Skip to main content

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

Project description

CIARA
(Cluster Independent Algorithm for the identification of markers 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.

Tutorial

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)
sc.pp.log1p(pbmc)

sc.pp.pca(pbmc)
sc.pp.neighbors(pbmc)

The CIARA package contains the two main functions get_background_full() 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_background_full() 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_low=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, 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

sc.tl.umap(pbmc)

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

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

UMAP of top 4 rare cell type markers for PBMCs

R package

Link to R package:

https://github.com/ScialdoneLab/CIARA/

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ciara_python-1.0.4.tar.gz (4.0 kB view details)

Uploaded Source

Built Distribution

ciara_python-1.0.4-py3-none-any.whl (4.9 kB view details)

Uploaded Python 3

File details

Details for the file ciara_python-1.0.4.tar.gz.

File metadata

  • Download URL: ciara_python-1.0.4.tar.gz
  • Upload date:
  • Size: 4.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/34.0 requests/2.25.1 requests-toolbelt/0.9.1 urllib3/1.26.5 tqdm/4.62.2 importlib-metadata/4.5.0 keyring/23.4.1 rfc3986/1.5.0 colorama/0.4.4 CPython/3.6.8

File hashes

Hashes for ciara_python-1.0.4.tar.gz
Algorithm Hash digest
SHA256 0e778b8c72a7fff1d3dd0cd617f7f7c80183ae84debd699b7398ce765329bbaa
MD5 d9337beb333692e536b9e56d143214fb
BLAKE2b-256 9930291c268981a76e9bc2cef00d6de065768dfebc5fd49185449a8924bcfe74

See more details on using hashes here.

File details

Details for the file ciara_python-1.0.4-py3-none-any.whl.

File metadata

  • Download URL: ciara_python-1.0.4-py3-none-any.whl
  • Upload date:
  • Size: 4.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/34.0 requests/2.25.1 requests-toolbelt/0.9.1 urllib3/1.26.5 tqdm/4.62.2 importlib-metadata/4.5.0 keyring/23.4.1 rfc3986/1.5.0 colorama/0.4.4 CPython/3.6.8

File hashes

Hashes for ciara_python-1.0.4-py3-none-any.whl
Algorithm Hash digest
SHA256 8cdde78bb2c19bc19b46ea7604e04bc1c98d71d5a11fa6b4146c9734238489ea
MD5 9f052218be0a3293f2425533f8a0944f
BLAKE2b-256 90164dc7c0bd7b8037db620d37d95c8891373a1bbba67f8da06b596d8d5a791f

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page