scGAIN: Single Cell RNA-seq Data Imputation using Generative Adversarial Networks

Gunady, Mohamed K.; Kancherla, Jayaram; Bravo, Héctor Corrada; Feizi, Soheil

doi:10.1101/837302

Cited by 10 publications

(7 citation statements)

References 21 publications

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“…The raw scRNA-seq has a much higher zero expression rate than bulk RNA-seq (49.1% versus 14.8%) and shares fewest DEGs with bulk samples (Figure 4A ). After imputation, the number of DEGs is increased toward the DEGs numbers of bulk samples (except the four other deep learning-based methods, AutoImpute ( 48 ), DCA ( 6 ), DeepImpute ( 8 ) and scGAIN ( 49 ), which detect much fewer DEGs than raw data). Especially highlighted by the yellow and green bars in Figure 4A , the AutoImpute and scGAIN detect significantly fewer DEGs and show the poorest agreement with other methods.…”

Section: Resultsmentioning

confidence: 99%

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scIGANs: single-cell RNA-seq imputation using generative adversarial networks

Xu¹,

Zhang

You³

et al. 2020

Nucleic Acids Research

128

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Single-cell RNA-sequencing (scRNA-seq) enables the characterization of transcriptomic profiles at the single-cell resolution with increasingly high throughput. However, it suffers from many sources of technical noises, including insufficient mRNA molecules that lead to excess false zero values, termed dropouts. Computational approaches have been proposed to recover the biologically meaningful expression by borrowing information from similar cells in the observed dataset. However, these methods suffer from oversmoothing and removal of natural cell-to-cell stochasticity in gene expression. Here, we propose the generative adversarial networks (GANs) for scRNA-seq imputation (scIGANs), which uses generated cells rather than observed cells to avoid these limitations and balances the performance between major and rare cell populations. Evaluations based on a variety of simulated and real scRNA-seq datasets show that scIGANs is effective for dropout imputation and enhances various downstream analysis. ScIGANs is robust to small datasets that have very few genes with low expression and/or cell-to-cell variance. ScIGANs works equally well on datasets from different scRNA-seq protocols and is scalable to datasets with over 100 000 cells. We demonstrated in many ways with compelling evidence that scIGANs is not only an application of GANs in omics data but also represents a competing imputation method for the scRNA-seq data.

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Section: Resultsmentioning

confidence: 99%

“…During the submission of this work, another work using GANs for scRNA-seq imputation (referred to as scGAIN) was posted in BioRxiv ( 49 ). We added it in all evaluations and downstream analyses in our work and found that scGAIN is not as good as scIGANs in all evaluations.…”

Section: Discussionmentioning

confidence: 99%

scIGANs: single-cell RNA-seq imputation using generative adversarial networks

Xu¹,

Zhang

You³

et al. 2020

Nucleic Acids Research

128

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“…Low reverse transcription efficiency in single-cell RNA-seq data. [13][14][15][16][17][18][19][20][21] Proteomics data…”

Section: Genomics Datamentioning

confidence: 99%

Deep Learning Methods for Omics Data Imputation

Huang,

Song,

Shen

et al. 2023

Biology

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One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However, the imputation of missing omics data is a non-trivial task. Difficulties mainly come from high dimensionality, non-linear or non-monotonic relationships within features, technical variations introduced by sampling methods, sample heterogeneity, and the non-random missingness mechanism. Several advanced imputation methods, including deep learning-based methods, have been proposed to address these challenges. Due to its capability of modeling complex patterns and relationships in large and high-dimensional datasets, many researchers have adopted deep learning models to impute missing omics data. This review provides a comprehensive overview of the currently available deep learning-based methods for omics imputation from the perspective of deep generative model architectures such as autoencoder, variational autoencoder, generative adversarial networks, and Transformer, with an emphasis on multi-omics data imputation. In addition, this review also discusses the opportunities that deep learning brings and the challenges that it might face in this field.

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“…Many methods and tools are currently available for solving the dropout issues of scRNA-seq data (Pierson and Yau, 2015;Azizi et al, 2017;Lin et al, 2017;Chen and Zhou, 2018;Gong et al, 2018;Huang et al, 2018;Li and Li, 2018;Ronen and Akalin, 2018;van Dijk et al, 2018;Amodio et al, 2019;Arisdakessian et al, 2019;Eraslan et al, 2019;Gunady et al, 2019;Peng et al, 2019;Tracy et al, 2019;Wagner et al, 2019;Badsha et al, 2020;Marouf et al, 2020;Xu et al, 2020). MAGIC recovers dropout events by using diffusion geometry to share similarities across cells (van Dijk et al, 2018).…”

Section: Recover Dropout Events In Single-cell Transcriptome Profilesmentioning

confidence: 99%

Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges

Fan

Zhao

et al. 2021

Front. Genet.

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The rapid development of single-cell technologies allows for dissecting cellular heterogeneity at different omics layers with an unprecedented resolution. In-dep analysis of cellular heterogeneity will boost our understanding of complex biological systems or processes, including cancer, immune system and chronic diseases, thereby providing valuable insights for clinical and translational research. In this review, we will focus on the application of machine learning methods in single-cell multi-omics data analysis. We will start with the pre-processing of single-cell RNA sequencing (scRNA-seq) data, including data imputation, cross-platform batch effect removal, and cell cycle and cell-type identification. Next, we will introduce advanced data analysis tools and methods used for copy number variance estimate, single-cell pseudo-time trajectory analysis, phylogenetic tree inference, cell–cell interaction, regulatory network inference, and integrated analysis of scRNA-seq and spatial transcriptome data. Finally, we will present the latest analyzing challenges, such as multi-omics integration and integrated analysis of scRNA-seq data.

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scGAIN: Single Cell RNA-seq Data Imputation using Generative Adversarial Networks

Cited by 10 publications

References 21 publications

scIGANs: single-cell RNA-seq imputation using generative adversarial networks

scIGANs: single-cell RNA-seq imputation using generative adversarial networks

Deep Learning Methods for Omics Data Imputation

Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges

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