singleCellHaystack

This repository contains a Python implementation of singleCellHaystack (version >= 1.0.0).

This package is currently in beta. The most important functionality in the R package works, but some features are not yet available. Here is a (probably imcomplete) list of missing features. Some will be added in the future.

• weights.advanced.Q (formerly known as use.advanced.sampling).
• seeding method for calculating grid points.
• Hierarchical clustering method for cluster_genes.

Installation

You can install singleCellHaystack from PyPI:

pip install singleCellHaystack

You can install singleCellHaystack from GitHub with:

pip install git+http://github.com/ddiez/singleCellHaystack-py

Support for conda installation will be added in the future.

Example

import scanpy as sc
import singleCellHaystack as hs

adata = sc.read_h5ad("data.h5ad")

[... process adata object ...]

res = hs.haystack(adata, basis="pca")
res.top_features(n=10)

References

• Our manuscript describing the updated, more generally applicable version of singleCellHaystack including this Python implementation was published in Scientific Reports.

• Our manuscript describing the original implementation of singleCellHaystack for R (version 0.3.4) was published in Nature Communications.

If you use singleCellHaystack in your research please cite our work using:

Vandenbon A, Diez D (2023). “A universal tool for predicting differentially active features in single-cell and spatial genomics data.” Scientific Reports, 13(1), 11830. doi:10.1038/s41598-023-38965-2.