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deconvolution of complex DNA methylation data a systematic protocol - - PowerPoint PPT Presentation

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Reference-free deconvolution of complex DNA methylation data a systematic protocol Saarland University Michael Scherer Department of Genetics/Epigenetics HADACA, Aussois 11/26/2019 Overview Introduction into DNA methylation DNA

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Saarland University Department of Genetics/Epigenetics

Reference-free deconvolution of complex DNA methylation data – a systematic protocol

Michael Scherer HADACA, Aussois 11/26/2019

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Michael Scherer 11/22/2019 2

Overview

  • Introduction into DNA

methylation

  • DNA methylation-based

deconvolution

  • Systematic protocol for DNA-

methylation based deconvolution using MeDeCom

  • Application of the proposed

protocol on TCGA data

  • Conclusions
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Michael Scherer 11/22/2019 3

DNA methylation

  • Reversible epigenetic modification
  • Almost exclusively in CpG context
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Michael Scherer 11/22/2019 4

DNA methylation

  • Reversible epigenetic modification
  • Almost exclusively in CpG context
  • Transcriptional repression in

promoter regions

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Michael Scherer 11/22/2019 5

DNA methylation

  • Reversible epigenetic modification
  • Almost exclusively in CpG context
  • Transcriptional repression in

promoter regions

  • Highly cell type specific

Figure: tSNE plot of WGBS data from different cell types assayed in the DEEP1 and BLUEPRINT2 consortia

1 http://www.deutsches-epigenom-programm.de/ 2 http://www.blueprint-epigenome.eu/

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Michael Scherer 11/22/2019 6

DNA methylation based deconvolution

Reference-based deconvolution Reference-free deconvolution

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Michael Scherer 11/22/2019 7

DNA methylation based deconvolution

Reference-based deconvolution Reference-free deconvolution

  • Houseman approach1
  • MethylCIBERSORT2
  • EpiDISH3

1 Houseman, E. A. et al. DNA methylation arrays as surrogate measures of

cell mixture distribution. BMC Bioinformatics 13, (2012).

2 Chakravarthy, A. et al. Pan-cancer deconvolution of tumour composition

using DNA methylation. Nat. Commun. 9, (2018).

3 Teschendorff, A. E et al. A comparison of reference-based algorithms

for correcting cell-type heterogeneity in Epigenome-Wide Association

  • Studies. BMC Bioinformatics 18, 105 (2017).
  • RefFreeCellMix4
  • EDec5
  • MeDeCom6

1 Houseman, E. A. et al. Reference-free cell mixture adjustments in

analysis of DNA methylation data. Bioinformatics 30, 1431–1439 (2014).

2 Onuchic, V. et al. Epigenomic Deconvolution of Breast Tumors Reveals

Metabolic Coupling between Constituent Cell Types. Cell Rep. 17, 2075– 2086 (2016).

3 Lutsik, P

. et al. MeDeCom: discovery and quantification of latent components of heterogeneous methylomes. Genome Biol. 18, 55 (2017).

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Michael Scherer 11/22/2019 8

Non-negative matrix factorization

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Michael Scherer 11/22/2019 9

Key messages from HADACA 2018

  • Only small performance differences between the three

available reference-free deconvolution tools (RefFreeCellMix, EDec, MeDeCom) on in-silico mixed data

  • Thorough data processing more important than choice of the

deconvolution tool

  • Accounting for confounding factors critical for obtaining

biologically plausible results1

1 Decamps, C. et al. Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation

deconvolution software. Preprint at https://www.biorxiv.org/content/10.1101/698050v1.abstract (2019).

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Michael Scherer 11/22/2019 10

Systematic protocol for DNA methylation based deconvolution

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Michael Scherer 11/22/2019 11

DecompPipeline1

1 https://github.com/lutsik/DecompPipeline 2 Müller, F

. et al. RnBeads 2.0: comprehensive analysis of DNA methylation data. Genome Biol. 20, 55 (2019).

3 Nazarov, P

. V et al. Deconvolution of transcriptomes and miRNomes by independent component analysis provides insights into biological processes and clinical outcomes of melanoma patients. BMC Med. Genomics 12, 132 (2019).

  • Data import using the widely-used

RnBeads2 software package

  • Three-step procedure
  • Quality-aware filtering
  • Accounting for confounding factors

using independent component analysis (ICA3)

  • Selecting potentially informative CpGs
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Confounding factor adjustment using ICA

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Confounding factor adjustment using ICA

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Protocol overview

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MeDeCom1

  • Regularized non-negative matrix

factorization

  • Critical parameter choices:
  • Number of latent methylation

components (LMCs, K)

  • Regularization parameter (λ)
  • Optimized using an alternate
  • ptimization scheme
  • Cross validation error computed

1 Lutsik, P

. et al. MeDeCom: discovery and quantification of latent components of heterogeneous methylomes. Genome Biol. 18, 55 (2017).

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RefFreeCellMix and EDec

  • Similar approaches as MeDeCom
  • Seamless integration into the protocol
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Protocol overview

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FactorViz1 overview

1 https://github.com/lutsik/FactorViz

  • R/Shiny application to visualize

deconvolution results

  • Evaluation and interpretation

functions

  • Proportions and LMC matrix

biologically interpreted

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FactorViz: Interface

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FactorViz: Functions

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Application to TCGA LUAD dataset

  • 461 samples from the lung adenocarcinoma dataset from TCGA1
  • Assayed using the Illumina Infinium 450k BeadChip

1 https://cancergenome.nih.gov/

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QC on TCGA data

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Parameter selection

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Proportions heatmap

1

1 Aran, D., Sirota, M. & Butte, A. J. Systematic pan-

cancer analysis of tumour purity. Nat. Commun. 6, 1–11 (2015).

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Phenotypic trait associations

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LMC LOLA1 enrichment analysis

1Sheffield, N. & Bock, C. LOLA:Enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor. Bioinformatics 32, 587–589 (2016).

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Sample-specific marker gene expression

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Conclusions

  • Thorough data processing and biologically guided

interpretation more critical than the deconvolution tool itself

  • Three-stage protocol
  • Quality-adapted CpG filtering and confounding factor adjustment with

ICA using DecompPipeline

  • Methylome deconvolution using MeDeCom, RefFreeCellMix or EDec
  • Validation and interpretation of deconvolution results with FactorViz
  • Deconvolution of TCGA LUAD dataset shows indications of

immune cell infiltration, stromal, and epithelial components

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Michael Scherer 11/22/2019 29

Acknowledgements

Pavlo Lutsik Reka Toth Valentin Maurer Christoph Plass Jörn Walter Shashwat Sahay Petr V. Nazarov Tony Kaoma Thomas Lengauer