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:Robertson2005],[@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

@@ -41,6 +41,7 @@ Chen2015_trans_species	doi:10.1093/bioinformatics/btv315
 Choi2016_retain	arxiv:1608.05745
 Choi2016_gram	arxiv:1611.07012
 Chollet2016_xception	arxiv:1610.02357
+Christensen2009	doi:10.1371/journal.pgen.1000602
 Chryssolouris1996_confidence	doi:10.1109/72.478409
 Ciresan2013_mitosis	doi:10.1007/978-3-642-40763-5_51
 Coates2013_cots_hpc	url:http://www.jmlr.org/proceedings/papers/v28/coates13.html
@@ -71,8 +72,10 @@ Esfahani2016_melanoma	doi:10.1109/EMBC.2016.7590963
 Essinger2010_taxonomic	doi:10.1109/IJCNN.2010.5596644
 Esteva2017_skin_cancer_nature	doi:10.1038/nature21056
 Faruqi	url:http://alifar76.github.io/sklearn-metrics/
+Feinberg2018	doi:10.1056/NEJMra1402513
 Finnegan2017_maximum	doi:10.1101/105957
 Fong2017_perturb	doi:10.1109/ICCV.2017.371
+Fraga2005	doi:10.1073/pnas.0500398102
 Fu2019	doi:10.1109/TCBB.2019.2909237
 Gal2015_dropout	arxiv:1506.02142
 Gaublomme2015_th17	doi:10.1016/j.cell.2015.11.009
@@ -100,6 +103,10 @@ Hinton2015_dk	arxiv:1503.02531v1
 Hochreiter	doi:10.1093/bioinformatics/btm247
 Hoff	doi:10.1093/nar/gkp327
 Horton1992_assessment	doi:10.1093/nar/20.16.4331
+Horvath2013	doi:10.1186/gb-2013-14-10-r115
+Horvath2014	doi:10.1073/pnas.1412759111
+Houseman2012	doi:10.1186/1471-2105-13-86
+Houseman2016	doi:10.1186/s12859-016-1140-4
 Hubara2016_qnn	arxiv:1609.07061
 Huddar2016_predicting	doi:10.1109/ACCESS.2016.2618775
 Hughes2016_macromol_react	doi:10.1021/acscentsci.6b00162
@@ -128,11 +135,14 @@ Kooi2016_mamm_lesions	doi:10.1016/j.media.2016.07.007
 Kooi2017_mamm_tl	doi:10.1002/mp.12110
 Korfiatis2017	doi:10.1007/s10278-017-0009-z
 Kraus2017_deeploc	doi:10.15252/msb.20177551
+Kresovich2019	doi:10.1093/jnci/djz020
 Krizhevsky2013_nips_cnn	url:https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
 Krizhevsky2014_weird_trick	arxiv:1404.5997
+Kwabi-Addo2007	doi:10.1158/1078-0432.CCR-07-0085
 Khwaja2017	doi:10.1109/BIOCAS.2017.8325078
 Khwaja2018	arxiv:1810.01243
 Lacey2016_dl_fpga	arxiv:1602.04283
+Laird2010	doi:10.1038/nrg2732
 Lakhani2017_radiography	doi:10.1148/radiol.2017162326
 Lanchantin2016_motif	arxiv:1608.03644
 Lee2016_deeptarget	arxiv:1603.09123v2
@@ -150,6 +160,7 @@ Lipton2015_lstm	arxiv:1510.07641
 Litjens2016_histopath_survey	doi:10.1038/srep26286
 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
@@ -169,6 +180,7 @@ 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
+Meissner2008	doi:10.1038/nature07107
 Metaphlan	doi:10.1038/nmeth.2066
 Meng2016_mllib	arxiv:1505.06807
 Min2016_deepenhancer	doi:10.1109/BIBM.2016.7822593
@@ -179,6 +191,7 @@ Mrzelj	url:https://repozitorij.uni-lj.si/IzpisGradiva.php?id=85515
 matis	doi:10.1016/S0097-8485(96)80015-5
 nbc	doi:10.1093/bioinformatics/btq619
 Murdoch2017_automatic	arxiv:1702.02540
+Nazor2012	doi:10.1016/j.stem.2012.02.013
 Nemati2016_rl	doi:10.1109/EMBC.2016.7591355
 Ni2018	doi:10.1101/385849
 Nguyen2014_adversarial	arxiv:1412.1897v4
@@ -200,6 +213,7 @@ PerezSianes2016_screening	doi:10.1007/978-3-319-40126-3_2
 Phymm	doi:10.1038/nmeth.1358
 Poplin2016_deepvariant	doi:10.1101/092890
 Pratt2016_dr	doi:10.1016/j.procs.2016.07.014
+Quach2017	doi:10.18632/aging.101168
 Quang2017_factor	doi:10.1101/151274
 Qin2017_onehot	doi:10.1371/journal.pcbi.1005403
 Qiu2017_graph_embedding	doi:10.1101/110668
@@ -212,13 +226,17 @@ Rakhlin2018_histology	doi:10.1101/259911
 Ramsundar2015_multitask_drug	arxiv:1502.02072
 Ranganath2016_deep	arxiv:1608.02158
 Raina2009_gpu	doi:10.1145/1553374.1553486
+Relton2010	doi:10.1371/journal.pmed.1000356
 Ribeiro2016_lime	arxiv:1602.04938
+Robertson2005	doi:10.1038/nrg1655
 Rogers2010_fingerprints	doi:10.1021/ci100050t
 Roth2015_view_agg_cad	doi:10.1109/TMI.2015.2482920
 Romero2017_diet	url:https://openreview.net/pdf?id=Sk-oDY9ge
 Rosenberg2015_synthetic_seqs	doi:10.1016/j.cell.2015.09.054
 Russakovsky2015_imagenet	doi:10.1007/s11263-015-0816-y
 Sa2015_buckwild	pmcid:PMC4907892
+Salas2018_GR	doi:10.1101/gr.233213.117
+Salas2018	doi:10.1186/s13059-018-1448-7
 Salzberg	doi:10.1186/1471-2105-11-544
 Schatz2010_dna_cloud	doi:10.1038/nbt0710-691
 Schmidhuber2014_dnn_overview	doi:10.1016/j.neunet.2014.09.003
@@ -261,7 +279,9 @@ Tan2015_adage	doi:10.1128/mSystems.00025-15
 Tan2016_eadage	doi:10.1101/078659
 TAC-ELM	doi:10.1142/S0219720012500151
 TensorFlow	arxiv:1603.04467
+Teschendorff2017	doi:10.2217/epi-2016-0153
 Tian2019	doi:10.1186/s12864-019-5488-5
+Titus2017	doi:10.1093/hmg/ddx275
 Torracinta2016_deep_snp	doi:10.1101/097469
 Torracinta2016_sim	doi:10.1101/079087
 Tu1996_anns	doi:10.1016/S0895-4356(96)00002-9
@@ -276,6 +296,7 @@ Wang2016_protein_contact	doi:10.1371/journal.pcbi.1005324
 Wasson1985_clinical	doi:10.1056/NEJM198509263131306
 WayGreene2017_eval	arxiv:1711.04828
 WayGreene2017_tybalt	doi:10.1101/174474
+Wilhelm-Benartzi2013	doi:10.1038/bjc.2013.496
 Word2Vec	arxiv:1301.3781
 wgsquikr	doi:10.1371/journal.pone.0091784
 Wu2017_molecule_net	doi:10.1039/C7SC02664A
@@ -294,6 +315,7 @@ Zeng2015	doi:10.1186/s12859-015-0553-9
 Zeng2016_convolutional	doi:10.1093/bioinformatics/btw255
 Zhang2015_multitask_tl	doi:10.1145/2783258.2783304
 Zhang2017_generalization	arxiv:1611.03530v2
+Zhang2019	doi:10.1186/s12885-019-5932-6
 Zhou2015_deep_sea	doi:10.1038/nmeth.3547
 Zhu2016_advers_mamm	doi:10.1101/095786
 Zhu2016_mult_inst_mamm	doi:10.1101/095794