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

1. mOB Spatial Transcriptomics Analysis

2. Multi-section 3D Breast Cancer Spatial Transcriptomics Analysis

3. 3D Drosophila Spatial Transcriptomics Analysis

4. Understanding MERINGUE's Spatial Cross-Correlation Statistic using Simulations

5. Spatially-informed transcriptional clustering with MERINGUE

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.