Interactive Visual Pattern Search in Epigenomic Data with Peax: NIH ENCODE presentation

Slides from my presentation on visual pattern search in epigenomic data with Peax for the NIH ENCODE Analysis Working Group.

Project page: http://peax.lekschas.de
Paper: https://vcg.seas.harvard.edu/pubs/peax
Video introduction: https://youtu.be/FlzTdFUVE-M

Transcript

1. Peax
   Fritz Lekschas, Ph.D. candidate
   Harvard University
   Interactive Visual Pattern Search in Epigenomic Data
   Using Unsupervised Deep Representation Learning
   July 27, 2020
   Brand Peterson, Daniel Haehn, Eric Ma,

   Nils Gehlenborg, and Hanspeter Pfister
   

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2. View Slide

3. Search
   

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4. Search
   ? ? ? ? ? ?
   

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5. Davis et al. (2018) The Encyclopedia of DNA elements (ENCODE): data portal update.
   

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6. Davis et al. (2018) The Encyclopedia of DNA elements (ENCODE): data portal update. Maurano et al. (2012).
   >90% of disease-
   associated variants
   found in GWAS are
   located in non-
   coding regions
   

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7. Why not just search
   computationally?
   

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8. • Little to no ground truth
   • Peak calling is not solved
   • Feature calling is very hard
   • Formally defining patterns is hard
   

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9. • Little to no ground truth
   • Peak calling is not solved
   • Feature calling is very hard
   • Formally defining patterns is hard
   Visual quality control
   

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10. • Little to no ground truth
    • Peak calling is not solved
    • Feature calling is very hard
    • Formally defining patterns is hard
    Visual quality control
    

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11. • Little to no ground truth
    • Peak calling is not solved
    • Feature calling is very hard
    • Formally defining patterns is hard
    Visual quality control
    Interactive visual query
    

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12. Search Query
    

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13. Search Query Features
    Number of peaks
    Height of peaks
    Shape of peaks
    Position of peaks
    Average signal
    ...
    3
    37
    0.9
    14
    5
    2
    51
    1.3
    12
    7
    4
    29
    9.1
    14
    11
    2
    41
    1.0
    14
    8
    Example
    

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14. Search Query Features
    Number of peaks
    Height of peaks
    Shape of peaks
    Position of peaks
    Average signal
    ...
    3
    37
    0.9
    14
    5
    2
    51
    1.3
    12
    7
    4
    29
    9.1
    14
    11
    2
    41
    1.0
    14
    8
    Example
    Result
    

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15. 1. Encoding 2. Active Learning & User Interface
    PEAX
    

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16. 1. Encoding 2. Active Learning & User
    

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17. 2. Active Learning & User Interface
    

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18. 1. Data Processing 2. Convolutional Autoencoder
    DATA ENCODING
    

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19. 1. Data Processing 2. Convolutional Autoencoder
    DATA ENCODING
    

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20. 1. Data Processing 2. Convolutional Autoencoder
    1
    0
    DATA ENCODING
    

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21. 1. Data Processing 2. Convolutional Autoencoder
    DATA ENCODING
    

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22. 1. Data Processing 2. Convolutional Autoencoder
    DATA ENCODING
    

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23. Trained 6 autoencoders: 3 window sizes × 2 data types
    Data types: DNase, histone mark ChIP
    Window and bin sizes: 3 kb (25 bp), 12 kb (100 bp), 120 kb (1000 bp)
    120 DNase-seq datasets from ENCODE
    49 histone mark ChIP-seq experiments from Roadmap Epigenomics:

    H3K4me1/me3, H3K27ac/me3, H3K9ac/me3, H3K36me3
    

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24. 3 kb 12 kb 120 kb
    DNase-seq
    ChIP-seq
    

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25. 3 kb 12 kb 120 kb
    DNase-seq
    R2 .98 .90 .78
    R2 .84 .69 .73
    ChIP-seq
    

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26. Is the learned encoding useful for similarity search?
    Yes!
    

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27. Is the learned encoding useful for similarity search?
    Yes!
    Yes!
    

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28. Ours: CAE
    ED
    SAX
    DTW
    XCORR
    UMAP
    TSFRESH
    

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29. Ours: CAE
    ED
    UMAP TSFRESH XCORR DTW
    SAX
    

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30. Ours: CAE
    ED
    UMAP TSFRESH XCORR DTW
    SAX
    

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31. View Slide

32. Query view
    Reconstructions
    

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33. List view
    Labeling
    Tabs
    

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34. Embedding
    

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35. Progress
    

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36. View Slide

37. Find Asymmetrical Peaks
    Video screencast available at

    youtu.be/FlzTdFUVE-M?t=220
    

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38. Select Pattern for Querying
    Initial Sampling
    Binary Labeling
    Train First Classifier
    Active Learning Sampling
    Training Progress
    Embedding View
    Resolve Conflicts
    Explore Final Results Spatially Freely browse and select a
    region for quering
    Size of the selected region is
    fixed and based on the
    autoencoder
    We use HiGlass (Kerpedjiev et
    al. 2018) as the genome
    browser
    

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39. Initial Sampling
    Binary Labeling
    Train First Classifier
    Active Learning Sampling
    Training Progress
    Embedding View
    Resolve Conflicts
    Explore Final Results Spatially
    al. 2018) as the genome
    browser
    Increase distance of samples
    to the query
    Sample regions in dense areas
    Maximize pairwise distance
    between samples
    All in the latent space
    

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40. Initial Sampling
    Binary Labeling
    Train First Classifier
    Active Learning Sampling
    Training Progress
    Embedding View
    Resolve Conflicts
    Explore Final Results Spatially
    al. 2018) as the genome
    browser
    Increase distance of samples
    to the query
    Sample regions in dense areas
    Maximize pairwise distance
    between samples
    All in the latent space
    

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41. Binary Labeling
    Train First Classifier
    Active Learning Sampling
    Training Progress
    Embedding View
    Resolve Conflicts
    Explore Final Results Spatially
    al. 2018) as the genome
    browser
    Select regions that match and
    do not match the query
    Inconclusive regions can
    simply be skipped
    

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42. Train First Classifier
    Active Learning Sampling
    Training Progress
    Embedding View
    Resolve Conflicts
    Explore Final Results Spatially
    al. 2018) as the genome
    browser
    A random forrest classifier is
    trained online with the labels
    Each time a new set of
    samples is requested a new
    classfier is trained
    A new classifier can also be
    trained in between after labels
    have changed
    

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43. Active Learning Sampling
    Training Progress
    Embedding View
    Resolve Conflicts
    Explore Final Results Spatially
    al. 2018) as the genome
    browser
    Regions are sampled by their:
    - prediction uncertain

    - proximity to the target

    - in dense neighborhoods

    - with high pairwise distance
    

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44. Training Progress
    Embedding View
    Resolve Conflicts
    Explore Final Results Spatially
    al. 2018) as the genome
    browser
    Progress is tracked for every
    trained classifier
    Uncertainty is the overall
    prediction probability
    Change of the prediction
    probaility
    Convergence and divergence
    

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45. Embedding View
    Resolve Conflicts
    Explore Final Results Spatially
    al. 2018) as the genome
    browser
    Convergence and divergence
    2D UMAP embedding of all
    encoded regions
    Probability color encoder:

    ⬤ means matching

    ⬤ means non-matching

    ⬤ means unpredictable
    View is interactive and dots are
    selectable
    

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46. Embedding View
    Resolve Conflicts
    Explore Final Results Spatially
    al. 2018) as the genome
    browser
    Convergence and divergence
    2D UMAP embedding of all
    encoded regions
    Probability color encoder:

    ⬤ means matching

    ⬤ means non-matching

    ⬤ means unpredictable
    View is interactive and dots are
    selectable
    

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47. Resolve Conflicts
    Explore Final Results Spatially
    al. 2018) as the genome
    browser
    Convergence and divergence
    View is interactive and dots are
    selectable
    Peax warns about false
    positives and negatives when
    the labels and the classifier's
    predictions disagree
    

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48. Explore Final Results Spatially
    al. 2018) as the genome
    browser
    Convergence and divergence
    View is interactive and dots are
    selectable
    The query view is interactive
    A bed-like track shows the
    prediction probabilities:

    ⬤ means matching

    ⬤ means non-matching

    ⬤ means unpredictable
    

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49. Find Differentially Accessible Peaks
    Using Existing Peak Calls
    

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50. ENCODE e11.5 DNase-seq from face and hindbrain
    Differential, central, strong peak calls
    Balance positives and negatives
    Initial Classifier
    

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51. ENCODE e11.5 DNase-seq from face and hindbrain
    Differential, central, strong peak calls
    Balance positives and negatives
    Initial Classifier
    

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52. ENCODE e11.5 DNase-seq from face and hindbrain
    Differential, central, strong peak calls
    Balance positives and negatives
    Initial Classifier
    

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53. Resolve Conflicts
    Refine Labels & Assess Recall
    

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54. Explore Local Neighborhoods
    

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55. Final Results Excluding Labels
    

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56. CONCLUSION
    Leverage deep learning to augment human intelligence

    for visual pattern exploration
    Complementary to specialized feature detectors
    FUTURE WORK
    Explore other types of encoders
    Evaluate different active learning strategies
    

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57. PAPER, SOURCE CODE, MODELS, ETC.
    vcg.seas.harvard.edu/pubs/peax
    lekschas@seas.harvard.edu
    @flekschas
    lekschas.de
    CONTACT
    Thank You!
    

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58. H3K4me1 H3K4me3 H3K27ac
    Individual histone marks
    H3K27me3 H3K9ac H3K9me3 H3K36me3
    3 kb
    12 kb
    120 kb
    

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59. Backend: Python (Flask)
    Frontend: JavaScript (React)
    Autoencoders: Keras (Tensorflow)
    Genome Browser: HiGlass
    Search Setup: JSON file !
    {
    "encoders": [{
    "content_type": "dnase-seq-3kb",
    "from_file": "examples/autoencoders.json"
    }, {
    "content_type": "histone-mark-chip-seq-3kb",
    "from_file": "examples/autoencoders.json"
    }],
    "datasets": [{
    "filepath": "examples/data/ENCFF641OPE.bigWig",
    "content_type": "dnase-seq-3kb",
    "id": "encode-e11-5-limb-dnase-rdns",
    "name": "e11.5 limb DNase rdn signal"
    }, {
    "filepath": "examples/data/ENCFF336LAW.bigWig",
    "content_type": "histone-mark-chip-seq-3kb",
    "id": "encode-e11-5-limb-chip-h3k27ac-fc",
    "name": "e11.5 limb H3K27ac fc"
    }],
    "coords": "mm10",
    "chroms": ["chr12"],
    "step_freq": 2,
    "db_path": "examples/search-e11-5-limb.db"
    }
    TECHNOLOGY EXAMPLE SEARCH SETUP
    

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