An evaluation of methods correcting for cell-type heterogeneity in DNA methylation studies

[1]  M. Kobor,et al.  Adjusting for Cell Type Composition in DNA Methylation Data Using a Regression-Based Approach. , 2015, Methods in molecular biology.

[2]  Z. Herceg,et al.  Independent genomewide screens identify the tumor suppressor VTRNA2-1 as a human epiallele responsive to periconceptional environment , 2015, Genome Biology.

[3]  C. Marsit,et al.  Cell-composition effects in the analysis of DNA methylation array data: a mathematical perspective , 2015, BMC Bioinformatics.

[4]  Subhajyoti De,et al.  An assessment of computational methods for estimating purity and clonality using genomic data derived from heterogeneous tumor tissue samples , 2015, Briefings Bioinform..

[5]  Nilesh J Samani,et al.  Cigarette smoking reduces DNA methylation levels at multiple genomic loci but the effect is partially reversible upon cessation , 2014, Epigenetics.

[6]  L. Liang,et al.  Grasping nettles: cellular heterogeneity and other confounders in epigenome-wide association studies , 2014, Human molecular genetics.

[7]  Martin J. Aryee,et al.  Epigenome-wide association studies without the need for cell-type composition , 2014, Nature Methods.

[8]  K. Hansen,et al.  Functional normalization of 450k methylation array data improves replication in large cancer studies , 2014, Genome Biology.

[9]  E. Andres Houseman,et al.  Reference-free cell mixture adjustments in analysis of DNA methylation data , 2014, Bioinform..

[10]  R. Irizarry,et al.  Accounting for cellular heterogeneity is critical in epigenome-wide association studies , 2014, Genome Biology.

[11]  Alexandra M. Binder,et al.  Recommendations for the design and analysis of epigenome-wide association studies , 2013, Nature Methods.

[12]  T. Pastinen,et al.  Integration of high-resolution methylome and transcriptome analyses to dissect epigenomic changes in childhood acute lymphoblastic leukemia. , 2013, Cancer research.

[13]  Martin J. Aryee,et al.  Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in Rheumatoid Arthritis , 2013, Nature Biotechnology.

[14]  R. Harris,et al.  Human metastable epiallele candidates link to common disorders , 2013, Epigenetics.

[15]  J. Kere,et al.  Differential DNA Methylation in Purified Human Blood Cells: Implications for Cell Lineage and Studies on Disease Susceptibility , 2012, PloS one.

[16]  Johann A. Gagnon-Bartsch,et al.  Using control genes to correct for unwanted variation in microarray data. , 2012, Biostatistics.

[17]  Eleazar Eskin,et al.  Improved linear mixed models for genome-wide association studies , 2012, Nature Methods.

[18]  Devin C. Koestler,et al.  DNA methylation arrays as surrogate measures of cell mixture distribution , 2012, BMC Bioinformatics.

[19]  Ying Liu,et al.  FaST linear mixed models for genome-wide association studies , 2011, Nature Methods.

[20]  D. Balding,et al.  Epigenome-wide association studies for common human diseases , 2011, Nature Reviews Genetics.

[21]  Andrew E. Teschendorff,et al.  Independent surrogate variable analysis to deconvolve confounding factors in large-scale microarray profiling studies , 2011, Bioinform..

[22]  P. Szodoray,et al.  Altered T-cell and regulatory cell repertoire in patients with diffuse cutaneous systemic sclerosis , 2011, Scandinavian journal of rheumatology.

[23]  J. Rinn,et al.  DNA methylation and epigenetic control of cellular differentiation , 2010, Cell cycle.

[24]  T. Down,et al.  Genome-wide conserved consensus transcription factor binding motifs are hyper-methylated , 2010, BMC Genomics.

[25]  P. Laird Principles and challenges of genome-wide DNA methylation analysis , 2010, Nature Reviews Genetics.

[26]  T. Gambichler,et al.  Absolute count of T and B lymphocyte subsets is decreased in systemic sclerosis , 2010, European journal of medical research.

[27]  Zachary D. Smith,et al.  Genome-scale DNA methylation mapping of clinical samples at single-nucleotide resolution , 2010, Nature Methods.

[28]  Joachim Selbig,et al.  Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach , 2010, BMC Bioinformatics.

[29]  C. Vaillancourt,et al.  Isolation and culture of term human cytotrophoblast cells and in vitro methods for studying human cytotrophoblast cells' calcium uptake. , 2009, Methods in molecular biology.

[30]  T. Mikkelsen,et al.  Genome-scale DNA methylation maps of pluripotent and differentiated cells , 2008, Nature.

[31]  Steven Gallinger,et al.  Genome-wide association scan identifies a colorectal cancer susceptibility locus on chromosome 8q24 , 2007, Nature Genetics.

[32]  John D. Storey,et al.  Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis , 2007, PLoS genetics.

[33]  G. Manda,et al.  Imbalance of peripheral B lymphocytes and NK cells in rheumatoid arthritis , 2003, Journal of cellular and molecular medicine.

[34]  M. Pierer,et al.  B lymphocytopenia in rheumatoid arthritis is associated with the DRB1 shared epitope and increased acute phase response , 2002, Arthritis research.

[35]  V. Plerou,et al.  Random matrix approach to cross correlations in financial data. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[36]  A. Bird DNA methylation patterns and epigenetic memory. , 2002, Genes & development.

[37]  K. Roeder,et al.  Genomic Control for Association Studies , 1999, Biometrics.

[38]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[39]  N. Farid The immunogenetics of autoimmune diseases , 1991 .

[40]  C. Werning [Rheumatoid arthritis]. , 1983, Medizinische Monatsschrift fur Pharmazeuten.

[41]  L. Nebel,et al.  Isolation of Syncytiotrophoblast From Human Term Placentas , 1974, Obstetrics and gynecology.