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ACGCGAAACGTTCTATCG Peter Hickey (@PeteHaitch) Simulating DNA - PowerPoint PPT Presentation

Jan 21, 2024 •367 likes •740 views

DNA methylation ACGCGAAACGTTCTATCG Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 1 / 14 DNA methylation ACGCGAAACGTTCTATCG Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 1 / 14 DNA

  1. DNA methylation ACGCGAAACGTTCTATCG Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 1 / 14

  2. DNA methylation ACGCGAAACGTTCTATCG Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 1 / 14

  3. DNA methylation CH 3 CH 3 CH 3 ACGCGAAACGTTCTATCG Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 1 / 14

  4. DNA methylation CH 3 CH 3 CH 3 ACGCGAAACGTTCTATCG Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 1 / 14

  5. Measuring DNA methylation Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 2 / 14

  6. Measuring DNA methylation Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 2 / 14

  7. Measuring DNA methylation Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 2 / 14

  8. Measuring DNA methylation Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 2 / 14

  9. Measuring DNA methylation β i = 3/3 β i+2 = 2/4 β i+3 = 0/4 β i+1 = 4/4 Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 2 / 14

  10. Differentially methylated regions (DMRs) 1 0.8 Methylation 0.5 Normals β-values Cancers 0.2 1 kb Position (bp) 1 Hansen, K. D. et al. Nat Genet 43, 768–775 (2011) Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 3 / 14

  11. Differentially methylated regions (DMRs) 1 0.8 Methylation 0.5 Normals β-values Cancers 0.2 1 kb CpG islands (CGIs) Position (bp) 1 Hansen, K. D. et al. Nat Genet 43, 768–775 (2011) Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 3 / 14

  12. Why I care about simulating DNA methylation data Methods development and validation Do methods designed to find DMRs actually work? What method reigns supreme? Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 4 / 14

  13. Why I care about simulating DNA methylation data Methods development and validation Do methods designed to find DMRs actually work? What method reigns supreme? How to decide? No “gold standard” data ⇒ simulate Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 4 / 14

  14. Why I care about simulating DNA methylation data Methods development and validation Do methods designed to find DMRs actually work? What method reigns supreme? How to decide? No “gold standard” data ⇒ simulate No simulation software ⇒ I’m writing methsim . Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 4 / 14

  15. Simulation approaches Simulate β -values d Simulate independent β i = Beta ( µ i , ν i ) + induce correlation via variogram model. Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 5 / 14

  16. Simulation approaches Simulate β -values d Simulate independent β i = Beta ( µ i , ν i ) + induce correlation via variogram model. Re-sample real data in a way that tries to preserve correlation structure. Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 5 / 14

  17. Simulation approaches Simulate β -values d Simulate independent β i = Beta ( µ i , ν i ) + induce correlation via variogram model. Re-sample real data in a way that tries to preserve correlation structure. β -values are summarised measurements. Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 5 / 14

  18. Simulation approaches Simulate β -values d Simulate independent β i = Beta ( µ i , ν i ) + induce correlation via variogram model. Re-sample real data in a way that tries to preserve correlation structure. β -values are summarised measurements. Correlations of β -values are spurious. Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 5 / 14

  19. Simulation approaches Simulate β -values d Simulate independent β i = Beta ( µ i , ν i ) + induce correlation via variogram model. Re-sample real data in a way that tries to preserve correlation structure. β -values are summarised measurements. Correlations of β -values are spurious. Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 5 / 14

  20. Simulation approaches Simulate β -values d Simulate independent β i = Beta ( µ i , ν i ) + induce correlation via variogram model. Re-sample real data in a way that tries to preserve correlation structure. β -values are summarised measurements. Correlations of β -values are spurious. Simulate individual methylation events Higher resolution. Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 5 / 14

  21. Simulation approaches Simulate β -values d Simulate independent β i = Beta ( µ i , ν i ) + induce correlation via variogram model. Re-sample real data in a way that tries to preserve correlation structure. β -values are summarised measurements. Correlations of β -values are spurious. Simulate individual methylation events Higher resolution. Contains the mechanistic dependence structure. Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 5 / 14

  22. Simulation approaches Simulate β -values d Simulate independent β i = Beta ( µ i , ν i ) + induce correlation via variogram model. Re-sample real data in a way that tries to preserve correlation structure. β -values are summarised measurements. Correlations of β -values are spurious. Simulate individual methylation events Higher resolution. Contains the mechanistic dependence structure. Difficult given current data. Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 5 / 14

  23. My solution methsim : An R package for simulating whole genome DNA methylation data. Parameter distributions estimated from input data. Parts written in C ++ (via Rcpp ). Results today from a preliminary version of methsim . Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 6 / 14

  24. My solution methsim : An R package for simulating whole genome DNA methylation data. Parameter distributions estimated from input data. Parts written in C ++ (via Rcpp ). Results today from a preliminary version of methsim . Outline of methsim 1 Segment genome into “region of similarity” ( MethylSeekR 1 ) 2 Simulate “meta-haplotypes” within each region using Markov model. 3 Simulate sequencing of reads. a Burger, L., Gaidatzis, D., Schübeler, D. & Stadler, M. B. Nucleic Acids Res (2013). doi:10.1093/nar/gkt599 Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 6 / 14

  25. Simulating meta-haplotypes (2) For each region: Simulate each meta-haplotype using a Markov model Transition matrices depend on distance between CGs and the type of region Assign haplotype i in region r frequency q i,r q 1,r q 1,r+1 q i,r q i,r+1 q H,r q H,r+1 Region r Region r+1 (3) Simulate read positions Simulate reads for region r by sampling from i th haplotype with probability q i,r Simulate sequencing error Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 7 / 14

  26. Simulating meta-haplotypes (2) For each region: Simulate each meta-haplotype using a Markov model Transition matrices depend on distance between CGs and the type of region Assign haplotype i in region r frequency q i,r q 1,r q 1,r+1 q i,r q i,r+1 q H,r q H,r+1 Region r Region r+1 (3) Simulate read positions Simulate reads for region r by sampling from i th haplotype with probability q i,r Simulate sequencing error Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 7 / 14

  27. Distribution of β values CGI Non−CGI 4 3 density data Real (ADS) methsim 2 1 0 0 1 0 1 β values Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 8 / 14

  28. Within haplotype co-methylation at neighbouring CpGs 4 median log odds ratio CGI 0 data 4 Real (ADS) Non−CGI methsim 0 0 50 100 150 200 Distance between CpGs (bp) Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 9 / 14

  29. Within haplotype co-methylation at neighbouring CpGs 4 median log odds ratio all 0 data 4 ADS MySim all 0 0 50 100 150 200 Distance between CpGs (bp) Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 10 / 14

  30. Within haplotype co-methylation at neighbouring CpGs 4 (80% percentile band) median log odds ratio all 0 data 4 ADS MySim all 0 0 50 100 150 200 Distance between CpGs (bp) Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 10 / 14

  31. Correlations of pairs of β values 1 Pearson correlation CGI 0 data Real (ADS) 1 methsim Non−CGI 0 0 250 500 750 1000 Distance between CpGs (bp) Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 11 / 14

  32. Summary methsim models the mechanistic dependence structure of DNA methylation data. Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 12 / 14

  33. Summary methsim models the mechanistic dependence structure of DNA methylation data. Will be using methsim to simulate data with inserted DMRs and compare DMR-detection methods. Peter Hickey (@PeteHaitch) Simulating DNA methylation data 10 July 2014 12 / 14

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