brainstat.context.histology.compute_histology_gradients

brainstat.context.histology.compute_histology_gradients(mpc, kernel='normalized_angle', approach='dm', n_components=10, alignment=None, random_state=None, gamma=None, sparsity=0.9, reference=None, n_iter=10)[source]

Computes microstructural profile covariance gradients.

Parameters

  • mpc (numpy.ndarray) – Microstructural profile covariance matrix.

  • kernel (str, optional) – Kernel function to build the affinity matrix. Possible options: {‘pearson’, ‘spearman’, ‘cosine’, ‘normalized_angle’, ‘gaussian’}. If callable, must receive a 2D array and return a 2D square array. If None, use input matrix. By default “normalized_angle”.

  • approach (str, optional) – Embedding approach. Can be ‘pca’ for Principal Component Analysis, ‘le’ for laplacian eigenmaps, or ‘dm’ for diffusion mapping, by default “dm”.

  • n_components (int, optional) – Number of components to return, by default 10.

  • alignment (str, None, optional) – Alignment approach. Only used when two or more datasets are provided. Valid options are ‘pa’ for procrustes analysis and “joint” for joint embedding. If None, no alignment is peformed, by default None.

  • random_state (int, None, optional) – Random state, by default None

  • gamma (float, None, optional) – Inverse kernel width. Only used if kernel == “gaussian”. If None, gamma=1/n_feat, by default None.

  • sparsity (float, optional) – Proportion of smallest elements to zero-out for each row, by default 0.9.

  • reference (numpy.ndarray, optional) – Initial reference for procrustes alignments. Only used when alignment == ‘procrustes’, by default None.

  • n_iter (int, optional) – Number of iterations for Procrustes alignment, by default 10.

Returns

brainspace.gradient.gradient.GradientMaps – BrainSpace gradient maps object.