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Multiscale and integrative single-cell Hi-C analysis with Higashi
Higashi is a computational framework for scHi-C analysis with the following features:
- Higashi represents the scHi-C dataset as a hypergraph
- Each cell and each genomic bin are represented as the cell node and the genomic bin node.
- Each non-zero entry in the single-cell contact map is modeled as a hyperedge.
- The read count for each chromatin interaction is used as the attribute of the hyperedge.
- Higashi uses a hypergraph neural network to unveil high-order interaction patterns within this constructed hypergraph.
- Higashi can produce the embeddings for the scHi-C for downstream analysis.
- Higashi can impute single-cell Hi-C contact maps, enabling detailed characterization of 3D genome features such as TAD-like domain boundaries and A/B compartment scores at single-cell resolution.
There are three major parts of Higashi:
- Higashi-main, which is the core part of Higashi. It takes the input sparse scHi-C dataset and produces embeddings vectors as well as imputed contact maps.
- Higashi-analysis, which enables multi-scale and integrative analysis of the imputed contact maps.
- Higashi-vis, which is the visualization tool we developed for visualizing Higashi results or general single cell chromatin structure datasets.
Follow the links below to get started:
- Installing Higashi
- Usage of Higashi-main
- Single cell TAD calling
- Single cell A/B compartment calling
- Usage of Higashi-vis
Higashi is constantly being updated, see change log for the updating history
Please contact [email protected] or raise an issue in the github repo with any questions about installation or usage.
Higashi ~ ~ Wiki