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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
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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 cost or rarity of subpopulation. This absence of suicient 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. 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. Under the same conditions, ACTIVA trains up to 17 times faster than the GAN-based state-of-the-art model, scGAN (2.2 hours compared to 39.5 hours on Brain Small) while performing better or comparable in our quantitative and qualitative evaluations. We show that augmenting rare-populations with ACTIVA can significantly increase the classification accuracy of the rare population (more than 45% improvement in our rarest test case).

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

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 January 30, 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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