2009], [@tag:Relton2010]. 
+
+Traditional analytic approaches to DNA methylation data often focus on estimating differential DNA methylation between groups or related with an outcome using linear mixed effects models, so-called epigenome-wide association studies (EWAS) [@tag:Laird2010], [@tag:Wilhelm-Benartzi2013], [@tag:Liu2013], [@tag:Teschendorff2017]. 
+In addition, a growing application of DNA methylation measures is to infer cellular or subject phenotypes from samples and either examine the relation of these phenotypes with outcomes or disease states directly, and/or include them in models as covariates [@tag:Titus2017], [@tag:Salas2018_GR], [@tag:Zhang2019], [@tag:Horvath2014], [@tag:Quach2017]. 
+For example, inference of subject age using DNA methylation clock approaches are  established [@tag:Horvath2013], and are starting to be applied to test the relation of biological age with disease risk and outcomes [@tag:Kresovich2019]. 
+Different cell types have different DNA methylation profiles.
+A novel approach to immunophenotyping combines measurements with Reference DNA methylation profiles of leukocytes, which are available, to infer immune cell type proportions [@tag:Houseman2012],[@tag:Salas2018].
+This strategy is particularly helpful when only DNA is available from a sample.
+Cell type inference is important for adjusting for cell-type composition in epigenome-wide association studies [@tag:Teschendorff2017].
+While reference-based libraries have strong predictive value for immune cell type estimation and has broad utility, cell composition mixtures per se, and methods to incorporate estimates of mixtures pose important considerations on the interpretation of underlying biology associated with disease manifestations and phenotypes. 
+When a reference library is not available for use, methods that do not rely on these reference libraries, so called reference-free deconvolution [@tag:Houseman2016], are available to decompose signal purported to be contributed by cell types.
+However, using reference-free cell type proportion estimates as potential confounders in adjusted models can be overly conservative as as outcome-associated variation in DNA methylation may be decomposed into putative cell type estimates.
+Additional validated reference-based libraries for other tissue types, advancements in reference-free deconvolution methods, and application of deep learning methods are expected to provide new opportunities to understand and interpret DNA methylation in human health and disease.
+
+Deep learning approaches have numerous potential applications for DNA methylation data.
+Imputation methods that capture complex interactions between different regions of DNA can expand the number of CpG sites whose DNA methylation state can be studied, and ideally these methods can derive their own informative, biologically-relevant features.
+The primary deep learning methods developed to date focus on: 1) estimating regions of methylation status and imputing missing methylation values, 2) performing classification and regression tasks, and 3) using the latent embeddings of methylation states to derive biologically meaningful features, infer interpolated disease states, and uncover CpG sites that aid the above prediction tasks.
+
 #### Inference, imputation, and prediction
 
 Deep learning approaches are beginning to help address some of the current limitations of feature-by-feature analysis approaches to DNA methylation data and may help uncover additional important features necessary to understand the biological underpinnings behind different pathological states.

content/citation-tags.tsv

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 Angermueller2016_dl_review	doi:10.15252/msb.20156651
 Angermueller2016_single_methyl	doi:10.1186/s13059-017-1189-z
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 Choi2016_retain	arxiv:1608.05745
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 Hoff	doi:10.1093/nar/gkp327
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 Litjens2017_medimage_survey	doi:10.1016/j.media.2017.07.005
 Lisboa2006_review	doi:10.1016/j.neunet.2005.10.007
+Liu2013	doi:10.1038/nbt.2487
 Liu	doi:10.1371/journal.pone.0053253
 Liu2016_towards	arxiv:1604.07043
 Liu2016_sc_transcriptome	doi:10.12688/f1000research.7223.1
@@ -169,18 +173,18 @@ Mapreduce	doi:10.1145/1327452.1327492
 Mayr2016_deep_tox	doi:10.3389/fenvs.2015.00080
 McHardy	doi:10.1038/nmeth976
 McHardy2	doi:10.7717/peerj.1603
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 Nie2016_3d_survival	doi:10.1007/978-3-319-46723-8_25
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+Quach2017	doi:10.18632/aging.101168
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@@ -261,7 +267,8 @@ Tan2015_adage	doi:10.1128/mSystems.00025-15
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