Literature DB >> 33707472

EMeth: An EM algorithm for cell type decomposition based on DNA methylation data.

Hanyu Zhang1, Ruoyi Cai2, James Dai3, Wei Sun4,5,6.   

Abstract

We introduce a new computational method named EMeth to estimate cell type proportions using DNA methylation data. EMeth is a reference-based method that requires cell type-specific DNA methylation data from relevant cell types. EMeth improves on the existing reference-based methods by detecting the CpGs whose DNA methylation are inconsistent with the deconvolution model and reducing their contributions to cell type decomposition. Another novel feature of EMeth is that it allows a cell type with known proportions but unknown reference and estimates its methylation. This is motivated by the case of studying methylation in tumor cells while bulk tumor samples include tumor cells as well as other cell types such as infiltrating immune cells, and tumor cell proportion can be estimated by copy number data. We demonstrate that EMeth delivers more accurate estimates of cell type proportions than several other methods using simulated data and in silico mixtures. Applications in cancer studies show that the proportions of T regulatory cells estimated by DNA methylation have expected associations with mutation load and survival time, while the estimates from gene expression miss such associations.

Entities:  

Mesh:

Year:  2021        PMID: 33707472      PMCID: PMC7952399          DOI: 10.1038/s41598-021-84864-9

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  31 in total

Review 1.  Genome-wide DNA methylation analysis of archival formalin-fixed paraffin-embedded tissue using the Illumina Infinium HumanMethylation27 BeadChip.

Authors:  Christina Thirlwell; Marianne Eymard; Andrew Feber; Andrew Teschendorff; Kerra Pearce; Matthias Lechner; Martin Widschwendter; Stephan Beck
Journal:  Methods       Date:  2010-04-29       Impact factor: 3.608

Review 2.  Elements of cancer immunity and the cancer-immune set point.

Authors:  Daniel S Chen; Ira Mellman
Journal:  Nature       Date:  2017-01-18       Impact factor: 49.962

Review 3.  The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy.

Authors:  Jonathan J Havel; Diego Chowell; Timothy A Chan
Journal:  Nat Rev Cancer       Date:  2019-03       Impact factor: 60.716

Review 4.  Cancer immunotherapy using checkpoint blockade.

Authors:  Antoni Ribas; Jedd D Wolchok
Journal:  Science       Date:  2018-03-22       Impact factor: 47.728

5.  ICeD-T Provides Accurate Estimates of Immune Cell Abundance in Tumor Samples by Allowing for Aberrant Gene Expression Patterns.

Authors:  Douglas R Wilson; Chong Jin; Joseph G Ibrahim; Wei Sun
Journal:  J Am Stat Assoc       Date:  2019-09-16       Impact factor: 5.033

6.  Genome-wide DNA methylation analysis identifies hypomethylated genes regulated by FOXP3 in human regulatory T cells.

Authors:  Yuxia Zhang; Jovana Maksimovic; Gaetano Naselli; Junyan Qian; Michael Chopin; Marnie E Blewitt; Alicia Oshlack; Leonard C Harrison
Journal:  Blood       Date:  2013-08-23       Impact factor: 22.113

7.  Reference-free cell mixture adjustments in analysis of DNA methylation data.

Authors:  Eugene Andres Houseman; John Molitor; Carmen J Marsit
Journal:  Bioinformatics       Date:  2014-01-21       Impact factor: 6.937

8.  Age-related variations in the methylome associated with gene expression in human monocytes and T cells.

Authors:  Lindsay M Reynolds; Jackson R Taylor; Jingzhong Ding; Kurt Lohman; Craig Johnson; David Siscovick; Gregory Burke; Wendy Post; Steven Shea; David R Jacobs; Hendrik Stunnenberg; Stephen B Kritchevsky; Ina Hoeschele; Charles E McCall; David Herrington; Russell P Tracy; Yongmei Liu
Journal:  Nat Commun       Date:  2014-11-18       Impact factor: 14.919

9.  DNA methylation arrays as surrogate measures of cell mixture distribution.

Authors:  Eugene Andres Houseman; William P Accomando; Devin C Koestler; Brock C Christensen; Carmen J Marsit; Heather H Nelson; John K Wiencke; Karl T Kelsey
Journal:  BMC Bioinformatics       Date:  2012-05-08       Impact factor: 3.169

Review 10.  Regulatory T Cells in the Tumor Microenvironment and Cancer Progression: Role and Therapeutic Targeting.

Authors:  Belal Chaudhary; Eyad Elkord
Journal:  Vaccines (Basel)       Date:  2016-08-06
View more
  3 in total

1.  Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes.

Authors:  Yunhee Jeong; Lisa Barros de Andrade E Sousa; Dominik Thalmeier; Reka Toth; Marlene Ganslmeier; Kersten Breuer; Christoph Plass; Pavlo Lutsik
Journal:  Brief Bioinform       Date:  2022-07-18       Impact factor: 13.994

2.  DAISM-DNNXMBD: Highly accurate cell type proportion estimation with in silico data augmentation and deep neural networks.

Authors:  Yating Lin; Haojun Li; Xu Xiao; Lei Zhang; Kejia Wang; Jingbo Zhao; Minshu Wang; Frank Zheng; Minwei Zhang; Wenxian Yang; Jiahuai Han; Rongshan Yu
Journal:  Patterns (N Y)       Date:  2022-02-03

3.  Deconvolution of tumor composition using partially available DNA methylation data.

Authors:  Dingqin He; Ming Chen; Wenjuan Wang; Chunhui Song; Yufang Qin
Journal:  BMC Bioinformatics       Date:  2022-08-24       Impact factor: 3.307

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.