Integrating single-cell RNA-seq datasets with substantial batch effects

single-cell
rna-seq
integration
batch effects
methods
Authors

Karin Hrovatin

Amir Ali Moinfar

Luke Zappia

Alejandro Tejada Lapuerta

Benjamin Lengerich

Manolis Kellis

Fabian J. Theis

Date

November 3, 2023

Links
Citation stats
Abstract

Integration of single cell RNA sequencing (scRNAseq) datasets has become a standard part of the analysis, with conditional variational autoencoders (cVAE) being among the most popular approaches. Increasingly, researchers are asking to map cells across challenging cases such as cross-organs, species, or organoids and primary tissue, as well as different scRNAseq protocols, including single cell and single nuclei. Current computational methods struggle to harmonize datasets with such substantial differences, driven by technical or biological variation. Here, we propose to address these challenges for the popular cVAE based approaches by introducing and comparing a series of regularization constraints. The two commonly used strategies for increasing batch correction in cVAEs, that is Kullback Leibler divergence (KL) regularization strength tuning and adversarial learning, suffer from substantial loss of biological information. Therefore, we adapt, implement, and assess alternative regularization strategies for cVAEs and investigate how they improve batch effect removal or better preserve biological variation, enabling us to propose an optimal cVAE-based integration strategy for complex systems. We show that using a VampPrior instead of the commonly used Gaussian prior not only improves the preservation of biological variation but also unexpectedly batch correction. Moreover, we show that our implementation of cycle consistency loss leads to significantly better biological preservation than adversarial learning implemented in the previously proposed GLUE model. Additionally, we do not recommend relying only on the KL regularization strength tuning for increasing batch correction, as it removes both biological and batch information without discriminating between the two. Based on our findings, we propose a new model that combines VampPrior and cycle-consistency loss. We show that using it for datasets with substantial batch effects improves downstream interpretation of cell states and biological conditions. To ease the use of the newly proposed model, we make it available in the scvitools package as an external model named sysVI. Moreover, in the future, these regularization techniques could be added to other established cVAE based models to improve the integration of datasets with substantial batch effects.

Citation

BibTeX citation:
@misc{hrovatin2023,
  author = {Hrovatin, Karin and Ali Moinfar, Amir and Zappia, Luke and
    Tejada Lapuerta, Alejandro and Lengerich, Benjamin and Kellis,
    Manolis and J. Theis, Fabian},
  title = {Integrating Single-Cell {RNA-seq} Datasets with Substantial
    Batch Effects},
  date = {2023-11-03},
  url = {https://lazappi.id.au/publications/2023-hrovatin-batch-effects/},
  doi = {10.1101/2023.11.03.565463},
  langid = {en},
  abstract = {Integration of single cell RNA sequencing (scRNAseq)
    datasets has become a standard part of the analysis, with
    conditional variational autoencoders (cVAE) being among the most
    popular approaches. Increasingly, researchers are asking to map
    cells across challenging cases such as cross-organs, species, or
    organoids and primary tissue, as well as different scRNAseq
    protocols, including single cell and single nuclei. Current
    computational methods struggle to harmonize datasets with such
    substantial differences, driven by technical or biological
    variation. Here, we propose to address these challenges for the
    popular cVAE based approaches by introducing and comparing a series
    of regularization constraints. The two commonly used strategies for
    increasing batch correction in cVAEs, that is Kullback Leibler
    divergence (KL) regularization strength tuning and adversarial
    learning, suffer from substantial loss of biological information.
    Therefore, we adapt, implement, and assess alternative
    regularization strategies for cVAEs and investigate how they improve
    batch effect removal or better preserve biological variation,
    enabling us to propose an optimal cVAE-based integration strategy
    for complex systems. We show that using a VampPrior instead of the
    commonly used Gaussian prior not only improves the preservation of
    biological variation but also unexpectedly batch correction.
    Moreover, we show that our implementation of cycle consistency loss
    leads to significantly better biological preservation than
    adversarial learning implemented in the previously proposed GLUE
    model. Additionally, we do not recommend relying only on the KL
    regularization strength tuning for increasing batch correction, as
    it removes both biological and batch information without
    discriminating between the two. Based on our findings, we propose a
    new model that combines VampPrior and cycle-consistency loss. We
    show that using it for datasets with substantial batch effects
    improves downstream interpretation of cell states and biological
    conditions. To ease the use of the newly proposed model, we make it
    available in the scvitools package as an external model named sysVI.
    Moreover, in the future, these regularization techniques could be
    added to other established cVAE based models to improve the
    integration of datasets with substantial batch effects.}
}
For attribution, please cite this work as:
Hrovatin, Karin, Amir Ali Moinfar, Luke Zappia, Alejandro Tejada Lapuerta, Benjamin Lengerich, Manolis Kellis, and Fabian J. Theis. 2023. “Integrating Single-Cell RNA-Seq Datasets with Substantial Batch Effects.” bioRxiv. https://doi.org/10.1101/2023.11.03.565463.