Pipelines (graphpype.pipelines):
create_pipeline_nii_to_conmat(main_path[, …]) |
Pipeline from nifti 4D (after preprocessing) to connectivity matrices |
create_pipeline_nii_to_conmat_seg_template(…) |
Pipeline from nifti 4D (after preprocessing) to connectivity matrices |
create_pipeline_nii_to_conmat_simple(main_path) |
Pipeline from nifti 4D (after preprocessing) to connectivity matrices, no segmentation in tissues given, but coords for wm and csf are available and regressed. |
create_pipeline_nii_to_subj_ROI(main_path[, …]) |
Pipeline from nifti 4D (after preprocessing) to connectivity matrices Use Grey matter for having a mask for each subject |
create_pipeline_nii_to_weighted_conmat(main_path) |
Pipeline from resid_ts_file (after preprocessing) to weighted connectivity matrices Involves a regressor file as wiehgt for computing weighted correlations |
create_pipeline_intmat_to_graph_threshold(…) |
Pipeline from integer matrices (normally coclassification matrices) to graph analysis |
create_pipeline_net_list_to_graph(main_path) |
Pipeline from net_List (txt file) to graph analysis |
create_pipeline_conmat_to_graph_threshold(…) |
Pipeline from connectivity matrices to graph analysis |
create_pipeline_conmat_to_graph_density(…) |
Pipeline from connectivity matrices to graph analysis |
Nodes (graphpype.nodes.graph_stats):
PrepareCormat([from_file, resource_monitor, …]) |
Average correlation matrices, within a common reference (based on labels, or coordinates) |
SwapLists([from_file, resource_monitor, …]) |
Exchange lists of files in a random fashion (based on seed value) Typically, cor_mat, coords -> 2, or Z_list, node_corres and labels -> 3 |
ShuffleMatrix([from_file, resource_monitor, …]) |
Compute randomisation of matrix, keeping the same distribution |
StatsPairTTest([from_file, …]) |
Compute ttest stats between 2 group of matrix - matrix are arranged in group_cormat, with order (Nx,Ny,Nsubj). |
Utils (graphpype.utils_stats):
compute_pairwise_ttest_fdr(X, Y, cor_alpha, …) |
Two-way pairwise T-test stats |
compute_pairwise_oneway_ttest_fdr(X, …[, …]) |
Oneway pairwise T-test stats |
compute_pairwise_mannwhitney_fdr(X, Y, cor_alpha) |
compute pairwise Mann Whitney test |