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| Status |
Public on May 08, 2018 |
| Title |
FlowSorted.Blood.EPIC: An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray (II) |
| Organism |
Homo sapiens |
| Experiment type |
Methylation profiling by genome tiling array
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| Summary |
DNA methylation assessments of peripheral blood DNA can be used to accurately estimate the relative proportions of underlying leukocyte subtypes. Such cell deconvolution analysis relies on libraries of discriminating differentially methylated regions that are developed for each specific cell type measured. The relationship between estimated cell type proportions can then be tested for their association with phenotypes, disease states, and subject outcomes, or used in multivariable models as terms for adjustment in epigenome-wide association studies (EWAS). We obtained purified neutrophils, monocytes, B-lymphocytes, natural killer (NK) cells, CD4+ T-cells, and CD8+ T-cells from healthy subjects and measured DNA methylation with the Illumina HumanMethylationEPIC array platform. In addition, we measured DNA methylation with the EPIC array in two sets of artificial DNA mixtures comprising the above cell types. We compared three separate approaches to select reference differentially methylated region libraries (DMR library), for cell type proportion inference. The IDOL algorithm identified an optimal DMR library consisting of 450 CpG sites for inferring leukocyte subtype proportions (average R2=99.2). Importantly, the majority of CpG sites (69%) in the IDOL DMR library were unique to the new EPIC methylation array, in that they were not present on the 450K array. Our new reference DMR library is available as a Bioconductor package, has the potential to reduce any unintended technical differences arising from the combination of different generations of array platforms, and may be helpful in generating larger DMR libraries that include novel cell subtypes.
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| Overall design |
Bisulphite converted DNA from neutrophils (Neu, n=6), monocytes (Mono, n=6), B-lymphocytes (Bcells, n=6), CD4+ T-cells (CD4T, n=7, six samples and one technical replicate), CD8+ T-cells (CD8T, n=6), Natural Killer cells (NK, n=6), and 12 DNA artificial mixtures (labeled as MIX in the dataset) were hybridised to the Illumina Infinium HumanMethylationEPIC Beadchip v1.0_B4
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| Contributor(s) |
Salas LA, Koestler DC, Butler RA, Hansen HM, Wiencke JK, Kelsey KT, Christensen BC |
| Citation(s) |
29843789, 35140201, 36127421 |
| NIH grant(s) |
| Grant ID |
Grant title |
Affiliation |
Name |
| R01 CA052689 |
Genetic and molecular epidemiology of adult glioma |
University of California San Francisco |
John K. Wiencke |
| P50 CA097257 |
BRAIN TUMOR SPORT GRANT |
University of California San Francisco |
John K. Wiencke |
| R01 CA207110 |
Prospective immune profiling using methylation markers and pancreatic cancer risk |
TUFTS UNIVERSITY BOSTON |
Karl Timothy Kelsey |
| R01 DE022772 |
MicroRNA related genetic variation and head and neck cancer |
DARTMOUTH COLLEGE |
Brock Clarke Christensen |
| P20 GM104416 |
Early Risk Factor Related Epigenetic Alterations in Breast Cancer Pathogenesis |
DARTMOUTH COLLEGE |
Brock Clarke Christensen |
| R01 CA216265 |
(PQ3) Immune epigenetic biomarkers of bladder cancer outcomes |
DARTMOUTH COLLEGE |
Brock Clarke Christensen |
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| Submission date |
Feb 13, 2018 |
| Last update date |
Oct 04, 2022 |
| Contact name |
Lucas A. Salas |
| E-mail(s) |
lucas.a.salas@dartmouth.edu
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| Organization name |
Geisel School of Medicine at Dartmouth
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| Department |
Epidemiology
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| Lab |
Salas Lab
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| Street address |
1 Medical Center Dr, DHMC
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| City |
Lebanon |
| State/province |
NH |
| ZIP/Postal code |
03756 |
| Country |
USA |
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| Platforms (1) |
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| Samples (49)
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| This SubSeries is part of SuperSeries: |
| GSE110555 |
SuperSeries: An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray |
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| Relations |
| BioProject |
PRJNA433986 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSE110554_RAW.tar |
851.3 Mb |
(http)(custom) |
TAR (of IDAT) |
| GSE110554_signals.txt.gz |
204.2 Mb |
(ftp)(http) |
TXT |
| Raw data are available on Series record |
| Processed data included within Sample table |
| Raw data provided as supplementary file |
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