BrainImageR: spatiotemporal gene set analysis referencing the human brain.

Area of Science:

• Neuroscience
• Computational Biology
• Genetics

Background:

• Interpreting neuronal analyses (e.g., transcriptomics, epigenetics) requires context within the human brain.
• Accessing human brain tissue is challenging, leading to reliance on alternative sources like peripheral tissues or in vitro neurons.
• Current methods for analyzing post-mortem brain datasets often involve custom computational tools, causing inter-study discrepancies.

Purpose of the Study:

• To develop a user-friendly tool for examining spatiotemporal gene expression in the post-mortem human brain.
• To provide a standardized method for assessing gene set associations with the human brain.
• To aid in the molecular interrogation of neurological and psychiatric disorders and advance disease modeling.

Main Methods:

• Developed brainImageR, an R package for calculating spatial and temporal associations of datasets with the post-mortem human brain.
• Implemented functionalities to identify anatomical regions enriched for candidate gene set expression.
• Included a feature to predict the developmental time point of samples.

Main Results:

• brainImageR enables the calculation of both spatial and temporal associations of a dataset with the post-mortem human brain.
• The package identifies specific anatomical regions with enriched gene expression.
• It accurately predicts sample developmental time points, crucial for in vitro neuronal modeling.

Conclusions:

• brainImageR offers a quick and efficient method for characterizing datasets across normal human brain development.
• The tool addresses the need for standardized analysis of post-mortem brain data.
• Facilitates molecular interrogation in neurological/psychiatric disorders and benefits human genetics research.