Epigenome-wide association studies without the need for cell-type composition.
about
Establishing an analytic pipeline for genome-wide DNA methylationThe complexity of epigenetic diseasesDNA methylation biomarkers: cancer and beyondAssociating cellular epigenetic models with human phenotypesEpigenome-wide association of liver methylation patterns and complex metabolic traits in miceAnalysis pipelines and packages for Infinium HumanMethylation450 BeadChip (450k) dataComprehensive analysis of DNA methylation data with RnBeads.A coherent approach for analysis of the Illumina HumanMethylation450 BeadChip improves data quality and performance in epigenome-wide association studies.Cell-composition effects in the analysis of DNA methylation array data: a mathematical perspective.Removing unwanted variation in a differential methylation analysis of Illumina HumanMethylation450 array data.Adjusting for Cell Type Composition in DNA Methylation Data Using a Regression-Based Approach.A Flexible, Efficient Binomial Mixed Model for Identifying Differential DNA Methylation in Bisulfite Sequencing Data.The Role of DNA Methylation in Cardiovascular Risk and Disease: Methodological Aspects, Study Design, and Data Analysis for Epidemiological Studies.Imputation of missing covariate values in epigenome-wide analysis of DNA methylation data.Estimation of Cell-Type Composition Including T and B Cell Subtypes for Whole Blood Methylation Microarray Data.Cell type specific DNA methylation in cord blood: A 450K-reference data set and cell count-based validation of estimated cell type compositionStatistical challenges in analyzing methylation and long-range chromosomal interaction dataEstimating and accounting for tumor purity in the analysis of DNA methylation data from cancer studiesComparison of different cell type correction methods for genome-scale epigenetics studies.Fast and robust adjustment of cell mixtures in epigenome-wide association studies with SmartSVADifferential expression analysis for RNAseq using Poisson mixed models.Grasping nettles: cellular heterogeneity and other confounders in epigenome-wide association studiesEpigenetics of discordant monozygotic twins: implications for diseaseInfluence of environmental exposure on human epigenetic regulationPredicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements.Epigenetic mechanisms underlying the pathogenesis of neurogenetic diseasesAge and sun exposure-related widespread genomic blocks of hypomethylation in nonmalignant skinIdentifying molecular features associated with psychoneurological symptoms in women with breast cancer using multivariate mixed models.Concordant and discordant DNA methylation signatures of aging in human blood and brain.A varying T cell subtype explains apparent tobacco smoking induced single CpG hypomethylation in whole blood.Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL).Reference-free deconvolution of DNA methylation data and mediation by cell composition effects.Adiposity is associated with DNA methylation profile in adipose tissuePower and sample size estimation for epigenome-wide association scans to detect differential DNA methylation.eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data.GLINT: a user-friendly toolset for the analysis of high-throughput DNA-methylation array data.A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies.Genome-Wide Epigenetic Studies in Human Disease: A Primer on -Omic TechnologiesIntegrative DNA methylome analysis of pan-cancer biomarkers in cancer discordant monozygotic twin-pairsEpigenetic regulation of ageing: linking environmental inputs to genomic stability.
P2860
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P2860
Epigenome-wide association studies without the need for cell-type composition.
description
2014 nî lūn-bûn
@nan
2014年の論文
@ja
2014年学术文章
@wuu
2014年学术文章
@zh-cn
2014年学术文章
@zh-hans
2014年学术文章
@zh-my
2014年学术文章
@zh-sg
2014年學術文章
@yue
2014年學術文章
@zh
2014年學術文章
@zh-hant
name
Epigenome-wide association studies without the need for cell-type composition.
@en
Epigenome-wide association studies without the need for cell-type composition.
@nl
type
label
Epigenome-wide association studies without the need for cell-type composition.
@en
Epigenome-wide association studies without the need for cell-type composition.
@nl
prefLabel
Epigenome-wide association studies without the need for cell-type composition.
@en
Epigenome-wide association studies without the need for cell-type composition.
@nl
P2093
P2860
P356
P1433
P1476
Epigenome-wide association studies without the need for cell-type composition.
@en
P2093
David Heckerman
Jennifer Listgarten
P2860
P2888
P304
P356
10.1038/NMETH.2815
P577
2014-01-26T00:00:00Z