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[ENH] implement one-way ANOVA at group level to compare groups (#1296)
* start adding a 3 group dataset * [ENH] use sub commands for python CLI (#1292) * start adding sub commands * preproc subcommand * deal with create roi parser * deal with default_model sub command * deal with bms action * deal with stats actions * start adapting cli * add test for command building * add test for CLI for preproc, smooth... * add test for command building for stats * add test for command building for bms * rename function * start switching CLI * several fixes * more fix * keep switchingµ * fix * fix * linti * fixes * fixes * minor fixes * additional fixes * fix output ROI * visualize output in circle ci * add temp script for boutiques and bids model graph * start adding tweaks * try 3 groups * rename file * update code and tests * add multi group demo * fixes * adapt example * fix contrasts * deal with results * update doc * minor changes * tmp * refactor * refactor * refactor * several fixes * several fixes * fix and refactor * fix and refactor * fix and refactor * fix models * fix * fix * fix fast tests * fixes * fix and implement F test * run group level node by node * add F test to demo * octave fix
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| Original file line number | Diff line number | Diff line change |
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@@ -9,6 +9,7 @@ exclude = | |
| tests/* | ||
| _version.py | ||
| demos/* | ||
| WIP/* | ||
| count = True | ||
| show-source = True | ||
| statistics = True | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,43 @@ | ||
| from pathlib import Path | ||
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| import pandas as pd | ||
| from pandas.errors import ParserError | ||
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| openneuro = Path("/home/remi/datalad/datasets.datalad.org/openneuro") | ||
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| for ds in openneuro.iterdir(): | ||
| file = ds / "participants.tsv" | ||
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| if file.exists(): | ||
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| try: | ||
| df = pd.read_csv(file, sep="\t") | ||
| except ParserError: | ||
| ... | ||
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| if len(df) < 10: | ||
| continue | ||
| if "group" in df.columns: | ||
| col = "group" | ||
| elif "Group" in df.columns: | ||
| col = "Group" | ||
| else: | ||
| continue | ||
| if len(df[col].value_counts()) < 3: | ||
| continue | ||
| if "age" in df.columns and df["age"].mean() < 18: | ||
| continue | ||
| bold_files = list(ds.glob("**/*bold*")) | ||
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| has_func = len(bold_files) > 0 | ||
| if not has_func: | ||
| continue | ||
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| tasks = {x.name.split("task-")[1].split("_")[0] for x in bold_files} | ||
| if len(tasks) == 1 and "rest" in tasks: | ||
| continue | ||
|
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| print() | ||
| print(file) | ||
| print(df[col].value_counts()) | ||
| print(tasks) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,3 +1,5 @@ | ||
| % Demo to compare activations across sessions. | ||
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| % (C) Copyright 2023 bidspm developers | ||
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| clear; | ||
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||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,61 @@ | ||
| % Run a one-way ANOVA across group | ||
| % | ||
| % Only a few subjects are run because of the large size of each run. | ||
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| % (C) Copyright 2024 bidspm developers | ||
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| clear; | ||
| clc; | ||
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| addpath(fullfile(pwd, '..', '..')); | ||
| bidspm(); | ||
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| task = 'checkerboard'; | ||
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| % The directory where the data are located | ||
| root_dir = fileparts(mfilename('fullpath')); | ||
| bids_dir = fullfile(root_dir, 'inputs', 'ds003397'); | ||
| fmriprep_dir = fullfile(root_dir, 'inputs', 'ds003397-fmriprep'); | ||
| output_dir = fullfile(root_dir, 'outputs', 'ds003397', 'derivatives'); | ||
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| space = {'MNI152NLin2009cAsym'}; | ||
| participant_label = {'01', '06', '11', '12'}; | ||
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| % Copy (& smooth) | ||
| FWHM = 0; | ||
| bidspm(fmriprep_dir, output_dir, 'subject', ... | ||
| 'participant_label', participant_label, ... | ||
| 'action', 'smooth', ... | ||
| 'task', task, ... | ||
| 'space', space, ... | ||
| 'fwhm', FWHM, ... | ||
| 'verbosity', 3); | ||
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| %% Stats | ||
| preproc_dir = fullfile(output_dir, 'bidspm-preproc'); | ||
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| model_file = fullfile(root_dir, ... | ||
| 'models', ... | ||
| 'model-ds003397_smdl.json'); | ||
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| bidspm(bids_dir, output_dir, 'subject', ... | ||
| 'participant_label', participant_label, ... | ||
| 'action', 'stats', ... | ||
| 'preproc_dir', preproc_dir, ... | ||
| 'model_file', model_file, ... | ||
| 'roi_atlas', 'hcpex', ... | ||
| 'space', space, ... | ||
| 'fwhm', 0, ... | ||
| 'skip_validation', true, ... | ||
| 'verbosity', 3); | ||
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| bidspm(bids_dir, output_dir, 'dataset', ... | ||
| 'participant_label', participant_label, ... | ||
| 'action', 'stats', ... | ||
| 'preproc_dir', preproc_dir, ... | ||
| 'model_file', model_file, ... | ||
| 'roi_atlas', 'hcpex', ... | ||
| 'space', space, ... | ||
| 'fwhm', 0, ... | ||
| 'skip_validation', true, ... | ||
| 'verbosity', 3); |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,218 @@ | ||
| { | ||
| "Name": "1_way_ANOVA", | ||
| "BIDSModelVersion": "1.0.0", | ||
| "Input": { | ||
| "task": [ | ||
| "checkerboard" | ||
| ], | ||
| "space": [ | ||
| "MNI152NLin2009cAsym" | ||
| ] | ||
| }, | ||
| "Nodes": [ | ||
| { | ||
| "Level": "Run", | ||
| "Name": "run_level", | ||
| "GroupBy": [ | ||
| "run", | ||
| "subject" | ||
| ], | ||
| "Model": { | ||
| "Type": "glm", | ||
| "X": [ | ||
| "trial_type.flashing checkerboard", | ||
| "trans_?", | ||
| "rot_?" | ||
| ], | ||
| "HRF": { | ||
| "Variables": [ | ||
| "trial_type.flashing checkerboard" | ||
| ], | ||
| "Model": "spm" | ||
| } | ||
| }, | ||
| "Contrasts": [ | ||
| { | ||
| "Name": "flashing checkerboard", | ||
| "ConditionList": [ | ||
| "trial_type.flashing checkerboard" | ||
| ], | ||
| "Weights": [ | ||
| 1 | ||
| ], | ||
| "Test": "t" | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "Level": "Subject", | ||
| "Name": "subject_level", | ||
| "GroupBy": [ | ||
| "contrast", | ||
| "subject" | ||
| ], | ||
| "Model": { | ||
| "Type": "glm", | ||
| "X": [ | ||
| 1 | ||
| ], | ||
| "Software": { | ||
| "bidspm": { | ||
| "Results": [ | ||
| { | ||
| "name": [ | ||
| "flashing checkerboard" | ||
| ], | ||
| "p": 0.05, | ||
| "MC": "FWE", | ||
| "png": true, | ||
| "binary": false, | ||
| "nidm": false, | ||
| "montage": { | ||
| "do": true, | ||
| "slices": [ | ||
| -4, | ||
| 0, | ||
| 4, | ||
| 8, | ||
| 16 | ||
| ], | ||
| "background": { | ||
| "suffix": "T1w", | ||
| "desc": "preproc", | ||
| "modality": "anat" | ||
| } | ||
| } | ||
| } | ||
| ] | ||
| } | ||
| } | ||
| }, | ||
| "DummyContrasts": { | ||
| "Test": "t" | ||
| } | ||
| }, | ||
| { | ||
| "Level": "Dataset", | ||
| "Name": "dataset_level", | ||
| "Description": "average across all subjects", | ||
| "GroupBy": [ | ||
| "contrast" | ||
| ], | ||
| "Model": { | ||
| "Type": "glm", | ||
| "X": [ | ||
| 1 | ||
| ] | ||
| } | ||
| }, | ||
| { | ||
| "Level": "Dataset", | ||
| "Name": "between_groups", | ||
| "Description": "one way anova", | ||
| "GroupBy": [ | ||
| "contrast" | ||
| ], | ||
| "Model": { | ||
| "Type": "glm", | ||
| "X": [ | ||
| 1, | ||
| "group" | ||
| ], | ||
| "Software": { | ||
| "bidspm": { | ||
| "Results": [ | ||
| { | ||
| "name": [ | ||
| "B > I", | ||
| "average across groups" | ||
| ], | ||
| "p": 0.01, | ||
| "MC": "FWE", | ||
| "png": true, | ||
| "binary": false, | ||
| "nidm": false, | ||
| "montage": { | ||
| "do": false | ||
| } | ||
| } | ||
| ] | ||
| } | ||
| } | ||
| }, | ||
| "Contrasts": [ | ||
| { | ||
| "Name": "B > I", | ||
| "ConditionList": [ | ||
| "group.B", | ||
| "group.I" | ||
| ], | ||
| "Weights": [ | ||
| 1, | ||
| -1 | ||
| ], | ||
| "Test": "t" | ||
| }, | ||
| { | ||
| "Name": "average across groups", | ||
| "ConditionList": [ | ||
| "group.B", | ||
| "group.I", | ||
| "group.BI" | ||
| ], | ||
| "Weights": [ | ||
| 1, | ||
| 1, | ||
| 1 | ||
| ], | ||
| "Test": "t" | ||
| }, | ||
| { | ||
| "Name": "some F test", | ||
| "ConditionList": [ | ||
| "group.B", | ||
| "group.BI", | ||
| "group.I" | ||
| ], | ||
| "Weights": [ | ||
| [ | ||
| 1, | ||
| 0, | ||
| 0 | ||
| ], | ||
| [ | ||
| 0, | ||
| 1, | ||
| 0 | ||
| ], | ||
| [ | ||
| 0, | ||
| 0, | ||
| 1 | ||
| ] | ||
| ], | ||
| "Test": "F" | ||
| } | ||
| ] | ||
| } | ||
| ], | ||
| "Edges": [ | ||
| { | ||
| "Source": "run_level", | ||
| "Destination": "subject_level" | ||
| }, | ||
| { | ||
| "Source": "subject_level", | ||
| "Destination": "dataset_level" | ||
| }, | ||
| { | ||
| "Source": "subject_level", | ||
| "Destination": "between_groups", | ||
| "Filter": { | ||
| "contrast": [ | ||
| "flashing checkerboard" | ||
| ] | ||
| } | ||
| } | ||
| ] | ||
| } |
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