unravel.cluster_stats.fdr module

Use cstats_fdr from UNRAVEL to perform FDR correction on a 1 - p value map to define clusters.

Inputs:

• p value map (e.g., *`vox_p_`*`stat`*.nii.gz from vstats)

Outputs saved in the output directory:

• FDR-adjusted p value map

• Cluster information CSV

• Reversed cluster index image (output_dir/input_name_rev_cluster_index.nii.gz)

• min_cluster_size_in_voxels.txt

• p_value_threshold.txt

• 1-p_value_threshold.txt

Note

• Cluster IDs are reversed in the cluster index image so that the largest cluster is 1, the second largest is 2, etc.

• For bilateral data processed with a hemispheric mask, next run cstats_mirror_indices to mirror the cluster indices to the other hemisphere.

• For unilateral data or bilateral data processed with a whole brain mask, the cluster indices are ready for validation with cstats_validation.

Making directional cluster indices from non-directional p value maps output from ANOVAs:

• Provide the average immunostaining intensity images for each group being contrasted (img_avg)

• The –output needs to have <group1>_v_<group2> in the name

• _v_ will be replaced with _gt_ or _lt_ based on the effect direction

• The cluster index will be split accoding to the effect directions

• cstats_fdr -i vox_p_fstat1.nii.gz -mas mask.nii.gz -q 0.05 -a1 group1_avg.nii.gz -a2 group2_avg.nii.gz -o stats_info_g1_v_g2 -v

Next commands:

• cstats_mirror_indices to mirror the cluster indices to the other hemisphere (if bilateral data processed with a hemispheric mask).

• cstats_validation to validate the cluster indices (if unilateral data or bilateral data processed with a whole brain mask).

Usage

cstats_fdr -i path/vox_p_tstat1.nii.gz -mas path/mask.nii.gz -q 0.05 0.01 0.001 [-ms 100] [-o output_dir] [-a1 path/avg_img1.nii.gz] [-a2 path/avg_img2.nii.gz] [-th 10] [-v]

unravel.cluster_stats.fdr.parse_args()

unravel.cluster_stats.fdr.fdr(input_path, fdr_path, mask_path, q_value)

Perform FDR correction on the input p value map using a mask.

Parameters:

• input_path (-) – the path to the p value map

• fdr_path (-) – the path to the output directory

• mask_path (-) – the path to the mask

• q_value (-) – the q value for FDR correction

Saves in the fdr_path:

• FDR-adjusted p value map

Returns:

the path to the FDR-adjusted p value map - probability_threshold (float): the probability threshold for the FDR correction

Return type:

• adjusted_pval_output_path (str)

unravel.cluster_stats.fdr.cluster_index(adj_p_val_img_path, min_size, q_value, output_index)

unravel.cluster_stats.fdr.reverse_clusters(cluster_index_img, output, data_type, cluster_index_nii)

Reverse the cluster IDs in a cluster index image (ndarray). Return the reversed cluster index ndarray.

unravel.cluster_stats.fdr.split_clusters_based_on_effect(rev_cluster_index_img, avg_img1, avg_img2, output, max_cluster_id, data_type, cluster_index_nii)

unravel.cluster_stats.fdr.process_fdr_and_clusters(input, mask, q, min_size, avg_img1, avg_img2, output=None)

Process FDR correction and cluster index generation for a given q value.

unravel.cluster_stats.fdr.main()