cell2location

Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics (cell2location model)

Cell2location maps the spatial distribution of cell types by integrating single-cell RNA-seq (scRNA-seq) and multi-cell spatial transcriptomic data from a given tissue.

About

cell2location leverages reference cell type signatures that are estimated from scRNA-seq profiles, for example as obtained using conventional clustering to identify cell types and subpopulations followed by estimation of average cluster gene expression profiles. Cell2location implements this estimation step based on Negative Binomial regression, which allows to robustly combine data across technologies and batches. Using these reference signatures, cell2location decomposes mRNA counts in spatial transcriptomic data, thereby estimating the relative and absolute abundance of each cell type at each spatial location (see figure below).

Cell2location is implemented as an interpretable hierarchical Bayesian model:

  1. providing principled means to account for model uncertainty;
  2. accounting for linear dependencies in cell type abundances;
  3. modelling differences in measurement sensitivity across technologies;
  4. accounting for unexplained/residual variation by employing a flexible count-based error model.

Cell2location is computationally efficient, owing to variational approximate inference and GPU acceleration. For full details and a comparison to existing approaches see our preprint https://www.biorxiv.org/content/10.1101/2020.11.15.378125v1.

The cell2location software comes with a suite of downstream analysis tools, including the identification of groups of cell types with similar spatial locations.

Downloads

There are 2 ways to install and use our package: setup your own conda environment or use the singularity and docker images (recommended). See below for details.

Docker Repository on Quay

You can also try cell2location on Google Colab on a smaller data subset containing somatosensory cortex.

Open In Colab

Please report bugs via https://github.com/BayraktarLab/cell2location/issues and ask any usage questions in https://github.com/BayraktarLab/cell2location/discussions.

We also provide an experimental numpyro translation of the model which has improved memory efficiency (allowing analysis of multiple Visium samples on Google Colab) and minor improvements in speed – https://github.com/vitkl/cell2location_numpyro. You can try it on Google Colab. However, note that both numpyro itself and cell2location_numpyro are in very active development.

Open In Colab

Further information

Usage and Tutorials

Tutorials covering the estimation of expression signatures of reference cell types (1/3), spatial mapping with cell2location (2/3) and the downstream analysis (3/3) can be found here: https://cell2location.readthedocs.io/en/latest/

The architecture of the package is briefly described here. Cell2location architecture is designed to simplify extended versions of the model that account for additional technical and biologial information. We plan to provide a tutorial showing how to add new model classes but please get in touch if you would like to contribute or build on top our package.


Sanger Institute Contributors

Photo of Anna Arutyunyan

Anna Arutyunyan

PhD Student

Photo of Dr Omer Bayraktar

Dr Omer Bayraktar

Group Leader

Photo of Emma Dann

Emma Dann

PhD Student

Photo of Dr Tong LI

Dr Tong LI

Postdoctoral Fellow

Photo of Dr Jun Sung Park

Dr Jun Sung Park

Visiting Scientist

Photo of Martin Prete

Martin Prete

Senior Software Developer

Photo of Dr Lauma Ramona

Dr Lauma Ramona

Research Administrator Tree of Life Programme

Photo of Dr Oliver Stegle

Dr Oliver Stegle

Associate Faculty in the Cellular Genetics Programme

Photo of Roser Vento-Tormo

Roser Vento-Tormo

Group leader

External Contributors

Photo of Artem Shmatko

Artem Shmatko

EMBL-EBI and Moscow State University

Photo of Mika Sarkin Jain

Mika Sarkin Jain

University of Cambridge

Photo of Liz Tuck

Liz Tuck

Sanger Institute

Photo of Louisa James

Louisa James

Queen Mary University of London

Photo of Luz Garcia Alonso

Luz Garcia Alonso

Sanger Institute

 
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Publications

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