unravel.cluster_stats.mean_IF_summary module#

Use cstats_mean_IF_summary (cmis) from UNRAVEL to output plots of mean IF intensities for each cluster in atlas space.

Prereqs:

  • Generate CSV inputs withs cstats_IF_mean

  • Add conditions to input CSV file names: utils_prepend -sk $SAMPLE_KEY -f

Inputs:

  • *.csv files in the working dir with these columns: sample, cluster_ID, mean_IF_intensity

Outputs:

  • cluster_mean_IF_summary/cluster_<cluster_id>.pdf for each cluster

  • If significant differences are found, a prefix ‘_’ is added to the filename to sort the files

Note

  • The first word of the csv inputs is used for the the group names (underscore separated).

Usage for t-tests:#

cstats_mean_IF_summary –order Control Treatment –labels Control Treatment -t ttest [–cluster_ids 1 2 3] [-alt two-sided] [-v]

Usage for Tukey’s tests w/ reordering and renaming of conditions:#

cstats_mean_IF_summary –order group3 group2 group1 –labels Group_3 Group_2 Group_1 [–cluster_ids 1 2 3] [-v]

unravel.cluster_stats.mean_IF_summary.parse_args()[source]#
unravel.cluster_stats.mean_IF_summary.load_data(cluster_id)[source]#
unravel.cluster_stats.mean_IF_summary.perform_t_tests(df, order)[source]#

Perform t-tests between groups in the DataFrame.

unravel.cluster_stats.mean_IF_summary.plot_data(cluster_id, order=None, labels=None, test_type='tukey', alt='two-sided')[source]#
unravel.cluster_stats.mean_IF_summary.main()[source]#