Su-informatics-lab / DSTGLinks
Deconvoluting Spatial Transcriptomics Data through Graph-based Artificial Intelligence
☆39Updated 3 years ago
Alternatives and similar repositories for DSTG
Users that are interested in DSTG are comparing it to the libraries listed below
Sorting:
- ☆45Updated 3 months ago
- scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics☆41Updated 3 years ago
- Reproducing result from the paper☆34Updated 4 years ago
- Adversarial domain translation networks for integrating large-scale atlas-level single-cell datasets☆33Updated 2 years ago
- End-to-end analysis of spatial multi-omics data☆95Updated last week
- spatial transcriptome, single cell☆71Updated 2 years ago
- Cell-type Annotation for Single-cell Transcriptomics using Deep Learning with a Weighted Graph Neural Network☆106Updated 2 years ago
- ☆44Updated 3 years ago
- ☆50Updated 8 months ago
- ☆61Updated last year
- ☆56Updated last year
- ☆60Updated 2 years ago
- Deep learning model for single-cell inference of multi-omic profiles from a single input modality.☆44Updated 2 years ago
- Enhancing spatial transcriptomics data by predicting the expression of unmeasured genes from a dissociated scRNA-seq data☆38Updated last year
- SIMBA: SIngle-cell eMBedding Along with features☆19Updated last year
- Additional code and analysis from the single-cell integration benchmarking project☆68Updated 2 years ago
- overview of spatial datasets☆76Updated last year
- Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space☆79Updated 2 months ago
- Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data☆72Updated 9 months ago
- Single-cell biological network inference using a heterogeneous graph transformer☆76Updated 8 months ago
- SpatialGlue is a novel deep learning methods for spatial multi-omics data integration.☆71Updated last year
- Probabilistic Alignment of Spatial Transcriptomics Experiments☆96Updated 8 months ago
- BERMUDA (Batch Effect ReMoval Using Deep Autoencoders) is a novel transfer-learning-based method for batch correction in scRNA-seq data.☆31Updated 5 years ago
- Awesome list of tools and methods to perform spatial transcriptomic data analysis.☆46Updated 4 years ago
- Python package for analysis of multiomic single cell RNA-seq and ATAC-seq.☆66Updated 4 months ago
- ☆33Updated 7 months ago
- domain adaptation of spatial and single-cell transcriptome☆28Updated last year
- SIMBA: SIngle-cell eMBedding Along with features☆63Updated last year
- An unsupervised approach for the integrative analysis of single-cell multi-omics data☆31Updated 4 years ago
- ACTIONet single-cell analysis framework☆43Updated last year