Peax Interactive Concept Learning for Visual Exploration of Epigenetic Patterns

Peax Interactive Concept Learning for Visual Exploration of Epigenetic Patterns

Epigenomic data expresses a rich body of diverse patterns that help to identify regulatory elements like promoter, enhancers, etc. But finding these patterns reliably genome-wide is challenging. Peax is a tool for interactive visual pattern search and exploration of epigenomic patterns based on unsupervised representation learning with convolutional autoencoders. The visual search is driven by manually labeled genomic regions for actively learning a classifier to reflect your notion of interestingness.

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Fritz Lekschas

April 17, 2019
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    Peax Fritz Lekschas, Ph.D. candidate Harvard University Interactive Concept Learning

    for Visual Exploration of Epigenetic Patterns April 17, 2019 Brand Peterson, Daniel Haehn, Eric Ma, Nils Gehlenborg, and Hanspeter Pfister Bio-IT World
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    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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    • Little to no ground truth • Peak calling is

    not solved • Feature calling is very hard • Formally defining patterns is hard
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    • 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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    • 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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    • 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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    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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    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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    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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    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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    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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    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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    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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    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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    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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    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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    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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    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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    ENCODE e11.5 DNase-seq from face and hindbrain Differential, central, strong

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

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

    peak calls Balance positives and negatives Initial Classifier
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    CONCLUSION Leverage machine learning to improve visual exploratory pattern search

    Generative models work well as they show the encoding quality FUTURE WORK Explore other types of encoders (VAE, GAN, LSTM, etc.) Compare and evaluate different active learning strategies