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Whole-brain molecular axes encoding relative spatial location in the human brain |
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Project Title
Whole-brain molecular axes encoding relative spatial location in the human brain
Brief Project Description
There is strong evidence for large-scale genomic gradients helping to organize the spatial distribution of function in the brain, but this topic is very difficult to study in humans. We use PLS to define spatial gradients of gene expression in the adult human brain, and we explore how these gradients emerge of ontogeny and phylogeny, how they relate to topological brain features, how they may influence different traits, and what their relationship is to cell fate.
Project Lead(s)
Jacob W Vogel
Faculty Lead(s)
Theodore Satterthwaite; Jakob Seidlitz
Analytic Replicator
Maxwell Bertolero
Collaborators
Aaron Alexander-Bloch, Konrad Wagstyl, Ross Markello, Adam Pines, Valerie J Sydnor, Alexandr Diaz-Papkovich, Justine Hansen, Alan C Evans, Boris Bernhardt, Bratislav Misic
Project Start Date
October 2018
Current Project Status
Preprint published; manuscript submitted
Dataset
Primary: Allen Human Brain Atlas
Secondary: Brainspan, GTEx, PSYCHEncode Primate/Human
Github repo
https://github.com/PennLINC/Vogel_PLS_Tx-Space
Path to data on filesystem
n/a
Slack Channel
n/a
Trello board
https://trello.com/c/Ut7atlyO/27-pls-gene-expression-project
Google Drive Folder
https://drive.google.com/drive/folders/1fOZRjUR3lyKsENMgG05WNAaJY26l6ham?usp=sharing
Zotero library
None --> Paperpile used
Current work products
OHBM 2020 presentation --> https://www.humanbrainmapping.org/files/2020/ORAL_SESSION_Modeling_and_Analysis_Multivariate_multimodal_analysis.pdf
OHBM 2022 presentation --> https://www.humanbrainmapping.org/files/2022/Oral%20Sessions/Imaging_Genetics_Mapping_the_Effects_of_Genetic_and_Transcriptional_Variation_on_the_Brain.pdf
Preprint --> Coming soon...
Code documentation
Every single aspect of preprocessing, sample selection, analysis, plotting, etc has be thoroughly documented and annotated within 9 Jupyter notebooks, which can be found in the Git repo. These can be run in order (most importantly the first 3 notebooks), and all data and code necessary to execute them is avaialble within the repo or instructions are given on how to download necessary data from public repositories. Instructions for cloning python environment are provided in the documentation on the github repo. Since all of these analyses are based on public data, reproducing my environment, following the instructions and running the notebook should be sufficient for completely reproducing every aspect of the manuscript.