645
Views
0
CrossRef citations to date
0
Altmetric
Methodology

Calculating Detection Limits and Uncertainty of Reference-Based Deconvolution of Whole-Blood DNA Methylation Data

, , , , , , & ORCID Icon show all
Pages 435-451 | Received 07 Jan 2023, Accepted 16 May 2023, Published online: 20 Jun 2023

References

  • Rakyan VK , DownTA, BaldingDJ, BeckS. Epigenome-wide association studies for common human diseases. Nat. Rev. Genet.12(8), 529–541 (2011).
  • Michels KB , BinderAM, DedeurwaerderSet al. Recommendations for the design and analysis of epigenome-wide association studies. Nat. Methods10(10), 949–955 (2013).
  • Flanagan JM . Epigenome-wide association studies (EWAS): past, present, and future. Methods Mol. Biol.1238, 51–63 (2015).
  • Adalsteinsson BT , GudnasonH, AspelundTet al. Heterogeneity in white blood cells has potential to confound DNA methylation measurements. PLOS ONE7(10), 1–9 (2012).
  • Reinius LE , AcevedoN, JoerinkMet al. Differential DNA methylation in purified human blood cells: implications for cell lineage and studies on disease susceptibility. PLOS ONE7(7), e41361 (2012).
  • Jaffe AE , IrizarryRA. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol.15(2), R31 (2014).
  • Liang L , CooksonWO. Grasping nettles: cellular heterogeneity and other confounders in epigenome-wide association studies. Hum. Mol. Genet.23(R1), R83–R88 (2014).
  • Houseman EA , KimS, KelseyKT, WienckeJK. DNA methylation in whole blood: uses and challenges. Curr. Environ. Health Rep.2(2), 145–154 (2015).
  • Houseman EA , AccomandoWP, KoestlerDCet al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics13, 86 (2012).
  • Newman AM , LiuCL, GreenMRet al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods12(5), 453–457 (2015).
  • Houseman EA , KileML, ChristianiDC, InceTA, KelseyKT, MarsitCJ. Reference-free deconvolution of DNA methylation data and mediation by cell composition effects. BMC Bioinformatics17, 259 (2016).
  • Koestler DC , JonesMJ, UssetJet al. Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL). BMC Bioinformatics17, 120 (2016).
  • Teschendorff AE , BreezeCE, ZhengSC, BeckS. A comparison of reference-based algorithms for correcting cell-type heterogeneity in epigenome-wide association studies. BMC Bioinformatics18(1), 105 (2017).
  • Titus AJ , GallimoreRM, SalasLA, ChristensenBC. Cell-type deconvolution from DNA methylation: a review of recent applications. Hum. Mol. Genet.26(R2), R216–R224 (2017).
  • Decamps C , PriveF, BacherRet al. Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software. BMC Bioinformatics21(1), 16 (2020).
  • Scherer M , NazarovPV, TothRet al. Reference-free deconvolution, visualization and interpretation of complex DNA methylation data using DecompPipeline, MeDeCom and FactorViz. Nat. Protoc.15(10), 3240–3263 (2020).
  • Koestler DC , UssetJ, ChristensenBCet al. DNA methylation-derived neutrophil-to-lymphocyte ratio: an epigenetic tool to explore cancer inflammation and outcomes. Cancer Epidemiol. Biomarkers Prev.26(3), 328–338 (2017).
  • Wiencke JK , KoestlerDC, SalasLAet al. Immunomethylomic approach to explore the blood neutrophil lymphocyte ratio (NLR) in glioma survival. Clin. Epigenetics9, 10 (2017).
  • Grieshober L , GrawS, BarnettMJet al. Pre-diagnosis neutrophil-to-lymphocyte ratio and mortality in individuals who develop lung cancer. Cancer Causes Control32(11), 1227–1236 (2021).
  • Grieshober L , GrawS, BarnettMJet al. Methylation-derived neutrophil-to-lymphocyte ratio and lung cancer risk in heavy smokers. Cancer Prev. Res.11(11), 727–734 (2018).
  • Bell-Glenn S , ThompsonJA, SalasLA, KoestlerDC. A Novel Framework for the Identification of Reference DNA Methylation Libraries for Reference-Based Deconvolution of Cellular Mixtures. Front. Bioinform.2, doi:10.3389/fbinf.2022.835591 (2022).
  • Nissen E , ReinerA, LiuSet al. Assessment of immune cell profiles among post-menopausal women in the Women’s Health Initiative using DNA methylation-based methods. Clin. Epigenetics15(1), 1–16 (2023).
  • Mandal S , Van TreurenW, WhiteRA, EggesboM, KnightR, PeddadaSD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb. Ecol. Health Dis.26, doi:10.3402/mehd.v26.27663 (2015).
  • Chen JQ , SalasLA, WienckeJKet al. Immune profiles and DNA methylation alterations related with non-muscle-invasive bladder cancer outcomes. Clin. Epigenetics14(1), 14 (2022).
  • Pum J . A practical guide to validation and verification of analytical methods in the clinical laboratory. Adv. Clin. Chem.90, 215–281 (2019).
  • Croghan CW , EgeghyPP. Methods of dealing with values below the limit of detection using SAS. In: Presented at Southeastern SAS User Group.Environmental Protection Agency, FL, USA, 1–5 (2003).
  • Palarea-Albaladejo J , Martin-FernandezJA. Values below detection limit in compositional chemical data. Anal. Chim. Acta764, 32–43 (2013).
  • Nab L , van SmedenM, KeoghRH, GroenwoldRHH. Mecor: an R package for measurement error correction in linear regression models with a continuous outcome. Comput. Methods Programs Biomed.208, doi:10.1016/j.cmpb.2021.106238 (2021).
  • Wang XF , WangB. Deconvolution estimation in measurement error models: the R package decon. J. Stat. Softw.39(10), i10 (2011).
  • Armbruster DA , PryT. Limit of blank, limit of detection and limit of quantitation. Clin. Biochem. Rev.29(Suppl. 1), S49–S52 (2008).
  • Salas LA , ZhangZ, KoestlerDCet al. Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling. Nat. Commun.13(1), 761 (2022).
  • Browne RW , WhitcombBW. Procedures for determination of detection limits: application to high-performance liquid chromatography analysis of fat-soluble vitamins in human serum. Epidemiology21(Suppl. 4), S4–S9 (2010).
  • Linnet K , KondratovichM. Partly nonparametric approach for determining the limit of detection. Clin. Chem.50(4), 732–740 (2004).
  • Saadati M , BennerA. Statistical challenges of high-dimensional methylation data. Stat. Med.33(30), 5347–5357 (2014).
  • Perrier F , NovoloacaA, AmbatipudiSet al. Identifying and correcting epigenetics measurements for systematic sources of variation. Clin. Epigenetics10, 38 (2018).
  • Simas AB , Barreto-SouzaW, RochaAV. Improved estimators for a general class of beta regression models. Comput. Stat. Data Anal.54(2), 348–3 (2010).
  • Cribari-Neto F , ZeileisA. Beta regression in R. J. Stat. Softw.34(2), 1–24 (2010).
  • Meier R , NissenE, KoestlerDC. Low variability in the underlying cellular landscape adversely affects the performance of interaction-based approaches for conducting cell-specific analyses of DNA methylation in bulk samples. Stat. Appl. Genet. Mol. Biol.20(3), 73–84 (2021).
  • Efron B , TibshiraniRJ. An Introduction to the Bootstrap.Chapman & Hall/CRC, NY, USA (1993).
  • Nissen E , ReinerA, LiuSet al. Assessment of immune cell profiles among post-menopausal women in the Women’s Health Initiative using DNA methylation-based methods. Clin. Epigenetics15(1), 69 (2023).
  • Juhlin L . Basophil and eosinophil leukocyted in various internal disorders. Acta Med. Scand.174, 249–254 (1963).
  • Vaduganathan M , AmbrosyAP, GreeneSJet al. Predictive value of low relative lymphocyte count in patients hospitalized for heart failure with reduced ejection fraction: insights from the EVEREST trial. Circ. Heart Fail.5(6), 750–758 (2012).
  • Kumar BV , ConnorsTJ, FarberDL. Human T cell development, localization, and function throughout life. Immunity48(2), 202–213 (2018).
  • Zhang Z , ButlerR, KoestlerDCet al. Comparative analysis of the DNA methylation landscape in CD4, CD8, and B memory lineages. Clin. Epigenetics14(1), 173 (2022).
  • Vellame DS , ShirebyG, MaccalmanAet al. Uncertainty quantification of reference-based cellular deconvolution algorithms. Epigenetics18(1), 1–15doi:10.1080/15592294.2022.2137659 (2023).