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diagnostics.py
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diagnostics.py
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"""Methods for diagnosing problems in meta-analytic datasets or analyses."""
import copy
import logging
from abc import abstractmethod
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from nilearn import input_data
from nilearn.reporting import get_clusters_table
from scipy.spatial.distance import cdist
from tqdm.auto import tqdm
from nimare.base import NiMAREBase
from nimare.utils import _check_ncores, get_masker, mm2vox, tqdm_joblib
LGR = logging.getLogger(__name__)
class Diagnostics(NiMAREBase):
"""Base class for diagnostic methods.
.. versionchanged:: 0.1.0
- Transform now returns a MetaResult object.
.. versionadded:: 0.0.14
Parameters
----------
target_image : :obj:`str`, optional
The meta-analytic map for which clusters will be characterized.
The default is z because log-p will not always have value of zero for non-cluster voxels.
voxel_thresh : :obj:`float` or None, optional
An optional voxel-level threshold that may be applied to the ``target_image`` to define
clusters. This can be None if the ``target_image`` is already thresholded
(e.g., a cluster-level corrected map).
Default is None.
cluster_threshold : :obj:`int` or None, optional
Cluster size threshold, in :term:`voxels<voxel>`.
If None, then no cluster size threshold will be applied. Default=None.
n_cores : :obj:`int`, optional
Number of cores to use for parallelization.
If <=0, defaults to using all available cores.
Default is 1.
"""
def __init__(
self,
target_image="z_desc-size_level-cluster_corr-FWE_method-montecarlo",
voxel_thresh=None,
cluster_threshold=None,
n_cores=1,
):
self.target_image = target_image
self.voxel_thresh = voxel_thresh
self.cluster_threshold = cluster_threshold
self.n_cores = _check_ncores(n_cores)
@abstractmethod
def _transform(self, expid, label_map, result):
"""Apply transform to study ID and label map.
Must return a 1D array with the contribution of `expid` in each cluster of `label_map`.
"""
def transform(self, result):
"""Apply the analysis to a MetaResult.
Parameters
----------
result : :obj:`~nimare.results.MetaResult`
A MetaResult produced by a coordinate- or image-based meta-analysis.
Returns
-------
:obj:`~nimare.results.MetaResult`
Results of Diagnostics fitting.
Notes
-----
This method adds two new keys to ``maps`` and ``tables`` attributes of the
MetaResult object.
- ``<target_image>_diag-<Jackknife|FocusCounter>_tab-counts`` :
:obj:`pandas.DataFrame` or None.
A DataFrame with information about relative contributions of each experiment
to each cluster in the thresholded map.
There is one row for each experiment.
There is one column for each cluster, with column names being
``PostiveTail``/``NegativeTail`` indicating the sign (+/-) of the cluster's
statistical values, plus an integer indicating the cluster's associated value
in the ``label_maps[0]``/``label_maps[1]`` output.
If no clusters are found or a pairwise Estimator was used, ``None`` is returned.
- ``<target_image>_tab-clust`` : :obj:`pandas.DataFrame`
A DataFrame with information about each cluster.
There is one row for each cluster.
The columns in this table include: ``Cluster ID`` (the cluster id, plus a letter
for subpeaks only), ``X``/``Y``/``Z`` (coordinate for the center of mass),
``Max Stat`` (statistical value of the peak), and ``Cluster Size (mm3)``
(the size of the cluster, in cubic millimeters).
If no clusters are found, this table will be empty.
- ``label_<target_image>_tail-<positive|negative>`` : :obj:`numpy.ndarray`
Label maps.
Each cluster in the map has a single value, which corresponds to the cluster number
of the column name in ``contribution_table``.
If target_image has negative values after thresholding, first and second maps
correspond to positive and negative tails.
If no clusters are found, this list will be empty.
"""
masker = result.estimator.masker
diag_name = self.__class__.__name__
none_contribution_table = False
if not hasattr(result.estimator, "dataset"):
LGR.warning(
"MetaResult was not generated by an Estimator with a `dataset` attribute. "
"This may be because the Estimator was a pairwise Estimator. The "
"Jackknife/FocusCounter method does not currently work with pairwise Estimators. "
"The ``contribution_table`` will be returned as ``None``."
)
none_contribution_table = True
# Collect the thresholded cluster map
if self.target_image in result.maps:
target_img = result.get_map(self.target_image, return_type="image")
else:
available_maps = [f"'{m}'" for m in result.maps.keys()]
raise ValueError(
f"Target image ('{self.target_image}') not present in result. "
f"Available maps in result are: {', '.join(available_maps)}."
)
# Get clusters table and label maps
stat_threshold = self.voxel_thresh or 0
two_sided = (target_img.get_fdata() < 0).any()
clusters_table, label_maps = get_clusters_table(
target_img,
stat_threshold,
self.cluster_threshold,
two_sided=two_sided,
return_label_maps=True,
)
n_clusters = clusters_table.shape[0]
if n_clusters == 0:
LGR.warning("No clusters found")
else:
LGR.info(f"{n_clusters} clusters found")
# Make sure cluster IDs are strings
clusters_table = clusters_table.astype({"Cluster ID": "str"})
# Rename the clusters_table cluster IDs to match the contribution table columns
clusters_table["Cluster ID"] = [
f"PositiveTail {row['Cluster ID']}"
if row["Peak Stat"] > 0
else f"NegativeTail {row['Cluster ID']}"
for _, row in clusters_table.iterrows()
]
# Define bids-like names for tables and maps
image_name = "_".join(self.target_image.split("_")[1:])
image_name = "_" + image_name if image_name else image_name
clusters_table_name = f"{self.target_image}_tab-clust"
contribution_table_name = f"{self.target_image}_diag-{diag_name}_tab-counts"
label_map_names = (
[f"label{image_name}_tail-positive", f"label{image_name}_tail-negative"]
if len(label_maps) == 2
else [f"label{image_name}_tail-positive"]
)
# Check number of clusters
if (n_clusters == 0) or none_contribution_table:
result.tables[clusters_table_name] = clusters_table
result.tables[contribution_table_name] = None
result.maps[label_map_names[0]] = None
result.diagnostics.append(self)
return result
# Use study IDs in inputs_ instead of dataset, because we don't want to try fitting the
# estimator to a study that might have been filtered out by the estimator's criteria.
meta_ids = result.estimator.inputs_["id"]
rows = list(meta_ids)
contribution_tables = []
signs = ["PositiveTail", "NegativeTail"] if len(label_maps) == 2 else ["PositiveTail"]
for sign, label_map in zip(signs, label_maps):
cluster_ids = sorted(list(np.unique(label_map.get_fdata())[1:]))
# Create contribution table
cols = [f"{sign} {int(c_id)}" for c_id in cluster_ids]
contribution_table = pd.DataFrame(index=rows, columns=cols)
contribution_table.index.name = "id"
with tqdm_joblib(tqdm(total=len(meta_ids))):
contributions = Parallel(n_jobs=self.n_cores)(
delayed(self._transform)(expid, label_map, result) for expid in meta_ids
)
# Add results to table
for expid, stat_prop_values in zip(meta_ids, contributions):
contribution_table.loc[expid] = stat_prop_values
contribution_tables.append(contribution_table.reset_index())
# Concat PositiveTail and NegativeTail tables
contribution_table = pd.concat(contribution_tables, ignore_index=True, sort=False)
# Save tables and maps to result
diag_tables_dict = {
clusters_table_name: clusters_table,
contribution_table_name: contribution_table,
}
diag_maps_dict = {
label_map_name: np.squeeze(masker.transform(label_map))
for label_map_name, label_map in zip(label_map_names, label_maps)
}
result.tables.update(diag_tables_dict)
result.maps.update(diag_maps_dict)
# Add diagnostics class to result, since more than one can be run
result.diagnostics.append(self)
return result
class Jackknife(Diagnostics):
"""Run a jackknife analysis on a meta-analysis result.
.. versionchanged:: 0.0.14
* New parameter: `cluster_threshold`.
* Return clusters table.
.. versionchanged:: 0.0.13
Change cluster neighborhood from faces+edges to faces, to match Nilearn.
.. versionadded:: 0.0.11
Notes
-----
This analysis characterizes the relative contribution of each experiment in a meta-analysis
to the resulting clusters by looping through experiments, calculating the Estimator's summary
statistic for all experiments *except* the target experiment, dividing the resulting test
summary statistics by the summary statistics from the original meta-analysis, and finally
averaging the resulting proportion values across all voxels in each cluster.
Warnings
--------
Pairwise meta-analyses, like ALESubtraction and MKDAChi2, are not yet supported in this
method.
"""
def _transform(self, expid, label_map, result):
"""Apply transform to study ID and label map.
Parameters
----------
expid : :obj:`str`
Study ID.
label_map : :class:`nibabel.Nifti1Image`
The cluster label map image.
result : :obj:`~nimare.results.MetaResult`
A MetaResult produced by a coordinate- or image-based meta-analysis.
Returns
-------
stat_prop_values : 1D :obj:`numpy.ndarray`
1D array with the contribution of `expid` in each cluster of `label_map`.
"""
# We need to copy the estimator because it will otherwise overwrite the original version
# with one missing a study in its inputs.
estimator = copy.deepcopy(result.estimator)
dset = estimator.dataset
original_masker = estimator.masker
all_ids = estimator.inputs_["id"]
# Mask using a labels masker, so that we can easily get the mean value for each cluster
cluster_masker = input_data.NiftiLabelsMasker(label_map)
cluster_masker.fit(label_map)
# CBMAs have "stat" maps, while most IBMAs have "est" maps.
# Fisher's and Stouffer's only have "z" maps though.
if "est" in result.maps:
target_value_map = "est"
elif "stat" in result.maps:
target_value_map = "stat"
else:
target_value_map = "z"
stat_values = result.get_map(target_value_map, return_type="array")
# Fit Estimator to all studies except the target study
other_ids = [id_ for id_ in all_ids if id_ != expid]
temp_dset = dset.slice(other_ids)
temp_result = estimator.fit(temp_dset)
# Collect the target values (e.g., ALE values) from the N-1 meta-analysis
temp_stat_img = temp_result.get_map(target_value_map, return_type="image")
temp_stat_vals = np.squeeze(original_masker.transform(temp_stat_img))
# Voxelwise proportional reduction of each statistic after removal of the experiment
with np.errstate(divide="ignore", invalid="ignore"):
prop_values = np.true_divide(temp_stat_vals, stat_values)
prop_values = np.nan_to_num(prop_values)
voxelwise_stat_prop_values = 1 - prop_values
stat_prop_img = original_masker.inverse_transform(voxelwise_stat_prop_values)
stat_prop_values = cluster_masker.transform(stat_prop_img)
return stat_prop_values.flatten()
class FocusCounter(Diagnostics):
"""Run a focus-count analysis on a coordinate-based meta-analysis result.
.. versionchanged:: 0.0.14
* New parameter: `cluster_threshold`.
* Return clusters table.
.. versionchanged:: 0.0.13
Change cluster neighborhood from faces+edges to faces, to match Nilearn.
.. versionadded:: 0.0.12
Notes
-----
This analysis characterizes the relative contribution of each experiment in a meta-analysis
to the resulting clusters by counting the number of peaks from each experiment that fall within
each significant cluster.
Warnings
--------
This method only works for coordinate-based meta-analyses.
Pairwise meta-analyses, like ALESubtraction and MKDAChi2, are not yet supported in this
method.
"""
def _transform(self, expid, label_map, result):
"""Apply transform to study ID and label map.
Parameters
----------
expid : :obj:`str`
Study ID.
label_map : :class:`nibabel.Nifti1Image`
The cluster label map image.
result : :obj:`~nimare.results.MetaResult`
A MetaResult produced by a coordinate- or image-based meta-analysis.
Returns
-------
stat_prop_values : 1D :obj:`numpy.ndarray`
1D array with the contribution of `expid` in each cluster of `label_map`.
"""
affine = label_map.affine
label_arr = label_map.get_fdata()
clust_ids = sorted(list(np.unique(label_arr)[1:]))
coordinates_df = result.estimator.inputs_["coordinates"]
coords = coordinates_df.loc[coordinates_df["id"] == expid]
ijk = mm2vox(coords[["x", "y", "z"]], affine)
focus_counts = []
for c_val in clust_ids:
cluster_mask = label_arr == c_val
cluster_idx = np.vstack(np.where(cluster_mask))
distances = cdist(cluster_idx.T, ijk)
distances = distances < 1
distances = np.any(distances, axis=0)
n_included_voxels = np.sum(distances)
focus_counts.append(n_included_voxels)
return np.array(focus_counts)
class FocusFilter(NiMAREBase):
"""Remove coordinates outside of the Dataset's mask from the Dataset.
.. versionadded:: 0.0.13
Parameters
----------
mask : :obj:`str`, :class:`~nibabel.nifti1.Nifti1Image`, \
:class:`~nilearn.maskers.NiftiMasker` or similar, or None, optional
Mask(er) to use. If None, uses the masker of the Dataset provided in ``transform``.
Notes
-----
This filter removes any coordinates outside of the brain mask.
It does not remove studies without coordinates in the brain mask, since a Dataset does not
need to have coordinates for all studies (e.g., some may only have images).
"""
def __init__(self, mask=None):
if mask is not None:
mask = get_masker(mask)
self.masker = mask
def transform(self, dataset):
"""Apply the filter to a Dataset.
Parameters
----------
dataset : :obj:`~nimare.dataset.Dataset`
The Dataset to filter.
Returns
-------
dataset : :obj:`~nimare.dataset.Dataset`
The filtered Dataset.
"""
masker = self.masker or dataset.masker
# Get matrix indices for in-brain voxels in the mask
mask_ijk = np.vstack(np.where(masker.mask_img.get_fdata())).T
# Get matrix indices for Dataset coordinates
dset_xyz = dataset.coordinates[["x", "y", "z"]].values
# mm2vox automatically rounds the coordinates
dset_ijk = mm2vox(dset_xyz, masker.mask_img.affine)
# Check if each coordinate in Dataset is within the mask
# If it is, log that coordinate in keep_idx
keep_idx = [
i
for i, coord in enumerate(dset_ijk)
if len(np.where((mask_ijk == coord).all(axis=1))[0])
]
LGR.info(
f"{dset_ijk.shape[0] - len(keep_idx)}/{dset_ijk.shape[0]} coordinates fall outside of "
"the mask. Removing them."
)
# Only retain coordinates inside the brain mask
dataset.coordinates = dataset.coordinates.iloc[keep_idx]
return dataset