An implementation of [singleCellHaystack](https://github.com/alexisvdb/singleCellHaystack) in python.
Reason this release was yanked:
Bug in randomization.
Project description
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 asuse.advanced.sampling
).seeding
method for calculating grid points.
Installation
You can install singleCellHaystack from pypi:
pip install singleCellHaystack
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, coord="pca")
res["results"]
References
-
Our manuscript describing the updated, more generally applicable version of
singleCellHaystack
inclusing this python implementation is available on bioRxiv. -
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 (2020). “A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data.” Nature Communications, 11(1), 4318. doi:10.1038/s41467-020-17900-3.
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