cna

Covarying neighborhood analysis is a method for finding structure in- and conducting association analysis with multi-sample single-cell datasets. cna does not require a pre-specified transcriptional structure such as a clustering of the cells in the dataset. It aims instead to flexibly identify differences of all kinds between samples. cna is fast, does not require parameter tuning, produces measures of statistical significance for its association analyses, and allows for covariate correction.

cna is built on top of scanpy and offers a scanpy-like interface for ease of use.

If you prefer R, there is an R implementation maintained separately by Ilya Korsunsky. (Though the R implementation may occasionally lag behind this implementation as updates are made.)

installation

To use cna, you can either install it directly from the Python Package Index by running, e.g.,

pip install cna

or if you'd like to manipulate the source code you can clone this repository and add it to your PYTHONPATH.

demo

Take a look at our tutorial to see how to get started with a small synthetic data set.

talk

You can learn more about cna by watching our talk at the Broad Institute's Models, Inference, and Algorithms seminar, which is preceded by a primer by Dylan Kotliar on nearest-neighbor graphs.

notices

• January 20, 2022: It has come to our attention that a bug introduced on July 16, 2021 caused cna to behave incorrectly for users with anndata version 0.7.2 or later, possibly resulting in false positive or false negative results. This bug was fixed in cna version 0.1.4. We strongly recommend that any users with anndata version 0.7.2 or later either re-clone CNA or run pip install --upgrade cna and re-run all analyses that may have been affected.

citation

If you use cna, please cite

[Reshef, Rumker], et al., Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics. [...] contributed equally