unravel.region_stats.rstats_mean_IF_summary module#

Use rstats_mean_IF_summary from UNRAVEL to output plots of mean IF intensities for each region intensity ID.

Prereqs:

Generate CSV inputs withs rstats_IF_mean or rstats_IF_mean_in_seg
After rstats_IF_mean_in_seg, aggregate CSV inputs with utils_agg_files
If needed, add conditions to input CSV file names: utils_prepend -sk $SAMPLE_KEY -f

Inputs:

*.csv in the working dir with these columns: ‘Region_Intensity’, ‘Mean_IF_Intensity’

Outputs:

rstats_mean_IF_summary/region_<region_id>_<region_abbr>.pdf for each region
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 (e.g. Control from Control_sample01_cFos_rb4_atlas_space_z.csv)
Default csv: UNRAVEL/unravel/core/csvs/CCFv3-2020__regionID_side_IDpath_region_abbr.csv
Alternatively, use CCFv3-2017__regionID_side_IDpath_region_abbr.csv or provide a custom CSV with the same columns.
The look up table (LUT) csv has these columns: ‘Region_ID’, ‘Side’, ‘Name’, ‘Abbr’

Usage for t-tests:#

rstats_mean_IF_summary –order Control Treatment –labels Control Treatment -t ttest [-alt two-sided] [–lut CCFv3-2020__regionID_side_IDpath_region_abbr.csv] [-v]

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

rstats_mean_IF_summary –order group3 group2 group1 –labels Group_3 Group_2 Group_1 [–lut CCFv3-2020__regionID_side_IDpath_region_abbr.csv] [-v]

Usage with a custom atlas:#

atlas=path/custom_atlas.nii.gz ; rstats_mean_IF_summary –region_ids $(img_unique -i $atlas) –order group2 group1 –labels Group_2 Group_1 -t ttest [-alt two-sided] [–lut CCFv3-2020__regionID_side_IDpath_region_abbr.csv] [-v]

unravel.region_stats.rstats_mean_IF_summary.parse_args()[source]#

unravel.region_stats.rstats_mean_IF_summary.load_data(region_id)[source]#

unravel.region_stats.rstats_mean_IF_summary.get_max_region_id_from_csvs()[source]#: Retrieve the maximum Region_Intensity from all input CSVs.

unravel.region_stats.rstats_mean_IF_summary.get_region_details(region_id, csv_path)[source]#

unravel.region_stats.rstats_mean_IF_summary.get_all_region_ids(csv_path)[source]#: Retrieve all region IDs from the provided CSV.

unravel.region_stats.rstats_mean_IF_summary.filter_region_ids(region_ids, max_region_id)[source]#: Filter region IDs to be within the maximum region ID from the CSVs.

unravel.region_stats.rstats_mean_IF_summary.remove_zero_intensity_regions(region_ids)[source]#: Remove regions with Mean_IF_Intensity of 0 across all input CSVs.

unravel.region_stats.rstats_mean_IF_summary.perform_t_tests(df, order)[source]#: Perform t-tests between groups in the DataFrame.

unravel.region_stats.rstats_mean_IF_summary.plot_data(region_id, order=None, labels=None, csv_path=None, test_type='tukey', alt='two-sided')[source]#

unravel.region_stats.rstats_mean_IF_summary.main()[source]#