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
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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).
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Whole-body scRNA-seq atlases from humans (across multiple developmental stages) are used as a data-driven source of cell type-specific gene markers.
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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).
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.
Within R:
if(!require("BiocManager")) install.packages("BiocManager")
BiocManager::install("neurogenomics/MSTExplorer")
library(MSTExplorer)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
UK Dementia Research Institute
Department of Brain Sciences
Faculty of Medicine
Imperial College London
GitHub
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