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Harmonizing neuroimaging data across scanners and sites
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examples/bladder major code cleanup Jul 10, 2017
neuroCombat
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README.md

neuroCombat

Overview

This repository contains the ComBat algorithm for correcting batch effects in neuroimaging (or microarray) data. This code runs ~10-50 times faster than the R version, and is incredibly simplified in that you do NOT have to create any design matrices, etc. All you have to do is pass in TWO numpy arrays or pandas DataFrames (the dataset to correct, and the dataset containing the batch/confound/target variables).

Combining the ease-of-use, the ability to read neuroimages directly, the significant computational speedup, and the Python language, neuroCombat should fulfill an important niche in the neuroscience community.

Citation

If you use this code in your study or analysis, we would sincerely appreciate if you cited the following:

Fortin, J. P., N. Cullen, Y. I. Sheline, W. D. Taylor, I. Aselcioglu, P. A. Cook, P. Adams, C. Cooper, M. Fava, P. J. McGrath, M. McInnis, M. L. Phillips, M. H. Trivedi, M. M. Weissman and R. T. Shinohara (2017). "Harmonization of cortical thickness measurements across scanners and sites." Neuroimage 167: 104-120.

Installation

  1. Download zipped repository
  2. Unpack
  3. cd neuroCombat-master
  4. run python setup.py install or pip install .
  5. To use the neuroCombat function once installed:
    • from neuroCombat import neuroCombat

neuroCombat Function

neuroCombat(data, covars, batch_col, discrete_cols=None, continuous_cols=None)

Docstring:
Run ComBat to correct for batch effects in neuroimaging data

Arguments
---------
data : a pandas data frame or numpy array
    neuroimaging data to correct with shape = (samples, features)
    e.g. cortical thickness measurements, image voxels, etc

covars : a pandas data frame w/ shape = (samples, features)
    demographic/phenotypic/behavioral/batch data 
    
batch_col : string
    - batch effect variable
    - e.g. scan site

discrete_cols : string or list of strings
    - variables which are categorical that you want to predict
    - e.g. binary depression or no depression

continuous_cols : string or list of strings
    - variables which are continous that you want to predict
    - e.g. depression sub-scores

Returns
-------
- A numpy array with the same shape as `data` which has now been ComBat-corrected

Correspondance to SVA (R) version of ComBat

The most significant difference between the SVA and neuroCombat algorithms is that the neuroCombat version accepts input data X of shape (n_samples, n_features) while SVA accepts (n_features, n_samples).

In SVA's version of ComBat, you might do the following to correct for dataset X while adjusting for covariates Y$c1 and Y$c2:

batch <- Y$batch
model <- model.matrix(~ c1 + c2, data=Y)
combat_data <- ComBat(dat=X,batch=batch, mod=model)

In this Python version, you would do the following (assuming covars is a Pandas Dataframe with appropriate column labels):

combat_data = neuroCombat(data=X, 
                          covars=Y, 
                          batch_col='batch', 
                          discrete_cols=['c1','c2'])

As you see, there is no need for a model matrix.

Examples

No matter what, the covars argument must be a pandas.DataFrame. However, for the data argument, you may either give a numpy array or a pandas dataframe. Here is the difference between the two (note the result will be the same):

Correcting from Numpy Array as Data

from neuroCombat import neuroCombat

import pandas as pd
import numpy as np

data = np.load('data/bladder-expr.npy')
covars = pd.read_csv('data/bladder-pheno.txt', delimiter='\t')

discrete_cols = ['cancer']
continuous_cols = ['age']
batch_col = 'batch'

data_combat = neuroCombat(data=data,
                          covars=covars,
                          batch_col=batch_col,
                          discrete_cols=discrete_cols,
                          continuous_cols=continuous_cols)

Correcting from pandas.DataFrame as Data

from neuroCombat import neuroCombat

import pandas as pd
import numpy as np

data = pd.read_csv('data/bladder-expr.txt', delimiter='\t')
covars = pd.read_csv('data/bladder-pheno.txt', delimiter='\t')

discrete_cols = ['cancer']
continuous_cols = ['age']
batch_col = 'batch'

data_combat = neuroCombat(data=data,
                          covars=covars,
                          batch_col=batch_col,
                          discrete_cols=discrete_cols,
                          continuous_cols=continuous_cols)

Performance

On the bladder example above, we report the following:

  • Combat.py repository (not mine): ~0.6s
  • R's SVA package : ~10s
  • neuroCombat repository (mine): ~0.1 s
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