 Methodology article
 Open access
 Published:
A deep adversarial variational autoencoder model for dimensionality reduction in singlecell RNA sequencing analysis
BMC Bioinformatics volume 21, Article number: 64 (2020)
Abstract
Background
Singlecell RNA sequencing (scRNAseq) is an emerging technology that can assess the function of an individual cell and celltocell variability at the single cell level in an unbiased manner. Dimensionality reduction is an essential first step in downstream analysis of the scRNAseq data. However, the scRNAseq data are challenging for traditional methods due to their high dimensional measurements as well as an abundance of dropout events (that is, zero expression measurements).
Results
To overcome these difficulties, we propose DRA (Dimensionality Reduction with Adversarial variational autoencoder), a datadriven approach to fulfill the task of dimensionality reduction. DRA leverages a novel adversarial variational autoencoderbased framework, a variant of generative adversarial networks. DRA is wellsuited for unsupervised learning tasks for the scRNAseq data, where labels for cell types are costly and often impossible to acquire. Compared with existing methods, DRA is able to provide a more accurate low dimensional representation of the scRNAseq data. We illustrate this by utilizing DRA for clustering of scRNAseq data.
Conclusions
Our results indicate that DRA significantly enhances clustering performance over stateoftheart methods.
Background
Dimensionality reduction is a universal preliminary step prior to downstream analysis of scRNAseq data such as clustering and cell type identification [1]. Dimension reduction is crucial for analysis of scRNAseq data because the high dimensional scRNAseq measurements for a large number of genes and cells may contain high level of technical and biological noise [2]. Its objective is to project data points from the high dimensional gene expression measurements to a low dimensional latent space so that the data become more tractable and noise can be reduced. In particular, a special characteristic of scRNAseq data is that it contains an abundance of zero expression measurements that could be either due to biological or technical causes. This phenomenon of zero measurements due to technical reasons is often referred to as “dropout” events where an expressed RNA molecule is not detected. The identification of distinct cellular states or subtypes is a key application of scRNAseq data. However, some methods may not work well because of the existence of dropout events.
The most commonly used method is principal component analysis (PCA), which transforms the observations onto the latent space by defining linear combinations of the original data points with successively largest variance (that is, principal components) [3]. However, PCA is under the assumptions of linear dimensions and approximately normally distributed data, which may not be suitable for scRNAseq data [4]. Another linear technique is factor analysis, which is similar to PCA but aims to model correlations instead of covariances by describing variability among correlated variables [5]. Based on the factor analysis framework, a recent stateoftheart method, ZeroInflated Factor Analysis (ZIFA), accounts for the presence of dropouts by adding a zeroinflation modulation layer [6]. A limitation of ZIFA, however, is that the zeroinflation model may not be proper for all datasets [4]. Recently, deep learning frameworks, such as Singlecell Variational Inference (scVI) [7] and Sparse Autoencoder for Unsupervised Clustering, Imputation, and Embedding (SAUCIE) [8], utilizes the autoencoder which processes the data through narrower and narrower hidden layers and gradually reduces the dimensionality of the data. It should be noted that scVI and SAUCIE take advantage of parallel and scalable features in deep neural networks [7, 8].
Visualization of high dimensional data is an important problem in scRNAseq data analysis since it allows us to extract useful information such as distinct cell types. In order to facilitate the process of visualization, dimensionality reduction is normally utilized to reduce the dimension of the data, from tensofthousands (that is, the number of genes) to 2 or 3 [2]. Tdistributed stochastic neighbor embedding (tSNE) is a popular method for visualizing scRNAseq data [9,10,11], but not recommended as a dimensionality reduction method due to its weaknesses such as curse of intrinsic dimensionality and the infeasibility of handling general dimensionality reduction tasks for a dimensionality higher than three [12]. On the other hand, a recentlydeveloped nonlinear technique called Uniform Manifold Approximation and Projection (UMAP) [13] is claimed to improve visualization of scRNAseq data compared with tSNE [14].
Generative Adversarial Networks (GANs) [15] are an emerging technique that has attracted much attention in machine learning research because of its massive potential to sample from the true underlying data distribution in a wide variety of applications, such as videos, images, languages, and other fields [16,17,18]. The GAN framework consists of two components including a generative model G and a discriminative model D [15]. In practice, these two neural networks, G and D, are trained simultaneously. The generative model G is trained to generate fake samples from the latent variable z, while the discriminative model D inputs both real and fake samples and distinguishes whether its input is real or not. The discriminative model D estimates higher probability if it considers a sample is more likely to be real. In the meantime, G is trained to maximize the probability of D making a wrong decision. Concurrently, both G and D play against each other to accomplish their objectives such that the GAN framework creates a minmax adversarial game between G and D.
Recently, a variant of the GAN framework called an Adversarial AutoEncoder [19] was proposed to be a probabilistic autoencoder that leverages the GAN concept to transform an autoencoder into a GANbased structure. The architecture of an Adversarial AutoEncoder is composed of two components, a standard autoencoder and a GAN network. The encoder in an Adversarial AutoEncoder is also the generative model of the GAN network. The GANbased training ensures that the latent space conforms to some prior latent distribution. The Adversarial AutoEncoder models have been applied to identify and generate new compounds for anticancer therapy by using biological and chemical data [20, 21].
The main contributions of this work are as follows: In this work, we propose a novel GANbased architecture, which we refer to as DRA (Dimensionality Reduction with Adversarial variational autoencoder), for dimensionality reduction in scRNAseq analysis. We directly compare the performance of DRA to dimensionality reduction methods implemented in widely used software, including the PCA, ZIFA, scVI, SAUCIE, tSNE, and UMAP. Across several scRNAseq datasets, we demonstrate that our DRA approach leads to better clustering performance.
Results
Overview of DRA
DRA represents a deep adversarial variational autoencoderbased framework, which combines the concepts of two deep learning models including Adversarial AutoEncoder [19] and Variational AutoEncoder [22] (see Methods). Figure 1 provides an overview of the model structure in DRA, which models scRNAseq data through a zeroinflated negative binomial (ZINB) distribution structure [7, 23] in a GAN framework. DRA is a novel structure of an Adversarial Variational AutoEncoder with Dual Matching (AVAEDM), where both the generator and discriminator examine the input scRNAseq data. As shown in Fig. 1, an additional discriminator D2 tries to differentiate between real scRNAseq data and the reconstructed scRNAseq data from the decoder. While DRA manages to match the latent space distribution with a selected prior, it concurrently tries to match the distribution of the reconstructed samples with that of the underlying real scRNAseq data. This approach refers to dual distribution matching.
In accordance with the Wasserstein distancebased scheme [24], DRA further integrates the AVAEDM structure with the Bhattacharyya distance [25]. The Bhattacharyya distance BD(p, q) is an alternative metric to measure the similarity between two probability distributions, p and q distributions, over the same domain X. The Bhattacharyya distance is defined as
Therefore, our new Bhattacharyya distancebased scheme can be formalized as the following minimax objective:
where p_{data} and p(z) are the data distribution and the model distribution, respectively.
In summary, DRA has the following five key advantages: (1) DRA matches the distribution of the reconstructed samples with the underlying real scRNAseq data. (2) DRA matches the latent space distribution with a chosen prior. (3) DRA provides a ZINB distribution, which is a commonlyaccepted distributional structure for gene expression. (4) DRA is more stable for GAN training with the Bhattacharyya distancebased scheme. (5) DRA accounts for parallel and scalable features in a deep neural network framework (see Methods).
Real data analysis
To evaluate the performance of our approach for dimension reduction, we compared our DRA framework with other stateoftheart methods, including the PCA [3], ZIFA [6], scVI [7], SAUCIE [8], tSNE [12], and UMAP [13]. The dimensionality reduction was studied in 2 latent dimensions (K = 2), 10 latent dimensions (K = 10), and 20 latent dimensions (K = 20) for these methods.
In these experiments, we employed five datasets (Table 1), including the Zeisel3 k [1], Macoskco44 k [10], Zheng68 k [26], Zheng73 k [26], and Rosenberg156 k [27] datasets as described in the Methods section, where the cell types with ground truth are available.
We evaluated the effectiveness of these methods with impacts on the clustering performance of the Kmeans clustering algorithm with the latent dimensions of K = 2, 10, and 20. We assessed the clustering performance using the normalized mutual information (NMI) scores [28]. First, we applied the Kmeans clustering algorithm using the latent variables from the various algorithms of dimensionality reduction as an input and generated the predicted clustering labels. Then, we utilized NMI scores to measure the cluster purity between the predicted clustering labels and the cell types with ground truth in a given dataset. Based on the NMI scores, we compared our DRA framework with other algorithms of dimensionality reduction (including the PCA, ZIFA, scVI, SAUCIE, tSNE, and UMAP methods).
As shown in Table 2, our DRA framework performed maximally or comparably in all cases. The best NMI scores (with 10 and 20 latent dimensions) for the five datasets were all based on the DRA method (Table 2(b), K = 10; Table 2(c), K = 20). With 2 latent dimensions, the UMAP method performed marginally better than the DRA method using the Rosenberg156 k dataset (Table 2(a), K = 2). In addition, the best NMI scores (with 2 latent dimensions) for the Zheng73 k, Zheng68 k, Macosko44 k, and Zeisel3 k datasets were all based on the DRA method (Table 2(a), K = 2).
Furthermore, we compared our DRA framework with other variants of the GAN framework, including the AVAEDM structure with the Wasserstein distance and AVAE structure. Our DRA framework adopts the AVAEDM structure with Bhattacharyya distance. The DRA method improved the performance compared to the AVAEDM with the Wasserstein distance and AVAE methods (Additional file 1: Table S1), indicating the advantage of the Bhattacharyya distance and dual matching architecture. In addition, the experimental results of the DRA method with various batch sizes were shown in Additional file 1: Table S2.
Our analysis indicated that our DRA framework is wellsuited for largescale scRNAseq datasets. The hyperparameters for various datasets of DRA were shown in Table 3.
Data visualization
Moreover, we performed twodimensional (2D) visualization of the clustering results for the DRA, PCA, ZIFA, scVI, SAUCIE, tSNE, and UMAP methods using the Zeisel3 k (Fig. 2), Zheng73 k (Fig. 3), Macoskco44 k (Additional file 1: Figure S1), Zheng68 k (Additional file 1: Figure S2), and Rosenberg156 k (Additional file 1: Figure S3) datasets, respectively. We also carried out the twostep approach of combining DRA with tSNE (see Methods). We illustrated the 2D plots on the Macoskco44 k (Additional file 1: Figure S1) and Rosenberg156 k datasets (Additional file 1: Figure S3) only by using the top ten cell types in terms of the number of cells. Due to the large number of distinct cell types for the Macoskco44 k and Rosenberg156 k datasets (39 and 73, respectively), it may not be obvious to distinguish in 2D visualization by using all cell types.
Discussion
In this work, we specifically addressed the problem of the identification of distinct cellular subtypes in terms of dimensionality reduction in scRNAseq data. We developed a conceptually different class of the GAN framework, DRA, which is an AVAEDMbased method for robust estimation of cell types and is applicable to largescale scRNAseq datasets. We further demonstrated the utility of DRA in an application to five real scRNAseq datasets assuming 2, 10, and 20 latent dimensions. We also compared the performance of DRA to stateoftheart methods and intriguingly showed the improvement offered by DRA over widely used approaches, including PCA, ZIFA, scVI, SAUCIE, tSNE, and UMAP.
Furthermore, our experiments demonstrated that our DRA framework, which is based on the AVAEDM model with the Bhattacharyya distance, is a promising novel approach. All in all, our DRA method had a better performance than stateoftheart methods for all five datasets, indicating that DRA is scalable for largescale scRNAseq datasets.
Although the tSNE method is a wideused approach for data visualization of scRNAseq data, it has been suggested that tSNE may not be feasible for dimensionality reduction [12]. In line with this finding in the previous study, the clustering performances of tSNE in some datasets were worse than those of other algorithms such as scVI and DRA in this study (Table 2). To overcome this weakness, some studies [10] utilized a technique of using tSNE for data visualization after performing other dimensionality reduction methods. In accordance with this technique, we adapted the twostep approach of using DRA with tSNE. Interestingly, we found that the twostep approach combines the advantages of both DRA and tSNE methods and had an improved result that cells from relevant cell types appeared to be adjacent to each other, for example, as shown in Fig. 2 (a), (f), and (h) for the Zeisel3 k dataset. Likewise, the improvement for data visualization is presented for other four datasets (Fig. 3, Additional file 1: Figure S1, Additional file 1: Figure S2, and Additional file 1: Figure S3). Therefore, our results demonstrate that DRA is an effective 2D visualization tool for scRNAseq data.
Conclusions
In summary, we developed DRA, a novel AVAEDMbased framework, for scRNAseq data analysis and applications in dimension reduction and clustering. Compared systematically with other stateoftheart methods, DRA achieves higher cluster purity for clustering tasks and is generally suitable for different scale and diversity of scRNAseq datasets. We anticipate that scalable tools such as DRA will be a complementary approach to existing methods and will be in great demand due to an everincreased need for handling largescale scRNAseq data. In future work, we will verify if DRA could also be beneficial for other forms of downstream analysis, such as lineage estimation.
Methods
Generative adversarial networks
The idea of GANs is to train two neural networks (the generator G and the discriminator D) concurrently to establish a minmax adversarial game between them. The generator G(z) gradually learns to transform samples z from a prior distribution p(z) into the data space, while the discriminator D(x) is trained to distinguish a point x in the data space between the data points sampled from the actual data distribution (that is, true samples) and the data points produced by the generator (that is, fake samples). It is assumed that G(z) is trained to fully confuse the discriminator with its generated samples by using the gradient of D(x) with respect to x to modify its parameters. This scheme can be formalized as the following type of minimax objective [15]:
where p_{data} is the data distribution and p(z) is the model distribution.
The generator G and the discriminator D can be both modeled as fully connected neural networks and then are trained by backpropagation using a suitable optimizer. In our experiments, we used adaptive moment estimation (Adam) [29], which is an extension to stochastic gradient descent.
Adversarial AutoEncoder
A variant of GAN models called an Adversarial AutoEncoder [19] is a probabilistic autoencoder that transforms an autoencoder into a generative model by using the GAN framework. The structure of an Adversarial AutoEncoder is composed of two components, a standard autoencoder and an adversarial network. The encoder is also the generator of the adversarial network. The idea of the Adversarial AutoEncoder is that both the adversarial network and the autoencoder are trained simultaneously to perform inference. While the encoder (that is, the generator) is trained to fool the discriminator to believe that the latent vector is generated from the true prior distribution, the discriminator is trained to distinguish between the sampled vector and the latent vector of the encoder at the same time. The adversarial training ensures that the latent space matches with some prior latent distribution.
Variational AutoEncoder
A variant of autoencoder models called Variational Autoencoder [22] is a generative model, which estimates the probability density function of the training data. An input x is run through an encoder, which generates parameters of a distribution Q(z  x). Then, a latent vector z is sampled from Q(z  x). Finally, the decoder decodes z into an output, which should be similar to the input. This scheme can be trained by maximizing the following objective with gradientbased methods:
where D_{KL} is the Kullback–Leibler divergence, and p_{model}(x  z) is viewed as the decoder.
Adversarial Variational AutoEncoder
Figure 4 shows the structure of an Adversarial Variational AutoEncoder (AVAE), which adopts the structures of Adversarial Autoencoder [19] and Variational Autoencoder [22]. Let x be the input of the scRNAseq expression level (M cells x N genes) and z be the latent code vector of an autoencoder, which consists of a deep encoder and a deep decoder. Let p(z) be the prior distribution imposed on the latent code vector, q(zx) be an encoding distribution and p(xz) be the decoding distribution. The deep encoder provides the mean and covariance of Gaussian for the variational distribution q(zx) [22]. The autoencoder gradually learns to reconstruct the input x of the scRNAseq data to be as realistic as possible by minimizing the reconstruction error. Note that the encoder of the AVAE is also the generator of the GAN framework. The encoder is trained to fool the discriminator of the GAN framework such that the latent code vector q(z) stems from the true prior distribution p(z). Meanwhile, the discriminator is trained to distinguish between the sampled vector of p(z) and the latent code vector q(z) of the encoder (that is, the generator) at the same time. Thus, the GAN framework guides q(z) to match p(z). Eventually, AVAE is able to learn an unsupervised representation of the probability distribution of the scRNAseq data. In our work, we used the normal Gaussian distribution N(0, I) for the prior distribution p(z). In addition, the generator was updated twice for each discriminator update in this work. Note that in the training phase, labels for cell types are not provided and the entire framework is unsupervised.
Adversarial Variational AutoEncoder with dual matching (AVAEDM)
In this paper, we explore AVAEs in a different structure by altering the network architecture of an AVAE (Fig. 4). Figure 1 shows the novel structure of an Adversarial Variational AutoEncoder with Dual Matching (AVAEDM) employed in this work. Unlike a conventional AVAE, both the generator and discriminator observe the input scRNAseq data in an AVAEDM. In additional to the original AVAE structure (Fig. 4), we add another discriminator D2 that attempts to distinguish between real scRNAseq data and the decoder’s output (that is, the reconstructed scRNAseq data). As in the original AVAE structure, the goal of this AVAEDM architecture remains the same in the unsupervised setting (that is, labels for cell types are not provided during training). This architecture ensures that the distribution of the reconstructed samples match that of the underlying real scRNAseq. At the same time, the latent space distribution is matched with a chosen prior, leading to dual distribution matching.
Since the Wasserstein distance have been shown to be more stable for GAN training, the AVAEDM can be combined with the Wasserstein distance [30]. The AVAEDM can also be explored with the Wasserstein distance with gradient penalty (GP) [24]. Wasserstein distance W(p, q), also known as the earth mover’s distance, is informally defined as the minimum cost of transiting mass between the probability distribution p and the probability distribution q. The Wasserstein distancebased scheme can be formalized as the following minimax objective [24]:
Furthermore, we proposed to integrate the AVAEDM with the Bhattacharyya distance [25], which is yet another metric to measure the similarity of two probability distributions. The Bhattacharyya distance BD(p, q) between p and q distributions over the same domain X is defined as
Then, our new objective is
where p_{data} and p(z) are once again the data distribution and the model distribution, respectively.
Our DRA approach mainly encompasses the AVAEDMbased algorithm with Bhattacharyya distance. In DRA, we employed ZINB conditional likelihood for p(xz) to reconstruct the decoder’s output for the scRNAseq data [7, 23]. To accordingly handle dropout events (that is, zero expression measurements), DRA models the scRNAseq expression level x following a ZINB distribution, which appears to provide a good fit for the scRNAseq data [7, 23].
In this study, the encoder, decoder, and discriminator are designed from 1, 2, 3, or 4 layers of a fully connected neural network with 8, 16, 32, 64, 128, 256, 512, or 1024 nodes each. The best hyperparameter set from numerous possibilities was chosen from a grid search that maximized clustering performance in the testing data sets. Dropout regularization was used for all neural networks. The activation functions between two hidden layers are all leaky rectified linear (Leaky ReLu) activation functions. Deep learning models have high variance and never give the same answer when running multiple times. In order to achieve reproducible results, we used the Python and TensorFlow commands such as np.random.seed(0) and tf.set_random_seed(0) to obtain a single number.
Benchmarking
For the benchmarking task, we employed several stateoftheart methods as described below. We employed the ZIFA method [6] with the block algorithm (that is, function block) using default parameters, which is implemented in the ZIFA python package (Version 0.1) and is available at https://github.com/epierson9/ZIFA. The outcome of ZIFA is an N x K matrix corresponding to a lowdimensional projection in the latent space with the number of samples N and the number of latent dimensions K, where we chose K = 2, 10, and 20.
Furthermore, we used the PCA method [3] from Scikitlearn, a machine learning library, using default parameters and logdata. We also employed the tSNE method [12] from Scikitlearn, a machine learning library, using default parameters (for example, perplexity parameter of 30). In addition, we utilized the UMAP method [13], a manifold learning technique, using default parameters and logdata. The embedding layer was 2 10, and 20 latent dimensions.
Moreover, we utilized scVI [7], which is based on the variational autoencoder [22] and conditional distributions with a ZINB form [31]. Based on the implications described in scVI [7], we used one layer with 128 nodes in the encoder and one layer with 128 nodes in the decoder. We also used two layers with 128 nodes in the encoder and two layers with 128 nodes in the decoder. The embedding layer was 2, 10, and 20 latent dimensions. The ADAM optimizer was used with learning rate 0.001. The hyperparameters were selected through best clustering performance in the testing data.
We also employed SAUCIE [8], which is based on the autoencoder [32]. SAUCIE consists of an encoder, an embedding layer, and then a decoder. Based on the indications reported in SAUCIE [8], we used three layers with 512, 256, and 128 nodes in the encoder and symmetrically three layers with 128, 256, and 512 nodes in the decoder. We also used three layers with 256, 128, and 64 nodes in the encoder and symmetrically three layers with 64, 128, and 256 nodes in the decoder. The embedding layer was 2 10, and 20 latent dimensions. The ADAM optimizer was used with learning rate 0.001. The hyperparameters were chosen via best clustering performance in the testing data sets.
Datasets
Table 1 shows the list of the five scRNAseq datasets used in this study. All datasets were preprocessed to obtain 720 highest variance genes across the cells [33]. It is assumed that genes with highest variance relative to their mean expression are as a result of biological effects instead of technical noise [4]. The transformation used in the counts matrix data C was log_{2} (1 + C).
As shown in Table 1, the Zeisel3 k dataset [1] consists of 3005 cells in the somatosensory cortex and hippocampal region from the mouse brain. The Zeisel3 k dataset has the ground truth labels of 7 distinct cell types such as pyramidal cells, oligodendrocytes, mural cells, interneurons, astrocytes, ependymal cells, and endothelial cells in the brain.
Moreover, the Macoskco44 k dataset [10] is comprised of cells in the mouse retina region and chiefly consists of retinal cell types such as amacrine cells, bipolar cells, horizontal cells, photoreceptor cells, and retinal ganglion cells. In addition, the Zheng68 k dataset [26] contains fresh peripheral blood mononuclear cells in a healthy human and principally involves major cell types of peripheral blood mononuclear cells such as T cells, NK cells, B cells, and myeloid cells. Furthermore, the Zheng73 k dataset [26] consists of fluorescenceactivated cell sorting cells in a healthy human and primarily incorporates T cells, NK cells, and B cells. Finally, the Rosenberg156 k dataset [27] consists of cells from mouse brains and spinal cords and mainly contains neuronal cell types such as cerebellar granule cells, mitral cells, and tufted cells.
Performance evaluation
In order to evaluate the quality of lowdimensional representation from dimension reduction, we applied the Kmeans clustering algorithm to the lowdimensional representations of the dimension reduction methods (including the DRA, PCA, scVI, SAUCIE, ZIFA, tSNE, and UMAP methods as described previously) and compared the clustering results to the cell types with ground truth labels, where we set the number of clusters to the number of cell types. Then, we employed NMI scores [28] to assess the performance. Assume that X is the predicted clustering results and Y is the cell types with ground truth labels, NMI is calculated as follows:
where MI is the mutual entropy between X and Y, and H is the Shannon entropy.
Data visualization
After we performed the dimensionality reduction task using our DRA framework, we leveraged the lowdimensional view of the data for visualization. The objective of the visualization task is to identify cell types in an unlabelled dataset and then display them in 2D space. Note that all our datasets had a training set and a testing set with an 80% training and 20% testing split from the original dataset. First, we trained our DRA model to perform the clustering task in 2 latent dimensions (K = 2) using the training set. Next, we obtained a twodimensional embedding (K = 2) of the scRNAseq data by projecting the testing set with the trained DRA model. This latent (K = 2) estimated by our DRA model represents two dimensional coordinates for each input data point, which was then utilized to perform a 2D plot. Similarly, we implemented 2D plots for the PCA, ZIFA, scVI, SAUCIE, tSNE, and UMAP methods after performing the clustering task in 2 latent dimensions (K = 2), respectively.
In addition, we performed data visualization by a twostep approach, which combines our DRA method with the tSNE algorithm. In the first step, we performed the clustering task in 10 latent dimensions (K = 10) using our DRA model. In the second step, we used the latent (K = 10) estimated in the first step as input to the tSNE algorithm and generated a twodimensional embedding (K = 2) of the scRNAseq data. This latent (K = 2) estimated by the tSNE algorithm represents two dimensional coordinates for each input data point, which was then utilized to perform a 2D plot.
Availability of data and materials
The datasets and source code that support the findings of this study are available in https://github.com/eugenelin1/DRA.
Abbreviations
 2D:

Twodimensional
 AVAEDM:

Adversarial Variational AutoEncoder with Dual Matching
 DRA:

Dimensionality Reduction with Adversarial variational autoencoder
 GANs:

Generative Adversarial Networks
 NMI:

Normalized mutual information
 PCA:

Principal component analysis
 SAUCIE:

Sparse Autoencoder for Unsupervised Clustering, Imputation, and Embedding
 scRNAseq:

singlecell RNA sequencing
 scVI:

Singlecell Variational Inference
 tSNE:

tdistributed stochastic neighbor embedding
 UMAP:

Uniform Manifold Approximation and Projection
 ZIFA:

ZeroInflated Factor Analysis
 ZINB:

Zeroinflated negative binomial
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This work was partially supported by NSF CCF award 1703403, NSF Career award (grant 1651236), and NIH award number R01HG008164.
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EL developed the model, performed the experiments, and wrote the manuscript. EL and SM implemented the software. SK conceived the study. All authors read and approved the final manuscript.
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Lin, E., Mukherjee, S. & Kannan, S. A deep adversarial variational autoencoder model for dimensionality reduction in singlecell RNA sequencing analysis. BMC Bioinformatics 21, 64 (2020). https://doi.org/10.1186/s1285902034015
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DOI: https://doi.org/10.1186/s1285902034015