Multi-domain translation between single-cell imaging and sequencing data using autoencoders

Yang, Karren; Belyaeva, Anastasiya; Venkatachalapathy, Saradha; Damodaran, Karthik; Katcoff, Abigail; Radhakrishnan, Adityanarayanan; Shivashankar, G. V.; Uhler, Caroline

doi:10.1038/s41467-020-20249-2

Cited by 113 publications

(91 citation statements)

References 30 publications

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“…By training these VAE variants on L1000 gene expression readouts resulting from the same set of compound perturbations, we observed large differences in optimal hyperparameters as compared to training on Cell Painting image-based data (Subramanian et al, 2017;Xue et al, 2020). These observations indicate that the KL divergence penalty strongly influences cell morphology modeling ability, and that lessons learned by modeling other biomedical data types, such as gene expression, will not necessarily directly translate to cell morphology (Yang et al, 2021).…”

Section: Discussionmentioning

confidence: 96%

Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic

Chow

Singh

Carpenter

et al. 2021

Preprint

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A variational autoencoder (VAE) is a machine learning algorithm, useful for generating a compressed and interpretable latent space. These representations have been generated from various biomedical data types and can be used to produce realistic-looking simulated data. However, standard vanilla VAEs suffer from entangled and uninformative latent spaces, which can be mitigated using other types of VAEs such as β-VAE and MMD-VAE. In this project, we evaluated the ability of VAEs to learn cell morphology characteristics derived from cell images. We trained and evaluated these three VAE variants-Vanilla VAE, β-VAE, and MMD-VAE-on cell morphology readouts and explored the generative capacity of each model to predict compound polypharmacology (the interactions of a drug with more than one target) using an approach called latent space arithmetic (LSA). To test the generalizability of the strategy, we also trained these VAEs using gene expression data of the same compound perturbations and found that gene expression provides complementary information. We found that the β-VAE and MMD-VAE disentangle morphology signals and reveal a more interpretable latent space. We reliably simulated morphology and gene expression readouts from certain compounds thereby predicting cell states perturbed with compounds of known polypharmacology. Inferring cell state for specific drug mechanisms could aid researchers in developing and identifying targeted therapeutics and categorizing off-target effects in the future.

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Section: Discussionmentioning

confidence: 96%

Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic

Chow

Singh

Carpenter

et al. 2021

Preprint

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“…Apart from the feature-converted data, Seurat v3 15 and bindSC 30 also devised heuristic strategies to utilize information in the original feature space, which probably explains their improved performance than methods that do not 16, 17 . At the cell level, known cell types have also been used via (semi-)supervised learning 47, 48 , but this approach incurs substantial limitations in terms of applicability since such supervision is typically unavailable and in many cases serves as the purpose of multi-omics integration per se 26 . Notably, one of these methods was proposed with a similar autoencoder architecture and adversarial alignment 48 , but it relied on matched cell types or clusters to orient the alignment.…”

Section: Discussionmentioning

confidence: 99%

Multi-omics integration and regulatory inference for unpaired single-cell data with a graph-linked unified embedding framework

Cao

Gao

2021

Preprint

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With the ever-increasing amount of single-cell multi-omics data accumulated during the past years, effective and efficient computational integration is becoming a serious challenge. One major obstacle of unpaired multi-omics integration is the feature discrepancies among omics layers. Here, we propose a computational framework called GLUE (graph-linked unified embedding), which utilizes accessible prior knowledge about regulatory interactions to bridge the gaps between feature spaces. Systematic benchmarks demonstrated that GLUE is accurate, robust and scalable. We further employed GLUE for various challenging tasks, including triple-omics integration, model-based regulatory inference and multi-omics human cell atlas construction (over millions of cells) and found that GLUE achieved superior performance for each task. As a generalizable framework, GLUE features a modular design that can be flexibly extended and enhanced for new analysis tasks. The full package is available online at https://github.com/gao-lab/GLUE for the community.

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“…In these situations, modality alignment becomes paramount ( Lopez et al, 2019 ). A recent work integrating single-cell RNA-sequencing data and single-cell nuclear-imaging of naive T-cells ( Yang et al, 2021 ) has shown that DL representation of images contain signals predictive of true fold change of gene expression between different classes of cells.…”

Section: Going Further With Deep Learningmentioning

confidence: 99%

Deep learning for bioimage analysis in developmental biology

et al. 2021

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Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning. We then review how deep learning has impacted bioimage analysis and explore the open-source resources available to integrate it into a research project. Finally, we discuss the future of deep learning applied to cell and developmental biology. We analyze how state-of-the-art methodologies have the potential to transform our understanding of biological systems through new image-based analysis and modelling that integrate multimodal inputs in space and time.

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Multi-domain translation between single-cell imaging and sequencing data using autoencoders

Cited by 113 publications

References 30 publications

Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic

Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic

Multi-omics integration and regulatory inference for unpaired single-cell data with a graph-linked unified embedding framework

Deep learning for bioimage analysis in developmental biology

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