Authors: Brian Schilder, Alan Murphy, Julien Bryois, Nathan Skene

README updated: Oct-04-2024

Introduction

This R package contains code used for testing which cell types can explain the heritability signal from GWAS summary statistics. The method was described in our 2018 Nature Genetics paper.

This package takes GWAS summary statistics + single-cell transcriptome specificity data (in EWCE’s CellTypeData format) as input. It then calculates and returns the enrichment between the GWAS trait and the cell-types.

Installation

R

Install MAGMA.Celltyping as follows:

if(!require("remotes")) install.packages("remotes")

remotes::install_github("neurogenomics/MAGMA_Celltyping")
library(MAGMA.Celltyping)

MAGMA

MAGMA.Celltyping now installs the command line software MAGMA automatically when you first use a function that relies on MAGMA (e.g. celltype_associations_pipeline). If you prefer, you can later install other versions of MAGMA with:

MAGMA.Celltyping::install_magma(desired_version="<version>",
                                update = TRUE)

Documentation

Website

Getting started

Docker/Singularity

Using older versions

With the release of MAGMA_Celltyping 2.0 in January 2022, there have been a number of major updates and bug fixes.

• Only R>4.0.0 is supported. To use this package with older versions of R, install with:remotes::install_github("neurogenomics/MAGMA_Celltyping@01a9e53")

Bugs/fixes

Having trouble? Search the Issues or submit a new one.

Want to contribute new features/fixes? Pull Requests are welcomed!

Both are most welcome, we want the package to be easy to use for everyone!

Citations

If you use the software then please cite:

Skene, et al. Genetic identification of brain cell types underlying schizophrenia. Nature Genetics, 2018.

The package utilises the MAGMA software developed in the Complex Trait Genetics Lab at VU university (not us!) so please also cite:

de Leeuw, et al. MAGMA: Generalized gene-set analysis of GWAS data. PLoS Comput Biol, 2015.

If you use the EWCE package as well then please cite:

Skene, et al. Identification of Vulnerable Cell Types in Major Brain Disorders Using Single Cell Transcriptomes and Expression Weighted Cell Type Enrichment. Front. Neurosci, 2016.

If you use MungeSumstats to format your summary statistics then please cite:

Murphy, Schilder, & Skene, MungeSumstats: a Bioconductor package for the standardization and quality control of many GWAS summary statistics, Bioinformatics, Volume 37, Issue 23, 1 December 2021, Pages 4593–4596, https://doi.org/10.1093/bioinformatics/btab665

If you use the cortex/hippocampus single cell data associated with this package then please cite the following papers:

Zeisel, et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science, 2015.

If you use the midbrain and hypothalamus single cell datasets associated with the 2018 paper then please cite the following papers:

La Manno, et al. Molecular Diversity of Midbrain Development in Mouse, Human, and Stem Cells. Cell, 2016.

Romanov, et al. Molecular interrogation of hypothalamic organization reveals distinct dopamine neuronal subtypes. Nature Neuroscience, 2016.

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Contact

Neurogenomics Lab

UK Dementia Research Institute
Department of Brain Sciences
Faculty of Medicine
Imperial College London
GitHub
DockerHub