MERINGUE

MERINGUE characterizes spatial gene expression heterogeneity in spatially resolved single-cell transcriptomics data with non-uniform cellular densities.

The overall approach is detailed in the following publication: Miller, B., Bambah-Mukku, D., Dulac, C., Zhuang, X. and Fan, J. Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomics data with nonuniform cellular densities. Genome Research. May 2021.

Overview

MERINGUE is a computational framework based on spatial auto-correlation and cross-correlation analysis.

You can use MERINGUE to:

Identify genes with spatially heterogeneous expression
Group significantly spatially variable genes into primary spatial gene expression patterns
Identify pairs of genes with complementary expression patterns in spatially co-localized cell-types that may be indicative of cell-cell communication
Integrate density-agnostic spatial distance weighting to perform spatially-informed transcriptional clustering analysis

In a manner that:

Accomodates 2D, multi-section, and 3D spatial data
Is robut to variations in cellular densities, distortions, or warping common to tissues
Is highly scalable to enable analysis of 10,000s of genes and 1,000s of cells within minutes
Is applicable to diverse spatial transcriptomics technologies

Installation

To install MERINGUE, we recommend using remotes:

# install.packages(remotes)
require(remotes)
remotes::install_github('JEFworks-Lab/MERingue', build_vignettes = TRUE)

Tutorials

Contributing

We welcome any bug reports, enhancement requests, general questions, and other contributions. To submit a bug report or enhancement request, please use the MERINGUE GitHub issues tracker. For more substantial contributions, please fork this repo, push your changes to your fork, and submit a pull request with a good commit message.