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scripts Added matlab non-parametric May 20, 2019
README.md Added note about non-parametric May 20, 2019

README.md

ComBat harmonization in Matlab

Table of content

1. Installation

To use ComBat, the following scripts will need to be available in the Matlab path:

  • aprior.m
  • bprior.m
  • itSol.m
  • postmean.m
  • postvar.m
  • inteprior.m
  • combat.m

The directory containing those scripts can be added to the Matlab path as follows:

addPath('path/to/the/scripts/folder');

2. Multi-Site Harmonization

ComBat estimates scanner-specific location and scale parameters, for each feature separately, and pools information across features using empirical Bayes to improve the estimation of those parameters for small sample size studies.

2.1 Full ComBat with empirical Bayes

The combat function is the main function. It requires two mandatory arguments:

  • a data matrix (p x n) dat for which the p rows are features, and the n columns are participants.
  • a numeric or character vector batch of length n indicating the site/scanner/study id.

The ComBat algorithm also accepts an optional argument mod, which is a matrix containing the outcome of interest and other biological covariates. This is recommended when the goal of the downstream statiatical analyses is to look for associations between the imaging data and the biological variables; this will make sure to preserve the biological variability while removing the variability associated with site/scanner.

For illustration purpose, let's simulate an imaging dataset with n=10 participants, acquired on 2 scanners, with 5 participants each, with p=10000 voxels per scan.

p=10000;
n=10;
batch = [1 1 1 1 1 2 2 2 2 2]; %Batch variable for the scanner id
dat = randn(p,n); %Random data matrix

and let simulate an age and disease variable:

age = [82 70 68 66 80 69 72 76 74 80]'; % Continuous variable
disease = [1 2 1 2 1 2 1 2 1 2]'; % Categorical variable

We create a n x 2 model matrix with age as the first column, and the second disease group as a dummy variable for the second column (disease=1 being the baseline category):

disease = dummyvar(disease);
mod = [age disease(:,2)];

We use the function combat to harmonize the data across the 2 scanners using parametric adjustements:

data_harmonized = combat(dat, batch, mod, 1);

or using non-parametric adjustments:

data_harmonized = combat(dat, batch, mod, 0);

To use ComBat without a model matrix, simply set

mod=[];

3. Visualization

Coming soon.

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