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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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‎.flake8‎

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@@ -9,6 +9,7 @@ exclude =
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tests/*
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_version.py
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demos/*
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WIP/*
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count = True
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show-source = True
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statistics = True

‎.github/workflows/run_tests_cli.yml‎

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coverage run --source src -m pytest
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coverage xml
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- name: Code coverage
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uses: codecov/codecov-action@v4
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with:
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file: coverage.xml
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flags: cli
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name: codecov-cli
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fail_ci_if_error: false
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# - name: Code coverage
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# uses: codecov/codecov-action@v4
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# with:
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# file: coverage.xml
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# flags: cli
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# name: codecov-cli
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# fail_ci_if_error: false

‎CHANGELOG.md‎

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@@ -24,6 +24,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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### Added
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* [ENH] add support for one-way ANOVA across groups at the group level #1296 by @Remi-Gau
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* [ENH] allow for 2 sample T-Test, within group T-Test and one-way ANOVA to ne more flexible with respect to what praticipants.tsv column to use to allocate subjects in each group #1296 by @Remi-Gau
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* [ENH] make `addConfoundsToDesignMatrix` a method of `BidsModel` #1294 by @Remi-Gau
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* [ENH] add Apptainer definition #1254 by @Remi-Gau and @monique2208
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* [ENH] allow to copy anat only on raw datasets #1181 by @Remi-Gau
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### Changed
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* [ENH] align specification of F contrasts on the BIDS stats model: they should now be specified as a 2D matrix and not a 1D vector. #1276 @Remi-Gau
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* [DOC] change theme and structure of the documentation #1256 @Remi-Gau
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* [REF] Refactor and update CLI in #1096 @Remi-Gau

‎WIP/find_data_set_with_3_groups.py‎

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from pathlib import Path
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import pandas as pd
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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():
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file = ds / "participants.tsv"
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if file.exists():
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try:
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df = pd.read_csv(file, sep="\t")
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except ParserError:
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...
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if len(df) < 10:
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continue
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if "group" in df.columns:
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col = "group"
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elif "Group" in df.columns:
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col = "Group"
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else:
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continue
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if len(df[col].value_counts()) < 3:
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continue
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if "age" in df.columns and df["age"].mean() < 18:
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continue
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bold_files = list(ds.glob("**/*bold*"))
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has_func = len(bold_files) > 0
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if not has_func:
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continue
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tasks = {x.name.split("task-")[1].split("_")[0] for x in bold_files}
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if len(tasks) == 1 and "rest" in tasks:
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continue
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print()
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print(file)
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print(df[col].value_counts())
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print(tasks)

‎demos/openneuro/Makefile‎

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datalad get -d inputs/ds002799 inputs/ds002799/derivatives/fmriprep/sub-292/*/func/*tsv
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datalad get -d inputs/ds002799 inputs/ds002799/derivatives/fmriprep/sub-30[27]/*/func/*MNI152NLin2009cAsym* -J 2
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datalad get -d inputs/ds002799 inputs/ds002799/derivatives/fmriprep/sub-30[27]/*/func/*tsv -J 2
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data_ds003397:
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mkdir -p inputs
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cd inputs && datalad install ///openneuro/ds003397
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cd inputs && datalad install https://github.com/OpenNeuroDerivatives/ds003397-fmriprep
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cd inputs/ds003397-fmriprep && datalad get sub-[01][1267]/anat/*MNI152NLin2009cAsym*T1w.nii.gz -J 12
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cd inputs/ds003397-fmriprep && datalad get sub-[01][1267]/anat/*mask*.nii.gz -J 12
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cd inputs/ds003397-fmriprep && datalad get sub-[01][1267]/func/*time*tsv -J 12
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cd inputs/ds003397-fmriprep && datalad get sub-[01][1267]/func/*json -J 12
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cd inputs/ds003397-fmriprep && datalad get sub-[01][1267]/func/*MNI152NLin2009cAsym*desc-preproc*bold.nii.gz -J 12

‎demos/openneuro/ds000114_run.m‎

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% Demo to compare activations across sessions.
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% (C) Copyright 2023 bidspm developers
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clear;

‎demos/openneuro/ds003397_run.m‎

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% Run a one-way ANOVA across group
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%
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% 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;
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clc;
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addpath(fullfile(pwd, '..', '..'));
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bidspm();
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task = 'checkerboard';
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% The directory where the data are located
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root_dir = fileparts(mfilename('fullpath'));
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bids_dir = fullfile(root_dir, 'inputs', 'ds003397');
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fmriprep_dir = fullfile(root_dir, 'inputs', 'ds003397-fmriprep');
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output_dir = fullfile(root_dir, 'outputs', 'ds003397', 'derivatives');
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space = {'MNI152NLin2009cAsym'};
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participant_label = {'01', '06', '11', '12'};
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% Copy (& smooth)
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FWHM = 0;
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bidspm(fmriprep_dir, output_dir, 'subject', ...
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'participant_label', participant_label, ...
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'action', 'smooth', ...
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'task', task, ...
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'space', space, ...
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'fwhm', FWHM, ...
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'verbosity', 3);
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%% Stats
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preproc_dir = fullfile(output_dir, 'bidspm-preproc');
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model_file = fullfile(root_dir, ...
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'models', ...
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'model-ds003397_smdl.json');
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bidspm(bids_dir, output_dir, 'subject', ...
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'participant_label', participant_label, ...
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'action', 'stats', ...
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'preproc_dir', preproc_dir, ...
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'model_file', model_file, ...
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'roi_atlas', 'hcpex', ...
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'space', space, ...
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'fwhm', 0, ...
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'skip_validation', true, ...
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'verbosity', 3);
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bidspm(bids_dir, output_dir, 'dataset', ...
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'participant_label', participant_label, ...
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'action', 'stats', ...
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'preproc_dir', preproc_dir, ...
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'model_file', model_file, ...
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'roi_atlas', 'hcpex', ...
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'space', space, ...
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'fwhm', 0, ...
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'skip_validation', true, ...
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'verbosity', 3);

‎demos/openneuro/models/model-ds003397_smdl.json‎

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{
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"Name": "1_way_ANOVA",
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"BIDSModelVersion": "1.0.0",
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"Input": {
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"task": [
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"checkerboard"
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],
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"space": [
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"MNI152NLin2009cAsym"
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]
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},
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"Nodes": [
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{
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"Level": "Run",
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"Name": "run_level",
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"GroupBy": [
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"run",
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"subject"
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],
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"Model": {
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"Type": "glm",
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"X": [
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"trial_type.flashing checkerboard",
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"trans_?",
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"rot_?"
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],
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"HRF": {
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"Variables": [
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"trial_type.flashing checkerboard"
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],
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"Model": "spm"
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}
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},
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"Contrasts": [
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{
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"Name": "flashing checkerboard",
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"ConditionList": [
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"trial_type.flashing checkerboard"
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],
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"Weights": [
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1
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],
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"Test": "t"
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}
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]
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},
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{
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"Level": "Subject",
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"Name": "subject_level",
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"GroupBy": [
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"contrast",
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"subject"
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],
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"Model": {
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"Type": "glm",
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"X": [
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1
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],
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"Software": {
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"bidspm": {
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"Results": [
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{
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"name": [
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"flashing checkerboard"
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],
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"p": 0.05,
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"MC": "FWE",
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"png": true,
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"binary": false,
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"nidm": false,
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"montage": {
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"do": true,
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"slices": [
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-4,
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0,
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4,
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8,
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16
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],
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"background": {
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"suffix": "T1w",
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"desc": "preproc",
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"modality": "anat"
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}
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}
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}
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]
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}
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}
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},
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"DummyContrasts": {
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"Test": "t"
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}
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},
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{
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"Level": "Dataset",
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"Name": "dataset_level",
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"Description": "average across all subjects",
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"GroupBy": [
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"contrast"
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],
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"Model": {
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"Type": "glm",
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"X": [
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1
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]
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}
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},
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{
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"Level": "Dataset",
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"Name": "between_groups",
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"Description": "one way anova",
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"GroupBy": [
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"contrast"
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],
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"Model": {
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"Type": "glm",
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"X": [
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1,
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"group"
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],
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"Software": {
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"bidspm": {
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"Results": [
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{
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"name": [
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"B > I",
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"average across groups"
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],
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"p": 0.01,
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"MC": "FWE",
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"png": true,
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"binary": false,
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"nidm": false,
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"montage": {
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"do": false
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}
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}
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]
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}
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}
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},
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"Contrasts": [
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{
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"Name": "B > I",
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"ConditionList": [
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"group.B",
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"group.I"
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],
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"Weights": [
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1,
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-1
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],
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"Test": "t"
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},
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{
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"Name": "average across groups",
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"ConditionList": [
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"group.B",
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"group.I",
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"group.BI"
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],
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"Weights": [
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1,
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1,
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1
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],
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"Test": "t"
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},
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{
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"Name": "some F test",
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"ConditionList": [
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"group.B",
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"group.BI",
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"group.I"
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],
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"Weights": [
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[
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1,
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0,
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0
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],
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[
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0,
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1,
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0
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],
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[
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0,
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0,
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1
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]
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],
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"Test": "F"
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}
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]
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}
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],
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"Edges": [
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{
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"Source": "run_level",
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"Destination": "subject_level"
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},
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{
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"Source": "subject_level",
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"Destination": "dataset_level"
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},
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{
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"Source": "subject_level",
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"Destination": "between_groups",
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"Filter": {
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"contrast": [
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"flashing checkerboard"
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]
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}
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}
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]
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}

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