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scVGAE: A Novel Approach using ZINB-Based Variational Graph Autoencoder for Single-Cell RNA-Seq Imputation

Yoshitaka Inoue

TL;DR

Droplet dropout in scRNA-seq creates zeros that hinder downstream analysis. The authors propose scVGAE, a variational graph autoencoder that integrates a Graph Convolutional Network encoder with a Zero-Inflated Negative Binomial loss to jointly impute missing values and preserve cell-cell topology. The approach includes a dual loss (reconstruction and ZINB) and a graph-based imputation pipeline, demonstrating superior clustering performance across 14 real datasets and robust ablation support for each component. This method advances imputation accuracy and clustering reliability in single-cell data analyses and provides a scalable, open-source framework for graph-based, distribution-aware scRNA-seq imputation.

Abstract

Single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to study individual cellular distinctions and uncover unique cell characteristics. However, a significant technical challenge in scRNA-seq analysis is the occurrence of "dropout" events, where certain gene expressions cannot be detected. This issue is particularly pronounced in genes with low or sparse expression levels, impacting the precision and interpretability of the obtained data. To address this challenge, various imputation methods have been implemented to predict such missing values, aiming to enhance the analysis's accuracy and usefulness. A prevailing hypothesis posits that scRNA-seq data conforms to a zero-inflated negative binomial (ZINB) distribution. Consequently, methods have been developed to model the data according to this distribution. Recent trends in scRNA-seq analysis have seen the emergence of deep learning approaches. Some techniques, such as the variational autoencoder, incorporate the ZINB distribution as a model loss function. Graph-based methods like Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) have also gained attention as deep learning methodologies for scRNA-seq analysis. This study introduces scVGAE, an innovative approach integrating GCN into a variational autoencoder framework while utilizing a ZINB loss function. This integration presents a promising avenue for effectively addressing dropout events in scRNA-seq data, thereby enhancing the accuracy and reliability of downstream analyses. scVGAE outperforms other methods in cell clustering, with the best performance in 11 out of 14 datasets. Ablation study shows all components of scVGAE are necessary. scVGAE is implemented in Python and downloadable at https://github.com/inoue0426/scVGAE.

scVGAE: A Novel Approach using ZINB-Based Variational Graph Autoencoder for Single-Cell RNA-Seq Imputation

TL;DR

Droplet dropout in scRNA-seq creates zeros that hinder downstream analysis. The authors propose scVGAE, a variational graph autoencoder that integrates a Graph Convolutional Network encoder with a Zero-Inflated Negative Binomial loss to jointly impute missing values and preserve cell-cell topology. The approach includes a dual loss (reconstruction and ZINB) and a graph-based imputation pipeline, demonstrating superior clustering performance across 14 real datasets and robust ablation support for each component. This method advances imputation accuracy and clustering reliability in single-cell data analyses and provides a scalable, open-source framework for graph-based, distribution-aware scRNA-seq imputation.

Abstract

Single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to study individual cellular distinctions and uncover unique cell characteristics. However, a significant technical challenge in scRNA-seq analysis is the occurrence of "dropout" events, where certain gene expressions cannot be detected. This issue is particularly pronounced in genes with low or sparse expression levels, impacting the precision and interpretability of the obtained data. To address this challenge, various imputation methods have been implemented to predict such missing values, aiming to enhance the analysis's accuracy and usefulness. A prevailing hypothesis posits that scRNA-seq data conforms to a zero-inflated negative binomial (ZINB) distribution. Consequently, methods have been developed to model the data according to this distribution. Recent trends in scRNA-seq analysis have seen the emergence of deep learning approaches. Some techniques, such as the variational autoencoder, incorporate the ZINB distribution as a model loss function. Graph-based methods like Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) have also gained attention as deep learning methodologies for scRNA-seq analysis. This study introduces scVGAE, an innovative approach integrating GCN into a variational autoencoder framework while utilizing a ZINB loss function. This integration presents a promising avenue for effectively addressing dropout events in scRNA-seq data, thereby enhancing the accuracy and reliability of downstream analyses. scVGAE outperforms other methods in cell clustering, with the best performance in 11 out of 14 datasets. Ablation study shows all components of scVGAE are necessary. scVGAE is implemented in Python and downloadable at https://github.com/inoue0426/scVGAE.
Paper Structure (21 sections, 11 equations, 3 figures, 3 tables)

This paper contains 21 sections, 11 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Statistical Methods
  4. Affinity-based Methods
  5. Deep Learning Methods
  6. Deep Graph Methods
  7. Methods
  8. Data Preprocessing
  9. Adjacent matrix
  10. Zero-Inflated Negative Binomial Distribution
  11. ZINB-Based Graph Autoencoder
  12. Joint optimization
  13. Experiments
  14. scRNA-seq data
  15. Baseline
  16. ...and 6 more sections

Figures (3)

  • Figure 1: An overview of scVGAE: A cell-cell network is constructed from input data representing gene expression. The network informs an affinity matrix via a kernel function that measures cell similarity, and also the input data is concurrently transformed into a feature matrix for the Graph Convolution layer. Outputs of the graph convolution, capturing mean, dispersion, and dropout values, contribute to computing the ZINBLoss. The mean output also feeds into a fully connected layer to reconstruct the original matrix. Reconstruction loss is assessed by augmenting the reconstructed matrix with cell-wise and gene-wise normalized matrices, facilitating comparison against the original matrix. Through iterative optimization, the refined reconstructed matrix is an imputed matrix for cell clustering analysis.
  • Figure 2: Visualization of scRNA Imputation Results Using 7 Methods with UMAP
  • Figure 3: Effect of Data Size on Processing Time: Comparative Evaluation of Multiple Models. The x-axis displays dataset name and dimensions, while the y-axis represents speed in seconds.