scMDC: Single-cell Multi-omics Data Clustering Analysis
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scMDC: single-cell multi-omics data clustering analysis
- an end-to-end deep model that explicitly characterizes different data sources
- jointly learns latent features of deep learning for clustering analysis
multimodal sequencing technologies
- CITE-seq: cellular Indexing of Transcriptomes and Epitopes by Sequencing
- REAP-seq: RNA expression and protein sequencing assay
- scATAT-seq: the development of single-cell approaches for the assay of the transposase accessible chromatin sequencing
some multi-omics single-cell technologies have been developed to jointly profile chromatin accessibility and gene expression within a single cell, such as
- SNARE-seq
- 10X Single-cell Multipme ATAC + Gene Expression
in the multimodal data, the biological information provided by different modalities is complementary
- ADT and miRNA data
Clustering analysis
- Tscan: PCA on the scRNA-seq and then Gaussian Mixture Model (GMM)
- Seurat: kNN graph based on the Euclidean distance in PCA space. Then employs the Louvain/Leiden algorithm to iteratively group cells together by optimizing modularity.
- SC3: spectral clustering based on the distance matrices derived from the Euclidean, Pearson and Spearman metrics, respectively. Compute a consensus matrix. Finally, use hierarchical clustering
clustering analysis of CITE-seq data
- scDCC: single cell deep constrained clustering framework
- BREM-SC: hierarchical Bayesian mixture model
similarity matrix-based clustering cannot explicitly consider the dropout events in scRNA-seq data
Another line of research: focuses on learning a joint embedding of different modalities
zero-inflated negative binomial (ZINB)
ZINB can effectively characterize scRNA-seq data and improve the representation learning and clustering results