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ACTIVA: realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders

View ORCID ProfileA. Ali Heydari, Oscar A. Davalos, View ORCID ProfileLihong Zhao, Katrina K. Hoyer, Suzanne S. Sindi
doi: https://doi.org/10.1101/2021.01.28.428725
A. Ali Heydari
1Department of Applied Mathematics, University of California, Merced, 95343, USA
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Oscar A. Davalos
2Quantitative and Systems Biology Graduate Program, University of California Merced, 95343, USA
4Health Sciences Research Institute, University of California, Merced, 95343, USA
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Lihong Zhao
1Department of Applied Mathematics, University of California, Merced, 95343, USA
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Katrina K. Hoyer
3Department of Molecular and Cell Biology, University of California, Merced, 95343, USA
4Health Sciences Research Institute, University of California, Merced, 95343, USA
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Suzanne S. Sindi
1Department of Applied Mathematics, University of California, Merced, 95343, USA
4Health Sciences Research Institute, University of California, Merced, 95343, USA
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  • For correspondence: ssindi@ucmerced.edu
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Abstract

Motivation Single-cell RNA sequencing (scRNAseq) technologies allow for measurements of gene expression at a single-cell resolution. This provides researchers with a tremendous advantage for detecting heterogeneity, delineating cellular maps, or identifying rare subpopulations. However, a critical complication remains the low number of single-cell observations due to limitations by the rarity of a subpopulation, tissue degradation, or cost. This absence of sufficient data may cause inaccuracy or irreproducibility of downstream analysis. In this work, we present ACTIVA (Automated Cell-Type-informed Introspective Variational Autoencoder): a novel framework for generating realistic synthetic data using a single-stream adversarial variational autoencoder conditioned with cell-type information. Within a single framework, ACTIVA can generate data representative of the entire population, or specific subpopulations on demand, as opposed to two separate models (such as scGAN and cscGAN). Data generation and augmentation with ACTIVA can enhance scRNAseq pipelines and analysis, such as benchmarking new algorithms, studying the accuracy of classifiers, and detecting marker genes. ACTIVA will facilitate analysis of smaller datasets, potentially reducing the number of patients and animals necessary in initial studies.

Results We train and evaluate models on multiple public scRNAseq datasets. In comparison to GAN-based models (scGAN and cscGAN), we demonstrate that ACTIVA generates cells that are more realistic and harder for classifiers to identify as synthetic, which also have better pair-wise correlations between genes. We show that data augmentation with ACTIVA significantly improves the classification of rare subtypes (more than 45% improvement compared to not augmenting and 4% better than cscGAN) all while reducing training time by an order of magnitude in comparison to both models.

Availability of data and code Links to raw, pre- and post-processed data, source code and tutorials are available at https://github.com/SindiLab.

Supplementary information Supplementary material can be found as a separate file with the same pre-print submission.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/SindiLab/ACTIVA

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 23, 2021.
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ACTIVA: realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders
A. Ali Heydari, Oscar A. Davalos, Lihong Zhao, Katrina K. Hoyer, Suzanne S. Sindi
bioRxiv 2021.01.28.428725; doi: https://doi.org/10.1101/2021.01.28.428725
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ACTIVA: realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders
A. Ali Heydari, Oscar A. Davalos, Lihong Zhao, Katrina K. Hoyer, Suzanne S. Sindi
bioRxiv 2021.01.28.428725; doi: https://doi.org/10.1101/2021.01.28.428725

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