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Compute single-cell cell-type expression specificity

Project description

PyPI version shields.io

CELLEX

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.

CELLEX_overview

Contents

Documentation

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.

TL;DR

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)
eso.compute(verbose=True)
eso.results["esmu"].to_csv("mydataset.esmu.csv.gz")

Walkthrough

Setup

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 https://github.com/perslab/CELLEX.git --branch develop --single-branch
cd CELLEX
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)

eso.compute(verbose=True)

View Expression Specificity scores

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

eso.results["esmu"]

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

eso.results["esmu"].to_csv("mydataset.esmu.csv.gz")

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.

Tutorials

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.

About

Developers

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

Contact

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

References

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

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