Add support for the concordant mode test in Fisher's and Stouffer's…

… estimators (#884) * Add support for the `concordant` mode test in Fisher's and Stouffer's estimators * Update figures.py * Update figures.py * Update base.py * Update figures.py * Update figures.py * Set symmetric_cbar to True in the example gallery * Update figures.py * Update references.bib * Update ibma.py * Update ibma.py
neurostuff · May 28, 2024 · d95c18b · d95c18b
1 parent 80cc7a5
commit d95c18b
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Showing 15 changed files with 109 additions and 9 deletions.
diff --git a/examples/02_meta-analyses/01_plot_cbma.py b/examples/02_meta-analyses/01_plot_cbma.py
@@ -13,6 +13,7 @@
 For a more detailed introduction to the elements of a coordinate-based
 meta-analysis, see other stuff.
 """
+
 ###############################################################################
 # Load Dataset
 # -----------------------------------------------------------------------------
@@ -57,13 +58,15 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.1,
 )
 plot_stat_map(
     cres.get_map("z_level-voxel_corr-FWE_method-montecarlo"),
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.1,
 )
 
@@ -87,12 +90,14 @@
     results.get_map("z_desc-uniformity"),
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.1,
 )
 plot_stat_map(
     cres.get_map("z_desc-uniformitySize_level-cluster_corr-FWE_method-montecarlo"),
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.1,
 )
 
@@ -117,13 +122,15 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.1,
 )
 plot_stat_map(
     cres.get_map("z_desc-size_level-cluster_corr-FWE_method-montecarlo"),
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.1,
 )
 
@@ -148,13 +155,15 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.1,
 )
 plot_stat_map(
     cres.get_map("z_desc-size_level-cluster_corr-FWE_method-montecarlo"),
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.1,
 )
 
@@ -200,6 +209,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.1,
 )
 

diff --git a/examples/02_meta-analyses/02_plot_ibma.py b/examples/02_meta-analyses/02_plot_ibma.py
@@ -13,6 +13,7 @@
 For a more detailed introduction to the elements of an image-based
 meta-analysis, see other stuff.
 """
+
 from nilearn.plotting import plot_stat_map
 
 ###############################################################################
@@ -56,6 +57,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 print("Description:")
@@ -74,6 +76,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 print("Description:")
@@ -94,6 +97,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 print("Description:")
@@ -115,6 +119,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 corrector = FWECorrector(method="montecarlo", n_iters=100, n_cores=1)
@@ -125,6 +130,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 print("Description:")
@@ -145,6 +151,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 print("Description:")
@@ -165,6 +172,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 print("Description:")
@@ -185,6 +193,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 print("Description:")

diff --git a/examples/02_meta-analyses/03_plot_kernel_transformers.py b/examples/02_meta-analyses/03_plot_kernel_transformers.py
@@ -17,6 +17,7 @@
 This example provides an introduction to the ``KernelTransformer`` class and
 a tour of available types.
 """
+
 # sphinx_gallery_thumbnail_number = 2
 import os
 
@@ -99,6 +100,7 @@
         annotate=False,
         colorbar=False,
         cmap="RdBu_r",
+        symmetric_cbar=True,
     )
 
 ###############################################################################
@@ -115,6 +117,7 @@
     title="MKDA",
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 ###############################################################################
@@ -131,6 +134,7 @@
     title="KDA",
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 ###############################################################################
@@ -150,4 +154,5 @@
     title="ALE",
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
diff --git a/examples/02_meta-analyses/05_plot_correctors.py b/examples/02_meta-analyses/05_plot_correctors.py
@@ -8,6 +8,7 @@
 
 Here we take a look at multiple comparisons correction in meta-analyses.
 """
+
 from pprint import pprint
 
 import matplotlib.pyplot as plt
@@ -119,6 +120,7 @@
         cres.get_map(map_name),
         draw_cross=False,
         cmap="RdBu_r",
+        symmetric_cbar=True,
         threshold=0.5,
         cut_coords=[0, 0, -8],
         figure=fig,
@@ -157,6 +159,7 @@
     cres.get_map("z"),
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.5,
     cut_coords=[0, 0, -8],
     figure=fig,
@@ -167,6 +170,7 @@
     cres.get_map("z_corr-FDR_method-indep"),
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.5,
     cut_coords=[0, 0, -8],
     figure=fig,

diff --git a/examples/02_meta-analyses/06_plot_compare_ibma_and_cbma.py b/examples/02_meta-analyses/06_plot_compare_ibma_and_cbma.py
@@ -11,6 +11,7 @@
 Collection of NIDM-Results packs downloaded from Neurovault collection 1425,
 uploaded by Dr. Camille Maumet.
 """
+
 import os
 
 import pandas as pd
@@ -53,6 +54,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 ###############################################################################
@@ -65,6 +67,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 ###############################################################################

diff --git a/examples/02_meta-analyses/07_macm.py b/examples/02_meta-analyses/07_macm.py
@@ -12,6 +12,7 @@
 analysis in which task-independent "connectivity" is assessed by selecting
 studies within a larger database based on locations of report coordinates.
 """
+
 import nibabel as nib
 import numpy as np
 from nilearn import datasets, image, plotting
@@ -71,6 +72,7 @@
     threshold=3.09,
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 ###############################################################################
@@ -85,4 +87,4 @@
 xyz = dset.coordinates[["x", "y", "z"]].values
 scale = SCALE(xyz=xyz, n_iters=10000, n_cores=1, kernel__n=20)
 results = scale.fit(dset_sel)
-plotting.plot_stat_map(results.get_map("z"), draw_cross=False, cmap="RdBu_r")
+plotting.plot_stat_map(results.get_map("z"), draw_cross=False, cmap="RdBu_r", symmetric_cbar=True)
diff --git a/examples/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py b/examples/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py
@@ -19,6 +19,7 @@
 5. Compare the two samples with a subtraction analysis.
 6. Compare the two within-sample meta-analyses with a conjunction analysis.
 """
+
 import os
 from pathlib import Path
 
@@ -71,6 +72,7 @@
     title="Semantic knowledge",
     threshold=2.326,  # cluster-level p < .01, one-tailed
     cmap="RdBu_r",
+    symmetric_cbar=True,
     vmax=4,
     axes=axes[0],
     figure=fig,
@@ -86,6 +88,7 @@
     title="Semantic relatedness",
     threshold=2.326,  # cluster-level p < .01, one-tailed
     cmap="RdBu_r",
+    symmetric_cbar=True,
     vmax=4,
     axes=axes[1],
     figure=fig,
@@ -189,6 +192,7 @@
     title="Conjunction",
     threshold=2.326,  # cluster-level p < .01, one-tailed
     cmap="RdBu_r",
+    symmetric_cbar=True,
     vmax=4,
 )
 

diff --git a/examples/02_meta-analyses/10_plot_cbma_workflow.py b/examples/02_meta-analyses/10_plot_cbma_workflow.py
@@ -11,6 +11,7 @@
 This tutorial will walk you through using a CBMA workflow function which puts together
 the fundamental steps of a CBMA meta-analysis.
 """
+
 import os
 from pathlib import Path
 
@@ -62,6 +63,7 @@
     display_mode="z",
     threshold=1.65,  # voxel_thresh p < .05, one-tailed
     cmap="RdBu_r",
+    symmetric_cbar=True,
     vmax=4,
 )
 plt.show()