Update subtraction ale script and data files

.gitignore

@@ -7,3 +7,4 @@ results/**
 *Icon*
 testing/**
 results_27-08-2020/**
+subtractions/**

README.md

@@ -17,7 +17,7 @@ usage: nimare-ales.py [-h] [--iters ITERS] [--cores CORES]
 Read in text file(s) and
 
 positional arguments:
-  in_file        Sleuth-style text files with coordinates to be meta-analyzed
+  in_file        Sleuth-style text file(s) with coordinates to be meta-analyzed
                  (separate files for MNI, Talairach).
   out_dir        Absolute or relative path to directory where output (figures
                  and results) will be saved.
@@ -55,11 +55,39 @@ optional arguments:
                      Default is 'z'
   --verbose          If selected, script will narrate its progress. 
 ```
+5. Compare patterns of convergent activation with `nimare-ale-subtraction.py`
+```
+usage: nimare-ale-subtraction.py [-h] [-1 DSET1 [DSET1 ...]]
+                                 [-2 DSET2 [DSET2 ...]] [--out_dir OUT_DIR]
+                                 [--iters ITERS]
+
+Read in text or dataset files and perform an activation likelihood estimation
+subtraction analysis to compare patterns of convergent activation between
+meta-analytic datasets.
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -1 DSET1 [DSET1 ...], --dset1 DSET1 [DSET1 ...]
+                        Sleuth-style text file(s) with coordinates of first
+                        dataset (separate files for MNI, Talairach) OR a
+                        gzipped, pickled file (`.pkl.gz`) containing both MNI
+                        and Talairach coordinateds.
+  -2 DSET2 [DSET2 ...], --dset2 DSET2 [DSET2 ...]
+                        Sleuth-style text files with coordinates tof second
+                        dataset (separate files for MNI, Talairach) OR a
+                        gzipped, pickled file (`.pkl.gz`) containing both MNI
+                        and Talairach coordinateds..
+  --out_dir OUT_DIR     Absolute or relative path to directory where output
+                        (figures and results) will be saved.
+  --iters ITERS         The number of iterations the FWE corrector should run.
+```
 Overall, you can use the code included here to run a meta-analysis and make figures with 3 commands, once you've prepared your Sleuth-style coordinate text files, navigate to the folder in which you've saved the `code` folder and run the following commands in a command line (e.g., Terminal on MacOS):
 ```
 bash code/setup.sh
 python code/nimare-ales.py /path/to/sleuth_file-mni.txt /path/to/sleuth_file-tal.txt /path/to/output-directory
 python code/make-figs.py /path/to/output-directory/results /path/to/output-directory/figures
+# optionally
+python code/nimare-ale-subtraction.py -1 /path/to/dataset1_file [/path/to/dataset1_file2 ...] -2 /path/to/dataset2_file [/path/to/dataset2_file2 ...] /path/to/output-directory
 ```
 All other arguments are optional and without them, you'll run a perfectly good ALE meta-analysis. Make sure you replace all the `path/to/...` with file paths to your text files and to your output directory, respectively.
 

code/make-figs.py

@@ -22,14 +22,19 @@
                     \'z\' - axial, \'ortho\' - three cuts are performed in orthogonal
                     directions, \'tiled\' - three cuts are performed
                     and arranged in a 2x2 grid. Default is \'z\'''')
+parser.add_argument('--subtraction', action='store_true', 
+                    help='If selected, script will use contrasting colormaps.')
 parser.add_argument('--verbose', action='store_true', 
                     help='If selected, script will narrate its progress.')
 args = parser.parse_args()
 
 if args.cmaps:
     cmaps = args.cmaps
 else:
-    cmaps = ['PuRd', 'BuPu','GnBu', 'BuGn', 'OrRd']
+    if args.subtraction:
+        cmaps = ['PuOr', 'RdBu_r', 'PRGn_r', 'PiYG', 'BrBG', 'RdGy', ]
+    else:
+        cmaps = ['PuRd', 'BuPu','GnBu', 'BuGn', 'OrRd']
 
 if args.orient:
     orientation = args.orient
@@ -44,7 +49,7 @@
 map_dir = args.map_dir
 out_dir = args.out_dir
 
-z_maps = glob('{0}/*z*voxel*.nii.gz'.format(map_dir))
+z_maps = glob('{0}/*z*.nii.gz'.format(map_dir))
 
 #make the surface + slices plots!
 fsaverage = datasets.fetch_surf_fsaverage()
@@ -69,29 +74,30 @@
 else:
     cuts = None
 
+thresh = 1.5
 
 for i in np.arange(0, len(z_maps)):
     basename = z_maps[i].split('/')[-1][:-7]
-    g = plot_stat_map(z_maps[i], colorbar=False, threshold=1.5,
+    g = plot_stat_map(z_maps[i], colorbar=True, threshold=thresh,
                       display_mode='z', cut_coords=cuts,
                       cmap=cmaps[i], draw_cross=False)
     g.savefig('{0}/{1}-slices.png'.format(out_dir,basename), dpi=300)
     r_texture = surface.vol_to_surf(z_maps[i], fsaverage.pial_right)
     l_texture = surface.vol_to_surf(z_maps[i], fsaverage.pial_left)
     h = plot_surf_stat_map(fsaverage.pial_right, r_texture,
-                           colorbar=False, cmap=cmaps[i], threshold=1.5,
+                           colorbar=False, cmap=cmaps[i], threshold=thresh,
                            bg_map=fsaverage.sulc_right, view='medial')
     h.savefig('{0}/{1}-surf-RL.png'.format(out_dir,basename), dpi=300)
     h = plot_surf_stat_map(fsaverage.pial_right, r_texture,
-                           colorbar=False, cmap=cmaps[i], threshold=1.5,
+                           colorbar=False, cmap=cmaps[i], threshold=thresh,
                            bg_map=fsaverage.sulc_right, view='lateral')
     h.savefig('{0}/{1}-surf-RM.png'.format(out_dir,basename), dpi=300)
     h = plot_surf_stat_map(fsaverage.pial_left, l_texture,
-                           colorbar=False, cmap=cmaps[i], threshold=1.5,
+                           colorbar=False, cmap=cmaps[i], threshold=thresh,
                            bg_map=fsaverage.sulc_left, view='medial')
     h.savefig('{0}/{1}-surf-LM.png'.format(out_dir,basename), dpi=300)
     h = plot_surf_stat_map(fsaverage.pial_left, l_texture,
-                           colorbar=False, cmap=cmaps[i], threshold=1.5,
+                           colorbar=False, cmap=cmaps[i], threshold=thresh,
                            bg_map=fsaverage.sulc_left, view='lateral')
     h.savefig('{0}/{1}-surf-LL.png'.format(out_dir,basename), dpi=300)
 

code/nimare-ale-subtraction.py

@@ -7,6 +7,7 @@
 from nilearn.plotting import plot_stat_map, plot_surf_stat_map
 from nilearn.surface import vol_to_surf
 from nilearn.datasets import fetch_surf_fsaverage
+from nimare.dataset import Dataset
 
 print('NiMARE version:', nim.__version__)
 print('Nibabel version:', nib.__version__)
@@ -22,11 +23,11 @@
                     help='The number of iterations the FWE corrector should run.')
 args = parser.parse_args()
 
-sleuth1 = args.dset1
-print('dset1:', sleuth1)
-sleuth2 = args.dset2
-print('dset2:', sleuth2)
-basename = sleuth1[0].split('/')[-1][:-4] + '+' + sleuth2[0].split('/')[-1][:-4]
+dset1 = args.dset1
+print('dset1:', dset1)
+dset2 = args.dset2
+print('dset2:', dset2)
+basename = dset1[0].split('/')[-1][:-4] + '+' + dset2[0].split('/')[-1][:-4]
 print(basename)
 out_dir = args.out_dir
 today = date.today().strftime('%d_%m_%Y')
@@ -40,11 +41,25 @@
 else:
     n_iters = 10000
 
-print(sleuth1, '\n', sleuth2, '\n', basename, '\n', out_dir, '\n', today, '\nniters =', n_iters)
+print(dset1, '\n', dset2, '\n', basename, '\n', out_dir, '\n', today, '\nniters =', n_iters)
 
-dset1 = nim.io.convert_sleuth_to_dataset(sleuth1)
-dset2 = nim.io.convert_sleuth_to_dataset(sleuth2)
+if any('.txt' in string for string in dset1):
+    print('Converting Sleuth coordinate file to NiMARE dataset...')
+    dset1 = nim.io.convert_sleuth_to_dataset(dset1)
 
+elif any('.pkl' in string for string in dset1):
+    print('Converting pickled dataset file to NiMARE dataset...')
+    dset1 = Dataset.load(dset1[0])
+
+if any('.txt' in string for string in dset2):
+    print('Converting Sleuth coordinate file to NiMARE dataset...')
+    dset2 = nim.io.convert_sleuth_to_dataset(dset2)
+
+elif any('.pkl' in string for string in dset2):
+    print('Converting pickled dataset file to NiMARE dataset...')
+    dset2 = Dataset.load(dset2[0])
+
+print('Starting subtraction analysis...')
 meta = nim.meta.ale.ALESubtraction(n_iters=n_iters)
 result = meta.fit(dset1, dset2)
 print(result.maps)

code/setup.sh

@@ -1,4 +1,4 @@
 pip install argparse
 pip install nilearn
-pip install nimare
-pip install nibabel
+pip install nimare==0.0.3
+pip install nibabel==3.1.1

code/subset-nimare-ds.py

@@ -0,0 +1,32 @@
+import os
+import nimare as nim
+from nimare.dataset import Dataset
+from datetime import date
+
+today = date.today().strftime('%d_%m_%Y')
+
+constructs = ['Affiliation', 'Others', 'Self', 'Soc_Comm']
+root_dir = '/Users/kbottenh/Dropbox/Projects/metas/ro-flux'
+
+all_dat = [f'{root_dir}/data/ALL_Talairach.txt', f'{root_dir}/data/ALL_MNI.txt']
+
+all_dset = nim.io.convert_sleuth_to_dataset(all_dat)
+
+for construct in constructs:
+    print(construct)
+    con_dat = [os.path.join(root_dir, 'data', f'{construct}_Pure_Talairach.txt'), 
+               os.path.join(root_dir, 'data', f'{construct}_Pure_MNI.txt')]
+
+    con_dset = nim.io.convert_sleuth_to_dataset(con_dat)
+    con_dset.save(f'{root_dir}/data/{construct}_all.pkl.gz')
+
+    non_construct_ids = list(set(all_dset.ids) - set(con_dset.ids))
+    non_construct_dset = all_dset.slice(non_construct_ids)
+
+    non_construct_dset.save(f'{root_dir}/data/Not_{construct}_all.pkl.gz')
+
+    #non_construct_ids = list(set(tal_dset.ids) - set(tal_construct.ids))
+    #tal_non_construct = mni_dset.slice(non_construct_ids)
+
+    #tal_non_construct.save(f'{root_dir}/data/Not_{construct}_Talairach.pkl', compress=False)
+