AHBAprocessing

This repository provides Matlab code for reproducing a range of analyses involving processing Allen Human Brain Atlas gene expression data and running a range of analyses to evaluate the effect of different processing choices. The code was verified using Matlab 2016b, 2017b, 2018a.

If you use this code, please cite our accompanying paper:

📗 A. Arnatkeviciute, B.D. Fulcher, A. Fornito. A practical guide to linking brain-wide gene expression and neuroimaging data. NeuroImage. 189: 353 (2019).

Change Log

NOTE: The code and corresponding data has been updated on the 7th of April 2020. We have discovered some inaccuracies in the cust100 and cust250 parcellations. New random parcellations containing 100 and 250 regions per hemisphere were generated and the corresponding data updated. If you used cust100 and cust250 parcellations, please refer to the new version of the data.

NOTE: The code has been updated on the 28th August 2018 - in the previous version gene ordering in the ROI x gene matrices did not correspond to the gene information provided in the probeInformation structure. This issue has now been fixed. If you processed the data before this date, please re-process it using the updated code.

Contact Aurina Arnatkeviciute by email.

Dependencies:

Brain Connectivity Toolbox, Version 2017-15-01. Toolbox Fast Marching, Version 1.2.0.0

Data files

Data files required for this project are hosted on this figshare repository. Please download and unzip the data in the root directory.

Data processing

After retrieving data add relevant paths using AddPaths.m, data can be generated by running processingPipeline.m with selected options. In order to calculate distances on the cortical surface, toolbox_fast_marching toolbox needs to be installed by running the following lines within matlab from the code/peripheralFunctions/toolbox_fast_marching directory:

• mex -setup
• compile_mex;

This script will run 4 data processing functions:

• S1_extractData - extracts the data from excel files and saves it in Matlab format
• S2_probes - performs probe selection and data filtering if required
• S3_samples2parcellation - assigns samples to the regions of interest in the parcellation
• S4_normalisation - performs data normalisation and calculates correlated gene expression and also provides data in the region x gene format.

See README_processingPipeline.txt for more information.

To generate data for all four parcellations using selected options use processingPipeline_4parcellations.m.

Analysis

Figures in our paper can be regenerated using makeFigures.m in the plotting folder. All scripts should be run from the root directory. Other relevant analysis scripts are in dataProcessing.

Example usage:

After downloading data from figshare to the root directory, add the contents of the root directory to the path using AddPaths function.

To enable distance calculation on the cortical surface, install toolbox_fast_marching toolbox. To install the toolbox run the following lines within matlab from the code/peripheralFunctions/toolbox_fast_marching directory (for more information refer to documentation):

• mex -setup
• compile_mex;

    % Add paths required for the project:
    AddPaths
    % go to the directory where toolbox is located
    cd code/peripheralFunctions/toolbox_fast_marching
    % install the toolbox_fast_marching
    mex -setup
    compile_mex;
    % return to the root directory
    cd ../../..
    % Generate ALL figures:
    makeFigures
    % Run all data processing scripts from scratch (with default options):
    processingPipeline

Useful links:

A package that compares a statistical map with gene expression patterns from Allen Human Brain Atlas https://github.com/chrisgorgo/alleninf.