brainstat.context.genetics.surface_genetic_expression

brainstat.context.genetics.surface_genetic_expression(pial, white, labels, volume_template, *, atlas_info=None, ibf_threshold=0.5, probe_selection='diff_stability', donor_probes='aggregate', lr_mirror=False, exact=True, tolerance=2, sample_norm='srs', gene_norm='srs', norm_matched=True, region_agg='donors', agg_metric='mean', corrected_mni=True, reannotated=True, return_counts=False, return_donors=False, donors='all', data_dir=None, verbose=1, n_proc=1)[source]

Computes genetic expression of surface parcels.

Parameters

  • pial (str, BSPolyData, list) – Path of a pial surface file, BSPolyData of a pial surface or a list containing multiple of the aforementioned.

  • white (str, BSPolyData, list) – Path of a white matter surface file, BSPolyData of a pial surface or a list containing multiple of the aforementioned.

  • labels (str, numpy.ndarray, list) – Path to a label file for the surfaces, numpy array containing the labels, or a list containing multiple of the aforementioned.

  • volume_template (str, nibabel.nifti1.Nifti1Image) – Path to a nifti file to use as a template for the surface to volume procedure, or a loaded NIfTI image.

  • the (For details of the remaining parameters please consult) –

  • "atlas" (abagen.get_expression_data() documentation. All its parameters bar) –

  • parameters. (are valid input) –

Returns

pandas.DataFrame – Dataframe containing the expression of each gene within each region.

Notes

An equal number of pial/white surfaces and labels must be provided. If parcellations overlap across surfaces, then the labels are kept for the first provided surface.

Examples

>>> from brainstat.context.genetics import surface_genetic_expression
>>> from nilearn import datasets

>>> fsaverage = datasets.fetch_surf_fsaverage()
>>> destrieux_atlas = datasets.fetch_atlas_surf_destrieux()
>>> parcellation = destrieux_atlas['map_left']
>>> mni152 = datasets.load_mni152_template()
>>> surface_genetic_expression(fsaverage['pial_left'], fsaverage['white_left'],
...    parcellation, mni152)