hictkpy

---

Downloads
Documentation
License
CI
CodeQL
Fuzzy Testing

---

Python bindings for hictk, a blazing fast toolkit to work with .hic and .cool files.

If you are looking for the R API, checkout the hictkR repository.

Installing hictkpy

hictkpy can be installed in various ways. The simplest method is using pip: pip install 'hictkpy[all]'.

Refer to Installation for alternative methods.

Using hictkpy

import hictkpy

path_to_clr = "file.mcool"  # "file.hic"

clr = hictkpy.File(path_to_clr, 100_000)
sel = clr.fetch("chr1")

df = sel.to_df()     # Get interactions as a pandas.DataFrame
m1 = sel.to_numpy()  # Get interactions as a numpy matrix
m2 = sel.to_csr()    # Get interactions as a scipy.sparse.csr_matrix

For more detailed examples refer to the Quickstart section in the documentation.

The complete documentation for the hictkpy API is available here.

Citing

If you use hictkpy in your research, please cite the following publication:

Roberto Rossini, Jonas Paulsen, hictk: blazing fast toolkit to work with .hic and .cool files
Bioinformatics, Volume 40, Issue 7, July 2024, btae408, https://doi.org/10.1093/bioinformatics/btae408

BibTex

@article{hictk,
    author = {Rossini, Roberto and Paulsen, Jonas},
    title = "{hictk: blazing fast toolkit to work with .hic and .cool files}",
    journal = {Bioinformatics},
    volume = {40},
    number = {7},
    pages = {btae408},
    year = {2024},
    month = {06},
    issn = {1367-4811},
    doi = {10.1093/bioinformatics/btae408},
    url = {https://doi.org/10.1093/bioinformatics/btae408},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/40/7/btae408/58385157/btae408.pdf},
}