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Title: Genome-wide hypothesis generation for single-cell expression via latent spaces of deep neural networks

Primary Focus Area

Computational Biology

Project Summary

The Human Cell Atlas (HCA) aims to provide a comprehensive map of all types of human cells. Connecting that map to disease states, which will be key to the CZI's mission of curing or managing all diseases in the next eighty years, will require us to see how these cell types change during aging, during disease processes, or in the presence of drugs. Ideally, we'd be able to apply a transformation to the HCA's reference map to predict and study these states.

Certain types of deep neural networks can generate hypothetical data by learning and decoding a lower dimensional latent space. An ideal latent space enables arithmetic operations that use data to produce realistic output for novel transformations. For example, FaceApp [@url:https://www.faceapp.com] can modify a picture of an individual to produce an image of the subject at an older age, with a different expression, or of a different gender.

The overall objective of this proposal is to determine how unsupervised deep neural network models can best be trained on single cell expression data from the HCA and the extent to which such models define biological latent spaces that capture disease states and targeted perturbations. The rationale is that latent space arithmetic for single cell transcriptomes would enable researchers to use predict how the expression of every gene would change in each HCA-identified cell type in numerous conditions including after drug treatment, in the context of a specific genetic variant, with a specific disease, or a combination of these and other factors.

Keywords

gene expression, deep learning, unsupervised, latent space, hypothesis generating

Full citations of contributions relevant to proposal

  • Tan J, Doing G, Lewis KA, Price CE, Chen KM, Cady KC, Perchuk B, Laub MT, Hogan DA, Greene CS. Unsupervised extraction of stable expression signatures from public compendia with an ensemble of neural networks. Cell Systems. 5:63-71. PMID:28711280
  • Beaulieu-Jones BK, Wu ZS, Williams C, Greene CS. Privacy-preserving generative deep neural networks support clinical data sharing. bioRxiv. doi:0.1101/159756
  • Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, Ferro E, Agapow P, Xie W, Rosen GL, Legenrich BJ, Israeli J, Lanchantin J, Woloszynek S, Carpenter AE, Shrikumar A, Xu J, Cofer EM, Harris DJ, DeCaprio D, Qi Y, Kundaje A, Peng Y, Wiley LK, Segler MHS, Gitter A+, Greene CS+. Opportunities and Obstacles for Deep Learning in Biology and Medicine. bioRxiv. doi:10.1101/142760
  • Way GP, Greene CS. Extracting a Biologically Relevant Latent Space from Cancer Transcriptomes with Variational Autoencoders. bioRxiv. doi:10.1101/174474
  • Beaulieu-Jones BK, Greene CS. Reproducibility of computational workflows is automated using continuous analysis. Nature Biotechnology. 35:342-346. PMID:28288103

Collaborative Network

Our collaborative network came together around a set of responses to the RFA that were shared openly on GitHub as they were written. Selected interactions between our groups center on topics such as:

  • Deep learning methods are expected to be relevant to many proposals. We recently completed a large GitHub-authored collaborative review of identify deep learning strategies for biological problems. Arjun Raj proposes to apply deep learning to images of single cells. We would love to use this RFA as an opportunity to host public discussions of techniques to troubleshoot deep learning challenges as they arise.
  • Data augmentation is a technique used in deep learning analysis of images in which arbitrary patches are selected or arbitrary rotations are applied during training to avoid learning irrelevant patterns. We propose an innovative equivalent for single cell expression data: sampling reads. This requires fast expression quantification. We plan to work with Rob Patro, an expert in fast gene expression quantification, to implement data augmentation by very rapidly sampling single cell observations. This parallels the sub-sampling proposed by Elana Fertig, and we will incorporate results from her work into our augmentation strategy.
  • Benchmark data need to be diverse and different data will be important for different evaluations. We will contribute a large compendium of harmonized bulk gene expression data from multiple rheumatic diseases. This, combined with HCA single cell assays, will help us to evaluate the extent to which bulk public data measuring disease states can be explained by HCA-identified cell types. Our analysis of latent spaces benefits from datasets that measure parallel perturbations across multiple cell types, like those proposed by Arjun Raj. Such data from the Raj lab and other groups will be needed to effectively evaluate unsupervised deep learning methods for single cell data.
  • Model interpretation is key for any unsupervised learning method. Elana Fertig and Loyal Goff propose new interpretation techniques, which we are excited to collaborate on. We have collaborated before via GitHub: members of the Fertig lab have requested features in software from our group, which we implemented. We look forward to continued interactions as a collaborative network.
  • Interactive servers can empower bench scientists to direct the analysis of single-cell genomics data. Lana Garmire's group developed Granatum, an interactive graphic interface for single cell analysis and is responding to this RFA. We will provide source code for generating deep neural network models of single cell data that can be incorporated into Granatum and Granatum-like systems.
  • Reproducible workflows will be needed to consistently and fairly evaluate methods. Our continuous analysis approach provides a framework for automated and reproducible analyses. Rob Patro's group has previously extended our example configurations to provide a workflow for Salmon. We are happy to work with any participating groups and CZI software developers to provide and configure continuous integration servers for scientific workflows. We have not budgeted this in our application as a centrally provided service makes sense; however, we can modify our budget to cover this if requested.

List of key personnel including name, organization, role on project

  • Casey Greene (PI)
  • Qiwen Hu (Postdoc)
  • Jaclyn Taroni (Postdoc)
  • Gregory Way (Graduate Student)

Proposal

Summary

Certain deep neural networks can generate hypothetical data by learning and decoding a lower dimensional latent space. This latent space enables arithmetic operations that produce realistic output for novel transformations. This allows users to generate hypothetical images [@arxiv:1502.04623] and to interpolate protein localizations through the cell-cycle [@arxiv:1708.04692]. An accessible example of latent space transformations comes from FaceApp [@url:https://www.faceapp.com], which modifies a picture of an individual to produce an image of the subject at an older age, with a different expression, or of a different genders.

Our overall objective is to determine how unsupervised deep neural network models can best be trained on single cell expression data from the Human Cell Atlas (HCA) and the extent to which such models define biological latent spaces that capture disease states and targeted perturbations. The rationale is that latent space arithmetic for genomic data would enable researchers to predict how the expression of every gene would change in each HCA-identified cell type after drug treatment, in the context of a specific genetic variant, with a specific disease, or a combination of these and other factors.

Aims

Aim 1: Develop proof-of-concept unsupervised deep learning methods for single cell transcriptomic data from the HCA.

Aim 2: Generate a benchmark dataset of harmonized public data to evaluate the extent to which HCA cell types capture rheumatic disease biology.

This proposal addresses two RFA points: Aim 1 develops machine learning approaches for solving the inference of state transitions and developmental trajectories, and Aim 2 provides curated benchmark datasets from existing data for evaluating computational methods and designing future assessments.

Prior Contributions / Preliminary Results

We previously developed neural-network based methods for unsupervised integration of transcriptomic data [@doi:10.1128/mSystems.00025-15 @doi:10.1016/j.cels.2017.06.003 @doi:10.1101/156620]. We now build to Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) which have a track record of defining meaningful latent spaces for images. We adapted GANs to generate realistic individuals under a differential privacy framework [@doi:10.1101/159756] and built VAEs over bulk transcriptomic data with the goal of describing a biologically-relevant latent space [@doi:10.1101/174474]. Here, we will apply these unsupervised deep learning methods to single cell transcriptomic data and incorporate novel data augmentation approaches for genomics. We also bring workflow automation experience to the HCA community [@doi:10.1038/nbt.3780].

Proposed work and deliverables

Aim 1: Develop proof-of-concept unsupervised deep learning methods for single cell transcriptomic data from the HCA.

The objective of this aim is to implement and test approaches to build deep generative models, such as VAEs [@arxiv:1312.6114] and GANs [@arxiv:1406.2661], from HCA single cell RNA-seq data.

Single cell data pose unique opportunities, but also challenges, for deep neural network algorithms. Many cells are often assayed, and many observations are needed to use deep learning effectively. However, transcript abundance estimates for each cell are generally subject to more error than bulk samples.

In our experience with generative deep learning [@doi:10.1101/159756 @doi:10.1101/174474] it can be difficult to predict optimal parameters in advance. We will perform a grid search over VAE architectures and hyperparameters to identify suitable options. We will evaluate zero-inflated loss among more traditional loss functions, as Chris Probert noted potential benefits on our proposal's GitHub repository [@url:https://github.com/greenelab/czi-rfa/issues/11] @doi:10.1186/s13059-015-0805-z @arxiv:1610.05857 @doi:10.1186/s13059-017-1188-0]. This process will identify a subset of parameters and architectures that are worth exploring further for single cells.

We will also develop data augmentation for single cell RNA-seq data, as no such approaches exist yet for transcriptomes. To understand data augmentation, imagine scanned pathology slides. Each slide may be prepared and scanned with a subtly different orientation or magnification. A deep learning method may identify these measurement differences, or there may be too few slides to train a good model. Applying arbitrary rotations, zooms, and other irrelevant transformations increases the effective amount of training data and reduces the model's propensity to learn such noise.

We plan to use fast abundance estimates for RNA-seq [@doi:10.1038/nmeth.4197 @10.1038/nbt.3519] to perform data augmentation for transcriptomes. Multiple resamples or subsamples of reads during transcript abundance estimation can capture uncertainty in the data, akin to arbitrary rotations. Therefore, we plan to collaborate with Rob Patro's laboratory (Collaborative Network) to implement these and related approaches. We posit that genomic data augmentation will improve latent feature generalization by separating biological from technical features and increasing the effective sample size during training.

We will select high-quality models by choosing those that minimize both reconstruction loss and KL divergence [@arxiv:1312.6114]. We will evaluate resulting models for their applicability to rheumatic disease and their suitability for latent space arithmetic (see: Evaluation).

Aim 2: Generate a benchmark dataset of harmonized public data to evaluate the extent to which HCA cell types capture rheumatic disease biology.

The HCA's partnership with the Immunological Genome Project (immgenH) will provide single-cell gene expression-based immunocyte phenotyping at an unprecedented resolution. A compendium comprised of bulk gene expression data from autoimmune/rheumatic diseases is exceptionally well-suited to evaluating the disease relevance of these immunocyte data. The objective of this aim is to build and share real and simulated benchmark datasets to evaluate the quality of the cell-type signatures. This will allow CZI to evaluate techniques, including VAEs and other methods, for defining cell-type-specific expression signatures from the HCA's single-cell datasets by measuring their ability to decompose bulk, whole-tissue autoimmune/rheumatic disease data.

We will generate simulated bulk datasets drawn from HCA-identified cell types by combining their expression profiles at different proportions. We will also build a multi-tissue autoimmune/rheumatic disease compendium from existing public datasets that we have curated (currently more than 12,000 samples). This compendium includes samples from patients with systemic lupus erythematosus (SLE), sarcoidosis, and inflammatory bowel disorders among many other diseases. Such a compendium lets us determine the extent to which HCA-derived cell type signatures capture disease-relevant information in a way that matches previous literature. For instance, we expect to detect higher proportions of activated macrophages in lupus nephritis samples than controls [@doi:10.4049/jimmunol.1103031].

These bulk compendia (simulated and real data) will enable HCA participants (computational-method and molecular-assay developers) to directly compare approaches where we expect their most immediate translational impact: application to existing datasets to explain disease-relevant phenomena via a single-cell perspective.

Proposal for evaluation and dissemination.

We will apply methods that produce low-dimensional representations including VAEs (Aim 1) and other methods to HCA-produced single cell transcriptomes. Source code that generates low-dimensional models will be released via GitHub, and we may produce a manuscript on the topic. Models and datasets will be disseminated via periodic release on Zenodo or a similar platform. We will test these low-dimensional representations via latent space arithmetic and relevance to disease as described below.

Evaluate the extent to which low-dimensional representations enable latent space arithmetic in the HCA.

Certain classes of generative deep neural network models, including VAEs and GANs have been shown to imbue intuitive mathematical features to the learned latent features [@arxiv:1312.6114 @arxiv:1406.2661]. For instance, a GAN learned latent features that could be manipulated with arithmetic: subtracting out the vector of a smile from a woman and adding it to a man with a neutral face resulted in an image of a smiling man [@arxiv:1511.06434]. We will evaluate the extent to which these properties exist for low-dimensional representations of the HCA's single-cell transcriptomes. We describe two experiments using data proposed by Arjun Raj's group (Collaborative Network), but any HCA benchmark datasets with similar properties will be suitable.

The Raj lab proposes to assay cardiomyocte differentiation from fibroblasts. The driving transcription factors for this process have been identified [@doi:10.1016/j.cell.2010.07.002]. A latent space vector between these two cell types should capture the key transcription factor (TF) networks (Gata4, Mef2c, and Tbx5). To calculate this vector we will subtract the latent space projections of fibroblasts from cardiomyocytes. We will compare the gene composition of this differentiation vector to TF-target calls from cistrome [@doi:10.1093/nar/gkw983], which are available for each of these TFs.

Latent space arithmetic can also generate new hypothetical data. We will test the extent to which these models predict the results of perturbations using data that Arjun Raj's homogenized cell type data. For each perturbation, we will hold out one or more cell types and map the rest into the latent space. Subtracting the latent space vector of included cell types from those after perturbation will produce a perturbation vector. We will add the perturbation vector to a withheld cell type to generate synthetic data and compare the synthetic and observed results to determine the prediction error. Comparing low-dimensional methods to a baseline of analogous transformations on raw gene expression can reveal whether or not these approaches more accurately predict perturbations.

Rheumatic Disease Evaluation

We will input signatures from low-dimensional projections into existing techniques that decompose bulk data with cell type signatures [@doi:10.1186/s13059-016-1070-5 @doi:10.1093/bioinformatics/btv015] and evaluate concordance with ground truth on Aim 2's simulated dataset. Comparing performance with multiple decomposition techniques allows us to benchmark methods' abilities to define bulk-relevant signatures from HCA data. We will also use signatures to decompose the rheumatic disease compendium. We can easily ask which methods produce cell-type signatures that explain the most variance in the compendium. But we can also use experiments within the compendium, such as studies of highly-targeted therapeutics (e.g., a monoclonal antibody to IFN-gamma in the context of systemic lupus erythematosus [@doi:10.1002/art.39248]), to develop additional data-driven hypotheses. In the case if the IFN-gamma antibody, we can use various methods to predict which cell-types change in proportion or pathway activation during the reduction of this cytokine. These analyses will allow us to identify disease-relevant cases where methods disagree, laying the groundwork for targeted experiments (beyond the one-year timeline) that directly probe these processes to produce informative ground-truth benchmarks.

Statement of commitment to share

We commit to sharing proposals, methods, data, and code publicly under open licenses. We understand that our proposal will be shared if it is funded: we shared it publicly under a CC-BY license as it was written.

References (Attached)

Biosketch (Attached)

Casey Greene, PhD (PI). 1.2 Cal Months. * cgreene on github. * Dr. Greene has primary responsibility for managing and carrying out the proposed research, interpreting findings, disseminating findings, and preparing and submitting progress reports.

Description of other key personnel

Gregory Way, MS (GCB Graduate Student). 4.2 Cal Months. Gregory Way (gwaygenomics on GitHub) is a graduate student in the Genomics and Computational Biology PhD program at the University of Pennsylvania performing thesis research in Dr. Greene's laboratory. Mr. Way has developed supervised and unsupervised methods to extract knowledge from bulk cancer transcriptomes, including the training of a VAE. He will, with Dr. Greene, extend this VAE to learn single cell features. He is the first author on the bulk VAE manuscript that serves as preliminary data for Aim 1. Together with Dr. Hu, he will be responsible for developing Aim 1 – namely, training an optimal single cell VAE with an augmentation approach. He will also lead the latent space arithmetic approach proposed in Aim 3. Mr. Way will participate in experimental design, analysis, and manuscript preparation.

Qiwen Hu, PhD (Postdoctoral Researcher). 6.0 Cal Months. Qiwen Hu (huqiwen0313 on GitHub) is a postdoc at the University of Pennsylvania in Dr. Greene's laboratory. Dr.Hu has developed supervised and unsupervised methods to learn genomic translational regulatory features. She will, with Dr. Greene, extend the methods to learn single cell features. Together with Mr. Way, she will be responsible for developing Aim 1 – namely, training an optimal single cell VAE with an augmentation approach. She will also work on the latent space arithmetic approach proposed in Aim 3. Dr. Hu will participate in experimental design, analysis, and manuscript preparation.

Jaclyn Taroni, PhD (Postdoctoral Researcher). 2.4 Cal Months. Jaclyn Taroni (jaclyn-taroni on GitHub) is a postdoctoral researcher in the Dr. Greene’s laboratory. Dr. Taroni has expertise in rheumatic diseases and in developing computational approaches to study transcriptomic data in these disorders. She has developed frameworks for integrating large compendia of bulk transcriptomic data and will be responsible for building the compendium of bulk rheumatic disease data in Aim 2. Dr. Taroni will evaluate the biological relevance of the low-dimensional representations of single-cell data learned by various methods in the context of rheumatic diseases. She will participate in experimental design, analysis, and manuscript preparation.

Brief preliminary budget (Attached)