It is a challenging task to integrate scRNA-seq and scATAC-seq data obtained from different batches. Existing methods tend to use a pre-defined gene activity matrix to convert the scATAC-seq data into scRNA-seq
Integrating scRNA-seq and scATAC-seq with inter-type attention heterogeneous graph neural networksdeep learningheterogeneous graphsmodality predictionsingle-cell multi-omics integrationSingle-cell multi-omics techniques, which enable the simultaneous measurement of multiple modalities such as RNA gene expression...
Similarly, effective multi-modal clustering techniques for parallel scRNA-seq and scATAC-seq data have demonstrated the importance of integrating multi-omic data for comprehensive cancer analysis [37]. Prior to this study, significant research gaps existed in the field of breast cancer diagnosis and ...
Across modalities (e.g., scRNAseq and spatial transcriptomics data, scMethylation, or scATAC-seq) Once multiple datasets are integrated, the package provides functionality for further data exploration, analysis, and visualization. Users can:
it becomes possible to capture longer dynamical processes or transient cellular states (Manno et al.2018). While the focus of this review centers on scRNAseq, complementary techniques such as scATAC-seq could provide valuable insight into biophysical properties and phenotypes with slower fluctuations....
Adversarial domain translation networks for integrating large-scale atlas-level single-cell datasets - YangLabHKUST/Portal
Across modalities (e.g., scRNAseq and spatial transcriptomics data, scMethylation, or scATAC-seq) Once multiple datasets are integrated, the package provides functionality for further data exploration, analysis, and visualization. Users can: