scGMAI: a Gaussian mixture model for clustering single-cell RNA-Seq data based on deep autoencoder

Yu, Bin; Chen, Chen; Qi, Ren; Zheng, Ruiqing; Skillman-Lawrence, Patrick J; Wang, Xiaolin; Ma, Anjun; Gu, Hanming

doi:10.1093/bib/bbaa316

Cited by 35 publications

(23 citation statements)

References 38 publications

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“…However, they are usually computationally intensive and limited in capturing non-linearity in scRNA-seq data. To better address this issue, deep learning approaches have been developed for scRNA-seq data imputation and denoising [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] . Based on an idea similar to regression imputation [50] , i.e.…”

Section: Applications Of Deep Learning In Scrna-seq Data Analysismentioning

confidence: 99%

Application of Deep Learning on Single-Cell RNA Sequencing Data Analysis: A Review

Brendel

Bai

et al. 2022

Genomics, Proteomics &Amp; Bioinformatics

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Single-cell RNA sequencing (scRNA-seq) has become a routinely used technique to quantify the gene expression profile of thousands of single cells simultaneously. Analysis of scRNA-seq data plays an important role in the study of cell states and phenotypes, and has helped elucidate biological processes, such as those occurring during the development of complex organisms, and improved our understanding of disease states, such as cancer, diabetes, and coronavirus disease 2019 (COVID-19). Deep learning, a recent advance of artificial intelligence that has been used to address many problems involving large datasets, has also emerged as a promising tool for scRNA-seq data analysis, as it has a capacity to extract informative and compact features from noisy, heterogeneous, and high-dimensional scRNA-seq data to improve downstream analysis. The present review aims at surveying recently developed deep learning techniques in scRNA-seq data analysis, identifying key steps within the scRNA-seq data analysis pipeline that have been advanced by deep learning, and explaining the benefits of deep learning over more conventional analytic tools. Finally, we summarize the challenges in current deep learning approaches faced within scRNA-seq data and discuss potential directions for improvements in deep learning algorithms for scRNA-seq data analysis.

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Section: Applications Of Deep Learning In Scrna-seq Data Analysismentioning

confidence: 99%

Application of Deep Learning on Single-Cell RNA Sequencing Data Analysis: A Review

Brendel

Bai

et al. 2022

Genomics, Proteomics &Amp; Bioinformatics

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“…At present, a variety of effective clustering methods have been proposed, such as the unsupervised clustering algorithm Seurat proposed by Butler [27] that combines dimensionality reduction algorithm with graph segmentation method to cluster scRNA-Seq data. scGMAI [28] combines autoencoder network and Gaussian mixture clustering algorithm to achieve accurate clustering. Consensus clustering SC3 [20] obtains the similarity matrix by calculating the cell-to-cell distance of the original data matrix, and then the eigenvectors of PCA and Laplace transformed matrices are clustered by K-means to construct the consensus matrix.…”

Section: Introductionmentioning

confidence: 99%

scBKAP: A Clustering Model for Single-Cell RNA-Seq Data Based on Bisecting K-Means

Wang

Gao

et al. 2023

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Advances in single-cell RNA sequencing (scRNA-Seq) technologies allow researchers to analyze the genome-wide transcription profile and to solve biological problems at the individual-cell resolution. However, existing clustering methods on scRNA-Seq suffer from high dropout rate and curse of dimensionality in the data. Here, we propose a novel pipeline, scBKAP, the cornerstone of which is a single-cell bisecting K-means clustering method based on an autoencoder network and a dimensionality reduction model MPDR. Specially, scBKAP utilizes an autoencoder network to reconstruct gene expression values from scRNA-Seq data to alleviate the dropout issue, and the MPDR model composed of the M3Drop feature selection algorithm and the PHATE dimensionality reduction algorithm to reduce the dimensions of reconstructed data. The dimensionality-reduced data are then fed into the bisecting K-means clustering algorithm to identify the clusters of cells. Comprehensive experiments demonstrate scBKAP's superior performance over nine state-of-the-art single-cell clustering methods on 21 public scRNA-Seq datasets and simulated datasets. The source codes and datasets are available at https://github.com/YuBinLab-QUST/scBKAP/ and https://doi.org/10.24433/CO.4592131.v1.

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“…These methods typically apply dimension reduction techniques such as PCA 14 , t-SNE 15 and UMAP 16 to obtain a lower-dimensional representation of the data. Deep-learning-based approaches, including scDeepCluster 17 , scAIDE 18 , SCA 19 , AAE-SC 20 , and scGMAI 21 , often use autoencoders to select important features and to project the data onto a low-dimensional latent space. Next, these clustering methods partition the cells using established clustering algorithms (e.g., k-means, spectral clustering, etc.).…”

Section: Introductionmentioning

confidence: 99%

scCAN: single-cell clustering using autoencoder and network fusion

Tran

Nguyen

et al. 2022

Sci Rep

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Unsupervised clustering of single-cell RNA sequencing data (scRNA-seq) is important because it allows us to identify putative cell types. However, the large number of cells (up to millions), the high-dimensionality of the data (tens of thousands of genes), and the high dropout rates all present substantial challenges in single-cell analysis. Here we introduce a new method, named single-cell Clustering using Autoencoder and Network fusion (scCAN), that can overcome these challenges to accurately segregate different cell types in large and sparse scRNA-seq data. In an extensive analysis using 28 real scRNA-seq datasets (more than three million cells) and 243 simulated datasets, we validate that scCAN: (1) correctly estimates the number of true cell types, (2) accurately segregates cells of different types, (3) is robust against dropouts, and (4) is fast and memory efficient. We also compare scCAN with CIDR, SEURAT3, Monocle3, SHARP, and SCANPY. scCAN outperforms these state-of-the-art methods in terms of both accuracy and scalability. The scCAN package is available at https://cran.r-project.org/package=scCAN. Data and R scripts are available at http://sccan.tinnguyen-lab.com/

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scGMAI: a Gaussian mixture model for clustering single-cell RNA-Seq data based on deep autoencoder

Cited by 35 publications

References 38 publications

Application of Deep Learning on Single-Cell RNA Sequencing Data Analysis: A Review

Application of Deep Learning on Single-Cell RNA Sequencing Data Analysis: A Review

scBKAP: A Clustering Model for Single-Cell RNA-Seq Data Based on Bisecting K-Means

scCAN: single-cell clustering using autoencoder and network fusion

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