scloop: embedding and clustering analysis of single-cell Hi-C

Release
Date: Nov 06, 2023

scloop is a Python package for analyzing single-cell Hi-C data. It provides a unified interface for existing single-cell Hi-C embedding and clustering methods and allows for easy comparison of different methods and testing of new methods.

Typical usage will take a single-cell cooler file and a cell metadata file as input:

scloop embed --dset human_pfc \  # dataset name
             --scool pfc.scool \  # single-cell cooler file
             --reference pfc_ref \  # cell metadata (e.g celltype, batch, depth, etc)
             --methods scHiCluster higashi VaDE  # embedding methods to run

The following sections go into more detail about how to prepare the input data for a new dataset and some of the most common arguments that can be used to customize the analysis.

Getting started:

Find optimal methods and settings:

List of all methods and options:

Specifying Embedding Methods and Preprocessing Steps
- List of Embedding Methods
- List of Preprocessing Steps

Indices and tables

Citing

To cite scloop please use the following publication:

Bibliography

Bollobas01: Xinjun Li, Fan Feng, Wai Yan Leung and Jie Liu, “scHiCTools: a computational toolbox for analyzing single cell Hi-C data”, PLOS Computational Biology 17(5): e1008978. https://doi.org/10.1371/journal.pcbi.1008978