License: GPL-3
R build status

Authors: Brian Schilder, Robert Gordon-Smith, Nathan Skene, Hiranyamaya Dash

README updated: Aug-08-2025

Introduction

Many genes have been associated with diseases Multi-Scale Target Explorer (MSTExplorer) systematically identifies, prioritises, and visualises cell-type-specific gene therapy targets across the phenome.

Core functionalities include:

1. Conducting phenotype x cell type genetic association tests at scale

  • The Human Phenotype Ontology (integrated with gene annotations from OMIM / DECIPHER / ORPHANET) is used as the source of phenotype gene signatures. Each gene-phenotype associated is given a continuous score that approximates the current strength of evidence for the association (using data derived from GenCC).

  • Whole-body scRNA-seq atlases from humans (across multiple developmental stages) are used as a data-driven source of cell type-specific gene markers.

  • The underlying association tests are designed for both speed and accuracy using memory-efficient data structures, and a highly parallelisable implementation of Generalised Linear Regression (GLM). For example, associations for all pairwise combinations of >11k phenotypes x >200 cell types (>2,200,000 associations) can be in <30 minutes on a Macbook laptop with 10 CPU cores).

2. Inferring multi-scale causal graphs of disease

MSTExplorer allows users to easily infer and construct multi-scale causal graphs of Diseases (blue nodes) -> Phenotypes (purple nodes) -> Cell types (orange nodes) -> Genes (yellow nodes).

Example multi-scale network focused on lethal skeletal dysplasia, a phenotype of multiple diseases

See here for more example networks..

3. Prioritising cell-type-specific gene therapy targets

MSTExplorer also provides a comprehensive and customisable pipeline that can be run via a single function (prioritise_targets()) to produce the most promising cell-type-specific gene therapy targets across the phenome.

Installation

Within R:

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

BiocManager::install("neurogenomics/MSTExplorer")
library(MSTExplorer)

Citation

If you use MSTExplorer, please cite:

Kitty B. Murphy, Robert Gordon-Smith, Jai Chapman, Momoko Otani, Brian M. Schilder, Nathan G. Skene (2023) Identification of cell type-specific gene targets underlying thousands of rare diseases and subtraits. medRxiv, https://doi.org/10.1101/2023.02.13.23285820

Contact

Neurogenomics Lab

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

Session Info

utils::sessionInfo()
## R version 4.5.1 (2025-06-13)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
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## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
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##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
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## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
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## time zone: UTC
## tzcode source: system (glibc)
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## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
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## other attached packages:
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## loaded via a namespace (and not attached):
##  [1] gtable_0.3.6        jsonlite_2.0.0      renv_1.1.5         
##  [4] dplyr_1.1.4         compiler_4.5.1      BiocManager_1.30.26
##  [7] tidyselect_1.2.1    dichromat_2.0-0.1   rvcheck_0.2.1      
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## [13] here_1.0.1          ggplot2_3.5.2       R6_2.6.1           
## [16] generics_0.1.4      knitr_1.50          yulab.utils_0.2.0  
## [19] tibble_3.3.0        desc_1.4.3          dlstats_0.1.7      
## [22] rprojroot_2.1.0     pillar_1.11.0       RColorBrewer_1.1-3 
## [25] rlang_1.1.6         badger_0.2.5        xfun_0.52          
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## [40] tools_4.5.1         pkgconfig_2.0.3     htmltools_0.5.8.1