diff --git a/content/04.study.md b/content/04.study.md index 021e7a6b..35b5c319 100644 --- a/content/04.study.md +++ b/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. diff --git a/content/citation-tags.tsv b/content/citation-tags.tsv index 2a6e3170..e302d04b 100644 --- a/content/citation-tags.tsv +++ b/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 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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 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@@ 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