Es#ma#ng cell type composi#on in whole blood using differen#ally methylated regions Stephanie Hicks Bioconductor 2017

ATCGCGTTACTGCGGAA TAGCGCAATGTCGCCTT m m m m m m What is DNA Methyla#on?

ATCGCGTTACTGCGGAA TAGCGCAATGTCGCCTT m m m What is DNA Methyla#on?

ATCGCGTTACTGCGGAA TAGCGCAATGTCGCCTT m m What is DNA Methyla#on? m

Data from GSE32148 20 30 40 50 60 70 0.02 0.06 0.10 Age Methylation DNA methyla#on in whole blood correlates with age at this one CpG Slide courtesy of A. Jaffe and R. Irizarry

Blood is a mixture of many cell types NK NK NK NK NK NK CD8T CD8T CD8T CD8T CD8T CD8T CD4T CD4T CD4T CD4T CD4T CD4T Gran Gran Gran Gran Gran Gran Bcell Bcell Bcell Bcell Bcell Bcell Mono Mono Mono Mono Mono Mono CpGs Cell types Whole blood cell types: •  Tcells •  CD8T •  CD4T •  Natural Killer •  Bcells •  Granulocytes •  Monocytes Bioconductor data package available: •  Data originally from Reinius et al. (2012) > library(FlowSorted.Blood.450k)

Cell composi#on changes with age Jaffe and Irizarry (2014). Genome Biology •  Different cell composi#ons in whole blood imply different observed whole blood DNA methyla#on profiles •  Important to es#mate differences in cell composi#on

Sta#s#cal Model: Houseman et al. (2012) Y ij = πik k=1 K ∑ X jk +εij = + Y (Jx1) X (JxK) = E (Jx1) π (Kx1) J CpGs K cell type profiles whole blood sample i = (1,..., N) = whole blood samples j = (1,...., J) = CpGs k = (1,...,K) = cell type profiles Measurement error rela#ve cell type propor#ons NK NK NK NK NK NK CD8T CD8T CD8T CD8T CD8T CD8T CD4T CD4T CD4T CD4T CD4T CD4T Gran Gran Gran Gran Gran Gran Bcell Bcell Bcell Bcell Bcell Bcell Mono Mono Mono Mono Mono Mono

New plaYorm technologies emerging First approach •  Apply Houseman method using new plaYorm technology Problems with this approach 1.  Observed methyla#on levels depend on plaYorm used 2.  Not all CpGs are included in new plaYorms

0 50 100 density Regions Not methylated Methylated Platform 450k PlaYorm-dependent differences between 450k array and RRBS plaYorms Chromosome 6 0 0.2 0.4 0.6 0.8 Observed Methylation ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 450k ● RRBS 33.284 mb 33.285 mb 33.286 mb 33.287 mb 33.288 mb 33.289 mb

PlaYorm-dependent differences between 450k array and RRBS plaYorms 0 50 100 0.00 0.25 0.50 0.75 1.00 Methylation density Regions Not methylated Methylated Platform 450k RRBS

New plaYorm technologies emerging First approach •  Apply Houseman method using new plaYorm technology Problems with this approach 1.  Observed methyla#on levels depend on plaYorm 2.  Not all CpGs are included in new plaYorms

Cell types preserve their methyla#on state across regions Cell type-specific CpG Cell type-specific region Beta values (Purified cell types on measured on 450k array) •  Iden#fy regions using bumphunter BioC pkg Chromosome 14 0 0.2 0.4 0.6 0.8 Observed Methylation ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● CD8T ● CD4T ● NK ● Bcell ● Mono ● Gran CpG DMR 0 0.2 0.4 0.6 0.8 Observed Methylation ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● 450k ● RRBS 102.6767 mb 102.6768 mb 102.6769 mb 102.677 mb 102.6771 mb 102.6772 mb 102.6773 mb Beta values (One whole blood sample) Microarray plaGorm Sequencing plaGorm Using CpGs 0.45 NA Using Regions 0.55 0.50

Recall Houseman Model: = + Y (Jx1) X (JxK) = E (Jx1) π (Kx1) J CpGs 1 whole blood sample rela#ve cell type propor#ons Measurement error 0.78 0.77 0.85 0.82 0.05 0.73 0.81 0.77 0.79 0.02 0.73 0.84 0.83 0.80 0.03 0.78 0.87 0.89 0.83 0.07 ! ! ! ! 0.06 0.09 0.81 0.08 0.07 0.06 0.03 0.77 0.02 0.04 0.08 0.03 PlaYorm-dependent methyla#on profiles Y ij = πik k=1 K ∑ X jk +εij i = (1,..., N) = whole blood samples j = (1,...., J) = CpGs k = (1,...,K) = cell type profiles

Our proposed model: = + Y (Rx1) X (RxK) = E (Rx1) π (Kx1) R regions 1 whole blood sample rela#ve cell type propor#ons Measurement error Y r = πk (1− Z rk )δo,r + Z rk δ1,r ⎡ ⎣ ⎤ ⎦+εr k=1 K ∑ r = (1,...., R) = differentially methylated regions k = (1,...,K) = cell types δ0,r ~ N(α0 , σ0 2 ) δ1,r ~ N(α1 , σ1 2 ) εr ~ N(0, σ 2 ) + 1-Z (RxK) δ0 δ1 Z (RxK) 1 1 1 1 0 1 1 1 1 0 1 1 1 1 0 1 1 1 1 0 ! ! ! ! 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 ! ! ! ! 1 1 0 1 1 1 1 0 1 1 1 1 Z rk = 1 if region r and cell type k is methylated 0 otherwise ⎧ ⎨ ⎪ ⎩ ⎪ 0.05 0.08 0.02 0.04 0.05 ! 0.09 0.07 0.06 0.87 0.89 0.75 0.82 0.79 ! 0.81 0.76 0.90

How does our model perform?

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● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Lymph Mono Gran Houseman Hicks 0.00 0.25 0.50 0.75 1.000.00 0.25 0.50 0.75 1.000.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 True Cell Composition (measured with flow cytometry) Model−based Cell Composition Estimates N = 800 whole blood samples run on 450k microarray plaYorm RMSE: 0.0385 RMSE: 0.0531

N = 12 samples measured on two plaYorms: •  450k microarray •  RRBS sequencing ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Mono Gran Bcell Tcell 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 450K platform RRBS platform Method ● ● Our method Houseman Cell composition estimates from whole blood samples measured on two platforms

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For more informa#on methylCC: hbps://github.com/stephaniehicks/methylCC Comments/SuggesNons: email: shicks@jimmy.harvard.edu GitHub & Twiber: @stephaniehicks CCG Me #BioC2017 #RLadies #dataparasite