ma-compbio/Fast-Higashi
single-cell Hi-C, scHi-C, Hi-C, 3D genome, nuclear organization, tensor decomposition
This tool helps computational biologists analyze how DNA folds inside individual cells. It takes single-cell Hi-C contact maps, which show how different parts of the genome interact, and reveals key patterns in 3D genome organization for each cell. The output helps researchers understand cellular differences and shared structural features across a population of cells. This is for scientists studying nuclear organization and gene regulation.
No commits in the last 6 months.
Use this if you need to rapidly analyze large datasets of single-cell Hi-C contact maps to understand cell-specific 3D genome structures and identify common interaction patterns.
Not ideal if you are working with bulk Hi-C data or primarily interested in 1D genomic features rather than 3D chromosomal architecture.
Stars
20
Forks
6
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Sep 12, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ma-compbio/Fast-Higashi"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
scverse/scanpy
Single-cell analysis in Python. Scales to >100M cells.
scverse/scvi-tools
Deep probabilistic analysis of single-cell and spatial omics data
Teichlab/celltypist
A tool for semi-automatic cell type classification
theislab/scarches
Reference mapping for single-cell genomics
Teichlab/cellhint
A tool for semi-automatic cell type harmonization and integration