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"Cluster Independent Algorithm for the identification of RAre cell types."

Project description

(Cluster Independent Algorithm for the identification of RAre cell types)

Python implementation of the CIARA algorithm that integrates into scanpy's analysis with the AnnData format.

The package can be installed via pip:

python -m pip install ciara_python

Note: The package only works on UNIX / MacOS operating systems, not on Windows systems, due to the copy-on-write multiprocessing setup used.


The functions are designed to work with scanpy's AnnData objects. For an interactive tutorial check out the Human Gastrula IPython Notebook which is also part of this repository.

First you load your dataset as a scanpy object and after normal preprocessing calculate the knn-network:

import scanpy as sc

pbmc = sc.datasets.pbmc3k()
sc.pp.filter_cells(pbmc, min_genes=50)
sc.pp.filter_genes(pbmc, min_cells=10)


The CIARA package contains the two main functions get_full_background() and ciara() which should be imported via:

from ciara_python import get_ background_full, ciara

Afterwards, the background genes get marked by running the get_full_background() function on your scanpy dataset. This adds the boolean column 'CIARA_background' to your pbmc.var AnnData slot, where relevant background genes are marked.

get_background_full(pbmc, threshold=1, n_cells=2, n_cells_high=5)

Finally, the ciara() function is run on the dataset. This adds the column 'CIARA_p_value' to your pbmc.var object, where the calculated p_values for each of the previously marked background genes are stored.

ciara(pbmc, n_cores=4, p_value=0.001, odds_ratio=2, approximation=True, local_region=1)

We can then extract the top markers or rare cells (lowest CIARA_p_value) and plot them onto the UMAP:

from matplotlib.pyplot import rc_context

top_markers = pbmc.var.nsmallest(4, ["CIARA_p_value"],)

with rc_context({'figure.figsize': (3, 3)}):, color=top_markers.index.tolist())

UMAP of top 4 rare cell type markers for PBMCs

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