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Vogel_PLS_Tx-Space

Data and code for Vogel et al., Transcriptomic Gradients paper

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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; Jakob Seidlitz

Faculty Lead(s)

Theodore Satterthwaite; Aaron Alexander-Bloch

Analytic Replicator

Maxwell Bertolero

Collaborators

Konrad Wagstyl, Ross Markello, Casey Paquola, Alexandr Diaz-Papkovic, Thomas Funck, Alan C Evans, Boris Bernhardt, Bratislav Misic

Project Start Date

October 2018

Current Project Status

Drafting manuscript

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

Not yet established.

Current work products

OHBM 2020 presentation –> https://www.humanbrainmapping.org/files/2020/ORAL_SESSION_Modeling_and_Analysis_Multivariate_multimodal_analysis.pdf

Code documentation

Every single aspect of preprocessing, sample selection, analysis, plotting, etc has be thoroughly documented and annotated within 11 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.