methylCC presentation

Presentation from bioRxiv paper: https://www.biorxiv.org/content/early/2017/11/03/213769

Transcript

1. Estimating cell type composition
   in whole blood using
   differentially methylated regions
   Stephanie Hicks
   Assistant Professor, Biostatistics
   Johns Hopkins Bloomberg School of Public Health
   

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2. ATCGCGTTACTGCGGAA
   TAGCGCAATGTCGCCTT
   m
   m
   m
   m
   m
   m
   What is DNA Methylation?
   

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3. What is DNA Methylation?
   ATCGCGTTACTGCGGAA
   TAGCGCAATGTCGCCTT
   m
   m
   m
   

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4. What is DNA Methylation?
   ATCGCGTTACTGCGGAA
   TAGCGCAATGTCGCCTT
   m
   m
   m
   

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

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6. 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)
   

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7. Jaffe and Irizarry (2014). Genome Biology
   • Different cell compositions in whole blood imply different
   observed whole blood DNA methylation profiles
   • Important to estimate differences in cell composition
   Cell composition changes with age
   

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8. Statistical 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
   relative cell type
   proportions
   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
   

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9. New platform technologies emerging
   First approach
   • Apply Houseman method using new platform technology
   

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10. ●
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    Mono Tcell
    Bcell Gran
    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
    Cell composition estimates from whole blood samples
    measured on two platforms (Houseman method)
    450k samples (n = 10):
    (485,512 CpGs)
    RRBS samples (n = 10):
    (6,823,620 CpGs)
    Total CpG overlaps:
    (142,002 CpGs)
    Houseman cell type-
    specific CpG overlaps:
    (91/600 CpGs)
    Consider n = 10 whole
    blood sample measured
    on two platforms:
    • 450k (microarray)
    • RRBS (sequencing)
    

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11. New platform technologies emerging
    First approach
    • Apply Houseman method using new platform technology
    Problems with this approach
    1. Not all CpGs are included in new platforms
    2. Observed methylation levels depend on platform used
    

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12. Chromosome 14
    0
    0.2
    0.4
    0.6
    0.8
    Observed Methylation
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    ● CD8T ● CD4T ● NK ● Bcell ● Mono ● Gran
    CpG
    DMR
    0
    0.2
    0.4
    0.6
    0.8
    Observed Methylation
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    ● 450k ● RRBS
    102.6767 mb
    102.6768 mb
    102.6769 mb
    102.677 mb
    102.6771 mb
    102.6772 mb
    102.6773 mb
    Cell types preserve their
    methylation state across regions
    Beta values
    (Purified cell types on
    measured on
    microarray platform)
    Beta values
    (One whole blood
    sample measured on
    sequencing platform)
    

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13. Cell types preserve their
    methylation state across regions
    Cell type-specific CpG
    Cell type-specific region
    Beta values
    (Purified cell types on
    measured on 450k array)
    • Identify regions using
    bumphunter BioC pkg
    Chromosome 14
    0
    0.2
    0.4
    0.6
    0.8
    Observed Methylation
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    ● CD8T ● CD4T ● NK ● Bcell ● Mono ● Gran
    CpG
    DMR
    0
    0.2
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    0.8
    Observed Methylation
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    ● 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
    platform
    Sequencing
    platform
    Using
    CpGs
    0.45 NA
    Using
    Regions
    0.55 0.50
    

    View Slide

14. New platform technologies emerging
    First approach
    • Apply Houseman method using new platform technology
    Problems with this approach
    1. Not all CpGs are included in new platforms
    2. Observed methylation levels depend on platform used
    

    View Slide

15. Chromosome 6
    0
    0.2
    0.4
    0.6
    0.8
    Observed Methylation
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    ● 450k ● RRBS
    33.257 mb
    33.258 mb
    33.259 mb
    33.26 mb
    33.261 mb
    33.262 mb
    33.263 mb
    33.264 mb
    Platform-dependent differences between
    microarray (450k) and sequencing (RRBS) platforms
    

    View Slide

16. 0
    50
    100
    density
    Regions
    Not methylated
    Methylated
    Platform
    450k
    Platform-dependent differences
    between 450k array and RRBS platforms
    Chromosome 6
    0
    0.2
    0.4
    0.6
    0.8
    Observed Methylation
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    ● 450k ● RRBS
    33.284 mb
    33.285 mb
    33.286 mb
    33.287 mb
    33.288 mb
    33.289 mb
    

    View Slide

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

    View Slide

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

    View Slide

19. Recall Houseman Model:
    = +
    Y
    (Jx1)
    X
    (JxK)
    = E
    (Jx1)
    π
    (Kx1)
    J CpGs
    1 whole
    blood sample
    relative cell type
    proportions
    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
    Platform-dependent
    methylation 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
    

    View Slide

20. Our proposed model:
    = +
    Y
    (Rx1)
    X
    (RxK)
    = E
    (Rx1)
    π
    (Kx1)
    R regions
    1 whole
    blood sample
    relative cell type
    proportions
    Measurement
    error
    r = (1,...., R) = differentially methylated regions
    k = (1,...,K) = cell types
    +
    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
    !"
    ~
    $%
    ~
    $&
    ~
    

    View Slide

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

    View Slide

22. Our proposed model:
    = +
    Y
    (Rx1)
    X
    (RxK)
    = E
    (Rx1)
    π
    (Kx1)
    R regions
    1 whole
    blood sample
    relative cell type
    proportions
    Measurement
    error
    r = (1,...., R) = differentially methylated regions
    k = (1,...,K) = cell types
    +
    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
    !"
    ~
    $%
    ~
    $&
    ~
    

    View Slide

23. 200
    150
    100
    50
    Cell type−specific DNAm profiles
    Methylated (black), Unmethylated (white)
    Cell types
    Regions (R=212)
    Tcell Bcell Mono Gran
    the “Z” matrix
    

    View Slide

24. Use informative genomic regions that are clearly methylated or
    unmethylated for each cell type
    1. Initialize parameter values
    2. Use EM algorithm for estimation
    Estimation
    θi
    (0) = (πi1
    (0),πi2
    (0),...,πiK
    (0),α0
    (0),α1
    (0),(σ0
    2 )(0),(σ1
    2 )(0),(σ 2 )(0) )
    

    View Slide

25. Just need the conditional
    distributions:
    Constructing the likelihood
    Complete-data likelihood:
    Complete-data vector:
    i = (1,..., N) = whole blood samples
    r = (1,...., R) = differentially methylated regions
    k = (1,...,K) = cell types
    

    View Slide

26. Theorem
    If
    Conditional distribution
    X
    ((r+s)×1)
    =
    (X
    1
    )
    (r×1)
    (X
    2
    )
    (s×1)
    ⎡
    ⎣
    ⎢
    ⎢
    ⎤
    ⎦
    ⎥
    ⎥
    ~ N
    r+s
    (µ,Σ)
    where µ((r+s)×1)
    =
    µ1
    µ2
    ⎡
    ⎣
    ⎢
    ⎢
    ⎤
    ⎦
    ⎥
    ⎥
    and Σ =
    Σ11
    Σ12
    Σ21
    Σ22
    ⎡
    ⎣
    ⎢
    ⎢
    ⎤
    ⎦
    ⎥
    ⎥
    X
    2
    | X
    1
    ~ N
    s
    (µ2
    + Σ21
    Σ11
    −1(X
    1
    − µ1
    ), Σ22
    − Σ21
    Σ11
    −1Σ12
    )
    Then,
    

    View Slide

27. where
    (Similar step for )
    Conditional distribution
    Use conditional distribution for the Expectation Step
    

    View Slide

28. Maximization Step
    !"
    = $
    %&"
    '
    (),%
    !+
    = $
    %&"
    '
    (",%
    !,
    = $
    %&"
    '
    (),%
    +
    !-
    = $
    %&"
    '
    (",%
    +
    where
    

    View Slide

29. Maximization Step
    Use quadratic programming:
    solve.QP() in quadprog R package
    (nonnegative and constrained)
    

    View Slide

30. How does our model perform?
    

    View Slide

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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 platform
    RMSE:
    0.0385
    RMSE:
    0.0531
    

    View Slide

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    Tcell Bcell Mono Gran
    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 1.000.00 0.25 0.50 0.75 1.00
    0.00
    0.25
    0.50
    0.75
    1.00
    Reference−based model (Houseman)
    Proposed model (Hicks)
    Model−based cell composition estimates from whole blood samples (n=689, Li et al. 2013)
    N = 689 whole blood samples run on 450k
    microarray platform
    

    View Slide

33. Simulation Study
    0.0 0.2 0.4 0.6 0.8 1.0
    0 10 20 30 40
    Simulated platform−dependent
    random effects
    Methylation
    density
    450k
    RRBS
    A
    ●
    Houseman methylCC
    0.03 0.05 0.07 0.09
    Simulated data from 450k platform
    Cell composition estimation method
    RMSE
    B
    ●
    ●
    Houseman methylCC
    0.03 0.05 0.07 0.09
    Simulated data from RRBS platform
    Cell composition estimation method
    RMSE
    C
    

    View Slide

34. N = 10 samples measured
    on two platforms:
    • 450k microarray
    • RRBS sequencing
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    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
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    1.00
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    1.00
    450K platform
    RRBS platform
    Method ● ●
    Our method Houseman
    Cell composition estimates from whole blood samples
    measured on two platforms
    

    View Slide

35. For more information
    methylCC:
    https://github.com/stephaniehicks/methylCC
    Comments/Suggestions:
    email: shicks19@jhu.edu
    GitHub & Twitter: @stephaniehicks
    Pre-print on bioRxiv:
    https://www.biorxiv.org/content/early/2017/11/03/213769
    CCG
    Me
    

    View Slide