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Cited by 135Single-cell and spatial transcriptomicsGene expression and cancer classificationCell Image Analysis Techniques

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Clustering of single-cell multi-omics data with a multimodal deep learning method

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Lin, Xiang, Tian, Tian, Wei, Zhi, Hakonarson, Hakon·2022·Nature Communications
DOI 10.1038/s41467-022-35031-9PMC PMC9748135
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Abstract

Single-cell multimodal sequencing technologies are developed to simultaneously profile different modalities of data in the same cell. It provides a unique opportunity to jointly analyze multimodal data at the single-cell level for the identification of distinct cell types. A correct clustering result is essential for the downstream complex biological functional studies. However, combining different data sources for clustering analysis of single-cell multimodal data remains a statistical and computational challenge. Here, we develop a novel multimodal deep learning method, scMDC, for single-cell multi-omics data clustering analysis. scMDC is an end-to-end deep model that explicitly characterizes different data sources and jointly learns latent features of deep embedding for clustering analysis. Extensive simulation and real-data experiments reveal that scMDC outperforms existing single-cell single-modal and multimodal clustering methods on different single-cell multimodal datasets. The linear scalability of running time makes scMDC a promising method for analyzing large multimodal datasets.

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Fig. 1 The architecture of scMDC. scMDC has one encoder for the concatenated data and two decoders for each modal in the multimodal data (a). It can be used for clustering CITE-seq data and 10x Single-Cell Multiome ATAC + Gene Expression (SMAGE-seq) data. The spiral symbols indicate the artificial noises added to the data. For multi-batch datasets, scMDC will work in a conditional autoencoder manner. A one-hot batch vector B (in dimension b) will be concatenated to the input feature of the encoder (with raw feature dimension, m) and the decoders (with latent feature dimension, z). This is designed for batch effect correction. scMDC learns a latent representation Z (in dimension z) of data on which different modalities are integrated. A deep K-means algorithm and a KLD loss are implemented on Z. Based on the clustering results, scMDC employs an ACE model36 to detect markers in different clusters (b). Then, pathway analyses can be conducted based on the gene ranks learned by ACE (c).
Fig. 1 The architecture of scMDC. scMDC has one encoder for the concatenated data and two decoders for each modal in the multimodal data (a). It can be used for clustering CITE-seq data and 10x Single-Cell Multiome ATAC + Gene Expression (SMAGE-seq) data. The spiral symbols indicate the artificial noises added to the data. For multi-batch datasets, scMDC will work in a conditional autoencoder manner. A one-hot batch vector B (in dimension b) will be concatenated to the input feature of the encoder (with raw feature dimension, m) and the decoders (with latent feature dimension, z). This is designed for batch effect correction. scMDC learns a latent representation Z (in dimension z) of data on which different modalities are integrated. A deep K-means algorithm and a KLD loss are implemented on Z. Based on the clustering results, scMDC employs an ACE model36 to detect markers in different clusters (b). Then, pathway analyses can be conducted based on the gene ranks learned by ACE (c).CC BY · PubMed Central
Fig. 2 Clustering performance of scMDC and the competing methods on different CITE-seq datasets. All the methods are tested on seven one-batch datasets (a, n = 7) and two two-batch datasets (b, n = 2). In panels (a) and (b), clustering performance is illustrated in a two-dimensional manner with ARI as the Y axis and NMI as the X axis. Circles stand for the results of the multi-omics methods and triangles stand for the results of the single-omics methods. The differences between the performance of scMDC and the competing methods are shown in boxplots (c, n = 9). Each boxplot shows the minimum, first quartile (Q1), median, third quartile (Q3), and maximum of data. The minimum and maximum are the smallest data point that is equal to or greater than Q1 −1.5*IQR and the largest data point that is equal to or less than Q3 + 1.5*IQR, respectively. Each data point (a difference of performance in a dataset) is shown by a dot. We also summarized the performance of each method by showing the averaged ranks (d, n = 9). Each data point (a rank of a method in a dataset) is shown by a dot and the standard errors are shown by the error bars. In panels (c) and (d), clustering performance is evaluated by AMI, NMI, and ARI. Source data is provided as a Source Data file.
Fig. 2 Clustering performance of scMDC and the competing methods on different CITE-seq datasets. All the methods are tested on seven one-batch datasets (a, n = 7) and two two-batch datasets (b, n = 2). In panels (a) and (b), clustering performance is illustrated in a two-dimensional manner with ARI as the Y axis and NMI as the X axis. Circles stand for the results of the multi-omics methods and triangles stand for the results of the single-omics methods. The differences between the performance of scMDC and the competing methods are shown in boxplots (c, n = 9). Each boxplot shows the minimum, first quartile (Q1), median, third quartile (Q3), and maximum of data. The minimum and maximum are the smallest data point that is equal to or greater than Q1 −1.5*IQR and the largest data point that is equal to or less than Q3 + 1.5*IQR, respectively. Each data point (a difference of performance in a dataset) is shown by a dot. We also summarized the performance of each method by showing the averaged ranks (d, n = 9). Each data point (a rank of a method in a dataset) is shown by a dot and the standard errors are shown by the error bars. In panels (c) and (d), clustering performance is evaluated by AMI, NMI, and ARI. Source data is provided as a Source Data file.CC BY · PubMed Central
Fig. 3 Clustering performance of scMDC and the competing methods on different SMAGE-seq datasets. All the methods are tested on three one-batch datasets (a, n = 3) and one two-batch dataset (b, n = 1). In panels (a) and (b), clustering performance is illustrated in a two-dimensional manner with ARI as the Y-axis and NMI as the X-axis. Circles stand for the results of the multi-omics methods and triangles stand for the results of the single-omics methods. The differences between the performance of scMDC and the competing methods are shown in boxplots (c, n = 4). Each boxplot shows the minimum, first quartile (Q1), median, third quartile (Q3), and maximum of data. The minimum and maximum are the smallest data point that is equal to or greater than Q1 − 1.5 * IQR and the largest data point that is equal to or less than Q3 + 1.5 * IQR, respectively. Each data point (a difference in performance in a dataset) is shown by a dot. We also summarized the performance of each method by showing the averaged ranks (d, n = 4). Each data point (a rank of a method in a dataset) is shown by a dot and the standard errors are shown by the error bars. In panels (c) and (d), clustering performance is evaluated by AMI, NMI, and ARI. Source data is provided as a Source Data file.
Fig. 3 Clustering performance of scMDC and the competing methods on different SMAGE-seq datasets. All the methods are tested on three one-batch datasets (a, n = 3) and one two-batch dataset (b, n = 1). In panels (a) and (b), clustering performance is illustrated in a two-dimensional manner with ARI as the Y-axis and NMI as the X-axis. Circles stand for the results of the multi-omics methods and triangles stand for the results of the single-omics methods. The differences between the performance of scMDC and the competing methods are shown in boxplots (c, n = 4). Each boxplot shows the minimum, first quartile (Q1), median, third quartile (Q3), and maximum of data. The minimum and maximum are the smallest data point that is equal to or greater than Q1 − 1.5 * IQR and the largest data point that is equal to or less than Q3 + 1.5 * IQR, respectively. Each data point (a difference in performance in a dataset) is shown by a dot. We also summarized the performance of each method by showing the averaged ranks (d, n = 4). Each data point (a rank of a method in a dataset) is shown by a dot and the standard errors are shown by the error bars. In panels (c) and (d), clustering performance is evaluated by AMI, NMI, and ARI. Source data is provided as a Source Data file.CC BY · PubMed Central
Fig. 4 Clustering performance of scMDC and the competing methods on the simulation datasets. The first simulation experiment is to test the clustering performance of scMDC with low (a), medium (b), and high (c) clustering signals. The second simulation experiment is to test the clustering performance of scMDC with low (d), medium (e), and high (f) dropout rates. Since scMDC, Seurat, and TotalVI can correct the batch effect, we also test their clustering performance on a multi-batch simulation dataset (g). In panels (a–f), bars stand for the mean values, dots stand for the data points, and error bars stand for the standard errors. We generate ten replicates for each experimental setting (n = 10). The differences between the averaged performance of scMDC and the competing methods over all simulation datasets are shown in boxplots (h, n = 6). Each boxplot shows the minimum, first quartile (Q1), median, third quartile (Q3), and maximum of data. The minimum and maximum are the smallest data point that is equal to or greater than Q1 − 1.5 * IQR and the largest data point that is equal to or less than Q3 + 1.5 * IQR, respectively. Each data point (a difference of averaged performance in a dataset) is shown by a dot. We also summarized the performance of each method by showing the averaged ranks (i, n = 7). Each data point (an average rank of a method in a setting) is shown by a dot and the standard errors are shown by the error bars. In all panels, the clustering performance is evaluated by AMI, NMI, and ARI. Source data is provided as a Source Data file.
Fig. 4 Clustering performance of scMDC and the competing methods on the simulation datasets. The first simulation experiment is to test the clustering performance of scMDC with low (a), medium (b), and high (c) clustering signals. The second simulation experiment is to test the clustering performance of scMDC with low (d), medium (e), and high (f) dropout rates. Since scMDC, Seurat, and TotalVI can correct the batch effect, we also test their clustering performance on a multi-batch simulation dataset (g). In panels (a–f), bars stand for the mean values, dots stand for the data points, and error bars stand for the standard errors. We generate ten replicates for each experimental setting (n = 10). The differences between the averaged performance of scMDC and the competing methods over all simulation datasets are shown in boxplots (h, n = 6). Each boxplot shows the minimum, first quartile (Q1), median, third quartile (Q3), and maximum of data. The minimum and maximum are the smallest data point that is equal to or greater than Q1 − 1.5 * IQR and the largest data point that is equal to or less than Q3 + 1.5 * IQR, respectively. Each data point (a difference of averaged performance in a dataset) is shown by a dot. We also summarized the performance of each method by showing the averaged ranks (i, n = 7). Each data point (an average rank of a method in a setting) is shown by a dot and the standard errors are shown by the error bars. In all panels, the clustering performance is evaluated by AMI, NMI, and ARI. Source data is provided as a Source Data file.CC BY · PubMed Central
Fig. 5 Low-dimension representation of scMDC and the competing methods on the BMNC dataset. The t-SNE plots of the embeddings from scMDC (a) and four competing methods including IDEC (b), SCVIS (c), TotalVI (d), and Seurat (e) are shown in different rows. The first three columns show the expression pattern of genes LYZ, CD8A, and NKG7. The last column shows the true labels (cell types) on the latent space learned from each method.
Fig. 5 Low-dimension representation of scMDC and the competing methods on the BMNC dataset. The t-SNE plots of the embeddings from scMDC (a) and four competing methods including IDEC (b), SCVIS (c), TotalVI (d), and Seurat (e) are shown in different rows. The first three columns show the expression pattern of genes LYZ, CD8A, and NKG7. The last column shows the true labels (cell types) on the latent space learned from each method.CC BY · PubMed Central
Fig. 6 Low-dimension representation of scMDC and the competing methods on the SLN111 dataset. The t-SNE plots of the embeddings from scMDC (a) and four competing methods including IDEC (b), SCVIS (c), TotalVI (d), and Seurat (e) are shown in different rows. The three columns show the predicted labels, batch IDs, and true labels on the latent space learned from each method.
Fig. 6 Low-dimension representation of scMDC and the competing methods on the SLN111 dataset. The t-SNE plots of the embeddings from scMDC (a) and four competing methods including IDEC (b), SCVIS (c), TotalVI (d), and Seurat (e) are shown in different rows. The three columns show the predicted labels, batch IDs, and true labels on the latent space learned from each method.CC BY · PubMed Central
Fig. 7 Clustering performance of scMDC and the variant models on the multimodal datasets. scMDC, scMDC-RNA, scMDC-ADT, and scMDC-Concat are tested on the CITE-seq data (a, n = 9) and scMDC, scMDC-RNA, scMDC-ATAC, and scMDC-Concat are tested on the SMAGE-seq data (b, n = 4). In panels a and b, clustering performance is illustrated in a two-dimensional manner with ARI as the Y-axis and NMI as the X-axis. Circles stand for the results of multi-batch datasets and triangles stand for the results of single-batch datasets. The differences between the performance of scMDC and the competing methods in CITE-seq data (left, n = 9) and SMAGE-seq data (right, n = 4) are shown in boxplots (c). Each boxplot shows the minimum, first quartile (Q1), median, third quartile (Q3), and maximum of data. The minimum and maximum are the smallest data point that is equal to or greater than Q1 − 1.5 * IQR and the largest data point that is equal to or less than Q3 + 1.5 * IQR, respectively. The comparisons between scMDC and scDeepCluster are shown in a dotplot (d, n = 13). The paired performance for each dataset from the two methods are connected by lines. The differences between the performance of scMDC and the scDeepCluster are shown in boxplots and violin plots (e, n = 13). The definition of boxplots is the same as that in panel (c). In panels (d) and (e), the results of CITE-seq data are shown by circles, and the results of SMAGE-seq data are shown by triangles. Source data is provided as a Source Data file.
Fig. 7 Clustering performance of scMDC and the variant models on the multimodal datasets. scMDC, scMDC-RNA, scMDC-ADT, and scMDC-Concat are tested on the CITE-seq data (a, n = 9) and scMDC, scMDC-RNA, scMDC-ATAC, and scMDC-Concat are tested on the SMAGE-seq data (b, n = 4). In panels a and b, clustering performance is illustrated in a two-dimensional manner with ARI as the Y-axis and NMI as the X-axis. Circles stand for the results of multi-batch datasets and triangles stand for the results of single-batch datasets. The differences between the performance of scMDC and the competing methods in CITE-seq data (left, n = 9) and SMAGE-seq data (right, n = 4) are shown in boxplots (c). Each boxplot shows the minimum, first quartile (Q1), median, third quartile (Q3), and maximum of data. The minimum and maximum are the smallest data point that is equal to or greater than Q1 − 1.5 * IQR and the largest data point that is equal to or less than Q3 + 1.5 * IQR, respectively. The comparisons between scMDC and scDeepCluster are shown in a dotplot (d, n = 13). The paired performance for each dataset from the two methods are connected by lines. The differences between the performance of scMDC and the scDeepCluster are shown in boxplots and violin plots (e, n = 13). The definition of boxplots is the same as that in panel (c). In panels (d) and (e), the results of CITE-seq data are shown by circles, and the results of SMAGE-seq data are shown by triangles. Source data is provided as a Source Data file.CC BY · PubMed Central
Fig. 8 Downstream analyses of scMDC in BMNC dataset. Differential expression analyses (a) and Hallmark gene set enrichment analyses (b) are conducted for four large cell clusters in the BMNC dataset based on the clustering result of scMDC. In panel (a), the dot size shows the expression percentage of a gene in a cell type, and colors show the average expression level of a gene in a cell type with blue as low and red as high.
Fig. 8 Downstream analyses of scMDC in BMNC dataset. Differential expression analyses (a) and Hallmark gene set enrichment analyses (b) are conducted for four large cell clusters in the BMNC dataset based on the clustering result of scMDC. In panel (a), the dot size shows the expression percentage of a gene in a cell type, and colors show the average expression level of a gene in a cell type with blue as low and red as high.CC BY · PubMed Central
Fig. 9 Hyperparameter tuning and running time testing of scMDC. This experiment is conducted on six real datasets (n = 6). Phi (a) and Gamma (b) are set ranging from 0 to 1 and 0.001 to 100, respectively. We test the running time of scMDC by increasing the cell numbers in the simulated datasets from 1000 to 100,000 (c, n = 7). Source data is provided as a Source Data file.
Fig. 9 Hyperparameter tuning and running time testing of scMDC. This experiment is conducted on six real datasets (n = 6). Phi (a) and Gamma (b) are set ranging from 0 to 1 and 0.001 to 100, respectively. We test the running time of scMDC by increasing the cell numbers in the simulated datasets from 1000 to 100,000 (c, n = 7). Source data is provided as a Source Data file.CC BY · PubMed Central

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Metadata

Authors
Lin, X., Tian, T., Wei, Z., & Hakonarson, H.
Affiliations
  • New Jersey Institute of Technology (US)
  • Children's Hospital of Philadelphia (US)
Corresponding author
Wei, Zhi
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2022-12-13
Volume
13
Issue
1
Pages
7705-7705
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-022-35031-9
PMC
PMC9748135
OpenAlex
W4311232381
Cited by
135
References
56
Citations by year
2026: 45 · 2025: 43 · 2024: 39 · 2023: 7 · 2022: 1
FWCI
7.35
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (UL1TR003017)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
Keywords
single-cell multi-omics, multimodal deep learning, cell type annotation, single-cell sequencing
MeSH terms
Deep Learning, Multiomics, Computer Simulation, Cluster Analysis, Single-Cell Analysis

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