Skip to main content

Compute single-cell cell-type expression specificity

Project description

PyPI version


CELLEX (CELL-type EXpression-specificity) is a tool for computing cell-type Expression Specificity (ES) profiles. It employs a "wisdom of the crowd"-approach by integrating multiple ES metrics, thus combining complementary cell-type ES profiles, to capture multiple aspects of ES and obtain improved robustness.




The documentation for CELLEX can be accessed in the following ways:

We are continually updating the documentation for CELLEX. If some information is missing, please submit your request or question via our issue tracker.

Quick start

This brief tutorial showcases the core features of CELLEX.


import numpy as np
import pandas as pd
import cellex

data = pd.read_csv("./data.csv", index_col=0)
metadata = pd.read_csv("./metadata.csv", index_col=0)

eso = cellex.ESObject(data=data, annotation=metadata, verbose=True)



Option A: Install the latest release from PyPi

pip install cellex

Option B: Install the development version from this repo

Clone the development repo and install from source using pip. The development version may contain bug fixes that have not been released, as well as experimental features.

git clone --branch develop --single-branch
pip install -e .

Import modules

import numpy as np # needed for formatting data for this tutorial
import pandas as pd # needed for formatting data for this tutorial
import cellex

Load input data and metadata

data = pd.read_csv("./data.csv", index_col=0)
metadata = pd.read_csv("./metadata.csv", index_col=0)

Data format

Data may consist of UMI counts (integer) for each gene and cell.

cell_1 ... cell_9
gene_x 0 ... 4
... ... ... ...
gene_z 3 ... 1

Shape: m genes by n cells.

Metadata format

Metadata should consist of unique cell id's and matching annotation (string).

cell_id cell_type
cell_1 type_A
... ...
cell_9 type_C

Shape: n cells by 2.

Create ESObject and compute ESmu

eso = cellex.ESObject(data=data, annotation=metadata, verbose=True)


View Expression Specificity scores

All results are accessible via the results attribute of the ESObject.


Save result(s)

Pro-tip: Using CELLEX with CELLECT

The ESmu scores may be used with CELLECT. CELLECT requires that genes are in the Human Ensembl Gene ID format. CELLEX provides a simple renaming utility for this purpose:

cellex.utils.mapping.mouse_ens_to_human_ens(eso.results["esmu"], drop_unmapped=True, verbose=True)

Save ESmu


Save all or specific results

eso.save_as_csv(keys=["all"], verbose=True)

Output format

Output consist of Expression Specificity Weights (float) for each gene and cell-type. ESmu values lie in the range [0,1].

type_A ... type_C
gene_x 0.0 ... 0.9
... ... ... ...
gene_z 0.1 ... 0.2

Shape: m genes by x unique annotations. N.B. a number of genes may be removed during preprocessing.


Various tutorials and walkthroughs will be made available here, while the Wiki is in the making. These Jupyter Notebooks cover everything from downloading CELLEX and data to analysis and plotting.



  • Tobias Overlund Stannius (University of Copenhagen) @TobiasStannius
  • Pascal Nordgren Timshel (University of Copenhagen) @ptimshel


Please create an issue in this repo, if you encounter any problems using CELLEX. Alternatively, you may write an email to timshel(at)


If you find CELLEX useful for your research, please consider citing: Timshel (eLife, 2020): Genetic mapping of etiologic brain cell types for obesity

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

cellex-1.2.2.tar.gz (7.5 MB view hashes)

Uploaded Source

Built Distribution

cellex-1.2.2-py3-none-any.whl (2.6 MB view hashes)

Uploaded Python 3

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