ttgump / scDCC
scDCC: Single Cell Deep Constrained Clustering
☆41Updated 6 months ago
Alternatives and similar repositories for scDCC:
Users that are interested in scDCC are comparing it to the libraries listed below
- ☆14Updated 3 years ago
- Pytorch implementation of scDeepCluster for Single Cell RNA-seq data☆31Updated 6 months ago
- scDeepCluster for Single Cell RNA-seq data☆99Updated 6 months ago
- An unsupervised scRNA-seq analysis workflow with graph attention networks☆24Updated last year
- A generative topic model that facilitates integrative analysis of large-scale single-cell RNA sequencing data.☆49Updated 3 years ago
- Deconvoluting Spatial Transcriptomics Data through Graph-based Artificial Intelligence☆37Updated 2 years ago
- Iterative transfer learning with neural network improves clustering and cell type classification in single-cell RNA-seq analysis☆54Updated 3 years ago
- Adversarial domain translation networks for integrating large-scale atlas-level single-cell datasets☆30Updated last year
- Deep soft K-means clustering with self-training for single cell RNA sequence data☆24Updated 4 years ago
- Cell clustering for spatial transcriptomics data with graph neural network☆58Updated last year
- ☆17Updated 3 years ago
- using graph convolutional neural network and spaital transcriptomics data to infer cell-cell interactions☆56Updated 3 years ago
- scGMAI: a Gaussian mixture model for clustering single-cell RNA-seq data based on deep autoencoder☆16Updated 3 years ago
- Enhancing spatial transcriptomics data by predicting the expression of unmeasured genes from a dissociated scRNA-seq data☆33Updated 6 months ago
- ☆57Updated last year
- Simultaneous deep generative modeling and clustering of single cell genomic data☆29Updated 2 years ago
- Deep joint-leaning single-cell multi-omics model☆16Updated 2 years ago
- BERMUDA (Batch Effect ReMoval Using Deep Autoencoders) is a novel transfer-learning-based method for batch correction in scRNA-seq data.☆29Updated 5 years ago
- ☆17Updated 3 years ago
- ☆26Updated 2 years ago
- ☆18Updated 5 months ago
- Semi-supervised adversarial neural networks for classification of single cell transcriptomics data☆73Updated last month
- Single cell joint embedding and modality prediction with autoencoder☆9Updated 2 years ago
- Deep learning model for single-cell inference of multi-omic profiles from a single input modality.☆39Updated last year
- ☆28Updated 3 years ago
- ☆101Updated 10 months ago
- A unified approach for integrating spatial and single-cell transcriptomics data by leveraging deep generative models☆58Updated 8 months ago
- ☆28Updated 4 months ago
- single-cell graph attentional clustering☆12Updated 2 years ago
- a unified single-cell data integration framework by optimal transport☆33Updated 3 months ago