class ABA(Paths):
"""
This class handles interaction with the Allen Brain Atlas datasets and APIs to get structure trees,
experimental metadata and results, tractography data etc.
"""
# useful vars for analysis
excluded_regions = ["fiber tracts"]
resolution = 25
def __init__(self, projection_metric = "projection_energy", base_dir=None, **kwargs):
"""
Set up file paths and Allen SDKs
:param base_dir: path to directory to use for saving data (default value None)
:param path_fiprojection_metricle: - str, metric to quantify the strength of projections from the Allen Connectome. (default value 'projection_energy')
:param kwargs: can be used to pass path to individual data folders. See brainrender/Utils/paths_manager.py
"""
Paths.__init__(self, base_dir=base_dir, **kwargs)
self.projection_metric = projection_metric
# get mouse connectivity cache and structure tree
self.mcc = MouseConnectivityCache(manifest_file=os.path.join(self.mouse_connectivity_cache, "manifest.json"))
self.structure_tree = self.mcc.get_structure_tree()
# get ontologies API and brain structures sets
self.oapi = OntologiesApi()
self.get_structures_sets()
# get reference space
self.space = ReferenceSpaceApi()
self.spacecache = ReferenceSpaceCache(
manifest=os.path.join(self.annotated_volume, "manifest.json"), # downloaded files are stored relative to here
resolution=self.resolution,
reference_space_key="annotation/ccf_2017" # use the latest version of the CCF
)
self.annotated_volume, _ = self.spacecache.get_annotation_volume()
# mouse connectivity API [used for tractography]
self.mca = MouseConnectivityApi()
# Get tree search api
self.tree_search = TreeSearchApi()
# Get some metadata about experiments
self.all_experiments = self.mcc.get_experiments(dataframe=True)
self.strains = sorted([x for x in set(self.all_experiments.strain) if x is not None])
self.transgenic_lines = sorted(set([x for x in set(self.all_experiments.transgenic_line) if x is not None]))
####### GET EXPERIMENTS DATA
def get_structures_sets(self):
"""
Get the Allen's structure sets.
"""
summary_structures = self.structure_tree.get_structures_by_set_id([167587189]) # main summary structures
summary_structures = [s for s in summary_structures if s["acronym"] not in self.excluded_regions]
self.structures = pd.DataFrame(summary_structures)
# Other structures sets
try:
all_sets = pd.DataFrame(self.oapi.get_structure_sets())
except:
print("Could not retrieve data, possibly because there is no internet connection.")
else:
sets = ["Summary structures of the pons", "Summary structures of the thalamus",
"Summary structures of the hypothalamus", "List of structures for ABA Fine Structure Search",
"Structures representing the major divisions of the mouse brain", "Summary structures of the midbrain", "Structures whose surfaces are represented by a precomputed mesh"]
self.other_sets = {}
for set_name in sets:
set_id = all_sets.loc[all_sets.description == set_name].id.values[0]
self.other_sets[set_name] = pd.DataFrame(self.structure_tree.get_structures_by_set_id([set_id]))
self.all_avaliable_meshes = sorted(self.other_sets["Structures whose surfaces are represented by a precomputed mesh"].acronym.values)
def print_structures_list_to_text(self):
"""
Saves the name of every brain structure for which a 3d mesh (.obj file) is available in a text file.
"""
s = self.other_sets["Structures whose surfaces are represented by a precomputed mesh"].sort_values('acronym')
with open('all_regions.txt', 'w') as o:
for acr, name in zip(s.acronym.values, s['name'].values):
o.write("({}) -- {}\n".format(acr, name))
def load_all_experiments(self, cre=False):
"""
This function downloads all the experimental data from the MouseConnectivityCache and saves the unionized results
as pickled pandas dataframes. The process is slow, but the ammount of disk space necessary to save the data is small,
so it's worth downloading all the experiments at once to speed up subsequent analysis.
:param cre: Bool - data from either wild time or cre mice lines (Default value = False)
"""
if not cre: raise NotImplementedError("Only works for wild type sorry")
# Downloads all experiments from allen brain atlas and saves the results as an easy to read pkl file
for acronym in self.structures.acronym.values:
print("Fetching experiments for : {}".format(acronym))
structure = self.structure_tree.get_structures_by_acronym([acronym])[0]
experiments = self.mcc.get_experiments(cre=cre, injection_structure_ids=[structure['id']])
print(" found {} experiments".format(len(experiments)))
try:
structure_unionizes = self.mcc.get_structure_unionizes([e['id'] for e in experiments],
is_injection=False,
structure_ids=self.structures.id.values,
include_descendants=False)
except: pass
structure_unionizes.to_pickle(os.path.join(self.output_data, "{}.pkl".format(acronym)))
def print_structures(self):
"""
Prints the name of every structure in the structure tree to the console.
"""
acronyms, names = self.structures.acronym.values, self.structures['name'].values
sort_idx = np.argsort(acronyms)
acronyms, names = acronyms[sort_idx], names[sort_idx]
[print("({}) - {}".format(a, n)) for a,n in zip(acronyms, names)]
def experiments_source_search(self, SOI, *args, source=True, **kwargs):
"""
Returns data about experiments whose injection was in the SOI, structure of interest
:param SOI: str, structure of interest. Acronym of structure to use as seed for teh search
:param *args:
:param source: (Default value = True)
:param **kwargs:
"""
"""
list of possible kwargs
injection_structures : list of integers or strings
Integer Structure.id or String Structure.acronym.
target_domain : list of integers or strings, optional
Integer Structure.id or String Structure.acronym.
injection_hemisphere : string, optional
'right' or 'left', Defaults to both hemispheres.
target_hemisphere : string, optional
'right' or 'left', Defaults to both hemispheres.
transgenic_lines : list of integers or strings, optional
Integer TransgenicLine.id or String TransgenicLine.name. Specify ID 0 to exclude all TransgenicLines.
injection_domain : list of integers or strings, optional
Integer Structure.id or String Structure.acronym.
primary_structure_only : boolean, optional
product_ids : list of integers, optional
Integer Product.id
start_row : integer, optional
For paging purposes. Defaults to 0.
num_rows : integer, optional
For paging purposes. Defaults to 2000.
"""
transgenic_id = kwargs.pop('transgenic_id', 0) # id = 0 means use only wild type
primary_structure_only = kwargs.pop('primary_structure_only', True)
if not isinstance(SOI, list): SOI = [SOI]
if source:
injection_structures=SOI
target_domain = None
else:
injection_structures = None
target_domain = SOI
return pd.DataFrame(self.mca.experiment_source_search(injection_structures=injection_structures,
target_domain = target_domain,
transgenic_lines=transgenic_id,
primary_structure_only=primary_structure_only))
def experiments_target_search(self, *args, **kwargs):
"""
:param *args:
:param **kwargs:
"""
return self.experiments_source_search(*args, source=False, **kwargs)
def fetch_experiments_data(self, experiments_id, *args, average_experiments=False, **kwargs):
"""
Get data and metadata for expeirments in the Allen Mouse Connectome project.
:param experiments_id: int, list, np.ndarray with ID of experiments whose data need to be fetched
:param *args:
:param average_experiments: (Default value = False)
:param **kwargs:
"""
if isinstance(experiments_id, np.ndarray):
experiments_id = [int(x) for x in experiments_id]
elif not isinstance(experiments_id, list):
experiments_id = [experiments_id]
if [x for x in experiments_id if not isinstance(x, int)]:
raise ValueError("Invalid experiments_id argument: {}".format(experiments_id))
default_structures_ids = self.structures.id.values
is_injection = kwargs.pop('is_injection', False) # Include only structures that are not injection
structure_ids = kwargs.pop('structure_ids', default_structures_ids) # Pass IDs of structures of interest
hemisphere_ids= kwargs.pop('hemisphere_ids', None) # 1 left, 2 right, 3 both
if not average_experiments:
return pd.DataFrame(self.mca.get_structure_unionizes(experiments_id,
is_injection = is_injection,
structure_ids = structure_ids,
hemisphere_ids = hemisphere_ids))
else:
raise NotImplementedError("Need to find a way to average across experiments")
unionized = pd.DataFrame(self.mca.get_structure_unionizes(experiments_id,
is_injection = is_injection,
structure_ids = structure_ids,
hemisphere_ids = hemisphere_ids))
for regionid in list(set(unionized.structure_id)):
region_avg = unionized.loc[unionized.structure_id == regionid].mean(axis=1)
####### ANALYSIS ON EXPERIMENTAL DATA
def analyze_efferents(self, ROI, projection_metric = None):
"""
Loads the experiments on ROI and looks at average statistics of efferent projections
:param ROI: str, acronym of brain region of interest
:param projection_metric: if None, the default projection metric is used, otherwise pass a string with metric to use (Default value = None)
"""
if projection_metric is None:
projection_metric = self.projection_metric
experiment_data = pd.read_pickle(os.path.join(self.output_data, "{}.pkl".format(ROI)))
experiment_data = experiment_data.loc[experiment_data.volume > self.volume_threshold]
# Loop over all structures and get the injection density
results = {"left":[], "right":[], "both":[], "id":[], "acronym":[], "name":[]}
for target in self.structures.id.values:
target_acronym = self.structures.loc[self.structures.id == target].acronym.values[0]
target_name = self.structures.loc[self.structures.id == target].name.values[0]
exp_target = experiment_data.loc[experiment_data.structure_id == target]
exp_target_hemi = self.hemispheres(exp_target.loc[exp_target.hemisphere_id == 1],
exp_target.loc[exp_target.hemisphere_id == 2],
exp_target.loc[exp_target.hemisphere_id == 3])
proj_energy = self.hemispheres(np.nanmean(exp_target_hemi.left[projection_metric].values),
np.nanmean(exp_target_hemi.right[projection_metric].values),
np.nanmean(exp_target_hemi.both[projection_metric].values)
)
for hemi in self.hemispheres_names:
results[hemi].append(proj_energy._asdict()[hemi])
results["id"].append(target)
results["acronym"].append(target_acronym)
results["name"].append(target_name)
results = pd.DataFrame.from_dict(results).sort_values("right", na_position = "first")
return results
def analyze_afferents(self, ROI, projection_metric = None):
"""[Loads the experiments on ROI and looks at average statistics of afferent projections]
:param ROI: str, acronym of region of itnerest
:param projection_metric: if None, the default projection metric is used, otherwise pass a string with metric to use (Default value = None)
"""
if projection_metric is None:
projection_metric = self.projection_metric
ROI_id = self.structure_tree.get_structures_by_acronym([ROI])[0]["id"]
# Loop over all strctures and get projection towards SOI
results = {"left":[], "right":[], "both":[], "id":[], "acronym":[], "name":[]}
for origin in self.structures.id.values:
origin_acronym = self.structures.loc[self.structures.id == origin].acronym.values[0]
origin_name = self.structures.loc[self.structures.id == origin].name.values[0]
experiment_data = pd.read_pickle(os.path.join(self.output_data, "{}.pkl".format(origin_acronym)))
experiment_data = experiment_data.loc[experiment_data.volume > self.volume_threshold]
exp_target = experiment_data.loc[experiment_data.structure_id == SOI_id]
exp_target_hemi = self.hemispheres(exp_target.loc[exp_target.hemisphere_id == 1], exp_target.loc[exp_target.hemisphere_id == 2], exp_target.loc[exp_target.hemisphere_id == 3])
proj_energy = self.hemispheres(np.nanmean(exp_target_hemi.left[projection_metric].values),
np.nanmean(exp_target_hemi.right[projection_metric].values),
np.nanmean(exp_target_hemi.both[projection_metric].values)
)
for hemi in self.hemispheres_names:
results[hemi].append(proj_energy._asdict()[hemi])
results["id"].append(origin)
results["acronym"].append(origin_acronym)
results["name"].append(origin_name)
results = pd.DataFrame.from_dict(results).sort_values("right", na_position = "first")
return results
####### GET TRACTOGRAPHY AND SPATIAL DATA
def get_projection_tracts_to_target(self, p0=None, **kwargs):
"""
Gets tractography data for all experiments whose projections reach the brain region or location of iterest.
:param p0: list of 3 floats with XYZ coordinates of point to be used as seed (Default value = None)
:param **kwargs:
"""
# check args
if p0 is None:
raise ValueError("Please pass coordinates")
elif isinstance(p0, np.ndarray):
p0 = list(p0)
elif not isinstance(p0, (list, tuple)):
raise ValueError("Invalid argument passed (p0): {}".format(p0))
tract = self.mca.experiment_spatial_search(seed_point=p0, **kwargs)
if isinstance(tract, str):
raise ValueError('Something went wrong with query, query error message:\n{}'.format(tract))
else:
return tract
### OPERATIONS ON STRUCTURE TREES
def get_structure_ancestors(self, regions, ancestors=True, descendants=False):
"""
Get's the ancestors of the region(s) passed as arguments
:param regions: str, list of str with acronums of regions of interest
:param ancestors: if True, returns the ancestors of the region (Default value = True)
:param descendants: if True, returns the descendants of the region (Default value = False)
"""
if not isinstance(regions, list):
struct_id = self.structure_tree.get_structures_by_acronym([regions])[0]['id']
return pd.DataFrame(self.tree_search.get_tree('Structure', struct_id, ancestors=ancestors, descendants=descendants))
else:
ancestors = []
for region in regions:
struct_id = self.structure_tree.get_structures_by_acronym([region])[0]['id']
ancestors.append(pd.DataFrame(self.tree_search.get_tree('Structure', struct_id, ancestors=ancestors, descendants=descendants)))
return ancestors
def get_structure_descendants(self, regions):
return self.get_structure_ancestors(regions, ancestors=False, descendants=True)
def get_structure_from_coordinates(self, p0):
"""
Given a point in the Allen Mouse Brain reference space, returns the brain region that the point is in.
:param p0: list of floats with XYZ coordinates.
"""
voxel = np.round(np.array(p0) / self.resolution).astype(int)
try:
structure_id = self.annotated_volume[voxel[0], voxel[1], voxel[2]]
except:
return None
# Each voxel in the annotation volume is annotated as specifically as possible
structure = self.structure_tree.get_structures_by_id([structure_id])[0]
return structure
def test_notebook(fn_temp_dir):
# coding: utf-8
# ## Mouse Connectivity
#
# This notebook demonstrates how to access and manipulate data in the Allen Mouse Brain Connectivity Atlas. The `MouseConnectivityCache` AllenSDK class provides methods for downloading metadata about experiments, including their viral injection site and the mouse's transgenic line. You can request information either as a Pandas DataFrame or a simple list of dictionaries.
#
# An important feature of the `MouseConnectivityCache` is how it stores and retrieves data for you. By default, it will create (or read) a manifest file that keeps track of where various connectivity atlas data are stored. If you request something that has not already been downloaded, it will download it and store it in a well known location.
#
# Download this notebook in .ipynb format <a href='mouse_connectivity.ipynb'>here</a>.
# In[1]:
from allensdk.core.mouse_connectivity_cache import MouseConnectivityCache
# The manifest file is a simple JSON file that keeps track of all of
# the data that has already been downloaded onto the hard drives.
# If you supply a relative path, it is assumed to be relative to your
# current working directory.
mcc = MouseConnectivityCache(manifest_file='connectivity/mouse_connectivity_manifest.json')
# open up a list of all of the experiments
all_experiments = mcc.get_experiments(dataframe=True)
print("%d total experiments" % len(all_experiments))
# take a look at what we know about an experiment with a primary motor injection
all_experiments.loc[122642490]
# `MouseConnectivityCache` has a method for retrieving the adult mouse structure tree as an `StructureTree` class instance. This is a wrapper around a list of dictionaries, where each dictionary describes a structure. It is principally useful for looking up structures by their properties.
# In[2]:
# pandas for nice tables
import pandas as pd
# grab the StructureTree instance
structure_tree = mcc.get_structure_tree()
# get info on some structures
structures = structure_tree.get_structures_by_name(['Primary visual area', 'Hypothalamus'])
pd.DataFrame(structures)
# As a convenience, structures are grouped in to named collections called "structure sets". These sets can be used to quickly gather a useful subset of structures from the tree. The criteria used to define structure sets are eclectic; a structure set might list:
#
# * structures that were used in a particular project.
# * structures that coarsely partition the brain.
# * structures that bear functional similarity.
#
# or something else entirely. To view all of the available structure sets along with their descriptions, follow this [link](http://api.brain-map.org/api/v2/data/StructureSet/query.json). To see only structure sets relevant to the adult mouse brain, use the StructureTree:
# In[3]:
from allensdk.api.queries.ontologies_api import OntologiesApi
oapi = OntologiesApi()
# get the ids of all the structure sets in the tree
structure_set_ids = structure_tree.get_structure_sets()
# query the API for information on those structure sets
pd.DataFrame(oapi.get_structure_sets(structure_set_ids))
# On the connectivity atlas web site, you'll see that we show most of our data at a fairly coarse structure level. We did this by creating a structure set of ~300 structures, which we call the "summary structures". We can use the structure tree to get all of the structures in this set:
# In[4]:
# From the above table, "Mouse Connectivity - Summary" has id 167587189
summary_structures = structure_tree.get_structures_by_set_id([167587189])
pd.DataFrame(summary_structures)
# This is how you can filter experiments by transgenic line:
# In[5]:
# fetch the experiments that have injections in the isocortex of cre-positive mice
isocortex = structure_tree.get_structures_by_name(['Isocortex'])[0]
cre_cortical_experiments = mcc.get_experiments(cre=True,
injection_structure_ids=[isocortex['id']])
print("%d cre cortical experiments" % len(cre_cortical_experiments))
# same as before, but restrict the cre line
rbp4_cortical_experiments = mcc.get_experiments(cre=[ 'Rbp4-Cre_KL100' ],
injection_structure_ids=[isocortex['id']])
print("%d Rbp4 cortical experiments" % len(rbp4_cortical_experiments))
# ## Structure Signal Unionization
#
# The ProjectionStructureUnionizes API data tells you how much signal there was in a given structure and experiment. It contains the density of projecting signal, volume of projecting signal, and other information. `MouseConnectivityCache` provides methods for querying and storing this data.
# In[6]:
# find wild-type injections into primary visual area
visp = structure_tree.get_structures_by_acronym(['VISp'])[0]
visp_experiments = mcc.get_experiments(cre=False,
injection_structure_ids=[visp['id']])
print("%d VISp experiments" % len(visp_experiments))
structure_unionizes = mcc.get_structure_unionizes([ e['id'] for e in visp_experiments ],
is_injection=False,
structure_ids=[isocortex['id']],
include_descendants=True)
print("%d VISp non-injection, cortical structure unionizes" % len(structure_unionizes))
# In[7]:
structure_unionizes.head()
# This is a rather large table, even for a relatively small number of experiments. You can filter it down to a smaller list of structures like this.
# In[8]:
dense_unionizes = structure_unionizes[ structure_unionizes.projection_density > .5 ]
large_unionizes = dense_unionizes[ dense_unionizes.volume > .5 ]
large_structures = pd.DataFrame(structure_tree.nodes(large_unionizes.structure_id))
print("%d large, dense, cortical, non-injection unionizes, %d structures" % ( len(large_unionizes), len(large_structures) ))
print(large_structures.name)
large_unionizes
# ## Generating a Projection Matrix
# The `MouseConnectivityCache` class provides a helper method for converting ProjectionStructureUnionize records for a set of experiments and structures into a matrix. This code snippet demonstrates how to make a matrix of projection density values in auditory sub-structures for cre-negative VISp experiments.
# In[9]:
import numpy as np
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
visp_experiment_ids = [ e['id'] for e in visp_experiments ]
ctx_children = structure_tree.child_ids( [isocortex['id']] )[0]
pm = mcc.get_projection_matrix(experiment_ids = visp_experiment_ids,
projection_structure_ids = ctx_children,
hemisphere_ids= [2], # right hemisphere, ipsilateral
parameter = 'projection_density')
row_labels = pm['rows'] # these are just experiment ids
column_labels = [ c['label'] for c in pm['columns'] ]
matrix = pm['matrix']
fig, ax = plt.subplots(figsize=(15,15))
heatmap = ax.pcolor(matrix, cmap=plt.cm.afmhot)
# put the major ticks at the middle of each cell
ax.set_xticks(np.arange(matrix.shape[1])+0.5, minor=False)
ax.set_yticks(np.arange(matrix.shape[0])+0.5, minor=False)
ax.set_xlim([0, matrix.shape[1]])
ax.set_ylim([0, matrix.shape[0]])
# want a more natural, table-like display
ax.invert_yaxis()
ax.xaxis.tick_top()
ax.set_xticklabels(column_labels, minor=False)
ax.set_yticklabels(row_labels, minor=False)
# ## Manipulating Grid Data
#
# The `MouseConnectivityCache` class also helps you download and open every experiment's projection grid data volume. By default it will download 25um volumes, but you could also download data at other resolutions if you prefer (10um, 50um, 100um).
#
# This demonstrates how you can load the projection density for a particular experiment. It also shows how to download the template volume to which all grid data is registered. Voxels in that template have been structurally annotated by neuroanatomists and the resulting labels stored in a separate annotation volume image.
# In[10]:
# we'll take this experiment - an injection into the primary somatosensory - as an example
experiment_id = 181599674
# In[11]:
# projection density: number of projecting pixels / voxel volume
pd, pd_info = mcc.get_projection_density(experiment_id)
# injection density: number of projecting pixels in injection site / voxel volume
ind, ind_info = mcc.get_injection_density(experiment_id)
# injection fraction: number of pixels in injection site / voxel volume
inf, inf_info = mcc.get_injection_fraction(experiment_id)
# data mask:
# binary mask indicating which voxels contain valid data
dm, dm_info = mcc.get_data_mask(experiment_id)
template, template_info = mcc.get_template_volume()
annot, annot_info = mcc.get_annotation_volume()
# in addition to the annotation volume, you can get binary masks for individual structures
# in this case, we'll get one for the isocortex
cortex_mask, cm_info = mcc.get_structure_mask(315)
print(pd_info)
print(pd.shape, template.shape, annot.shape)
# Once you have these loaded, you can use matplotlib see what they look like.
# In[12]:
# compute the maximum intensity projection (along the anterior-posterior axis) of the projection data
pd_mip = pd.max(axis=0)
ind_mip = ind.max(axis=0)
inf_mip = inf.max(axis=0)
# show that slice of all volumes side-by-side
f, pr_axes = plt.subplots(1, 3, figsize=(15, 6))
pr_axes[0].imshow(pd_mip, cmap='hot', aspect='equal')
pr_axes[0].set_title("projection density MaxIP")
pr_axes[1].imshow(ind_mip, cmap='hot', aspect='equal')
pr_axes[1].set_title("injection density MaxIP")
pr_axes[2].imshow(inf_mip, cmap='hot', aspect='equal')
pr_axes[2].set_title("injection fraction MaxIP")
# In[13]:
# Look at a slice from the average template and annotation volumes
# pick a slice to show
slice_idx = 264
f, ccf_axes = plt.subplots(1, 3, figsize=(15, 6))
ccf_axes[0].imshow(template[slice_idx,:,:], cmap='gray', aspect='equal', vmin=template.min(), vmax=template.max())
ccf_axes[0].set_title("registration template")
ccf_axes[1].imshow(annot[slice_idx,:,:], cmap='gray', aspect='equal', vmin=0, vmax=2000)
ccf_axes[1].set_title("annotation volume")
ccf_axes[2].imshow(cortex_mask[slice_idx,:,:], cmap='gray', aspect='equal', vmin=0, vmax=1)
ccf_axes[2].set_title("isocortex mask")
# On occasion the TissueCyte microscope fails to acquire a tile. In this case the data from that tile should not be used for analysis. The data mask associated with each experiment can be used to determine which portions of the grid data came from correctly acquired tiles.
#
# In this experiment, a missed tile can be seen in the data mask as a dark warped square. The values in the mask exist within [0, 1], describing the fraction of each voxel that was correctly acquired
# In[14]:
f, data_mask_axis = plt.subplots(figsize=(5, 6))
data_mask_axis.imshow(dm[81, :, :], cmap='hot', aspect='equal', vmin=0, vmax=1)
data_mask_axis.set_title('data mask')
# The ProjectionStructureUnionizes API data tells you how much signal there was in a given structure and experiment. It contains the density of projecting signal, volume of projecting signal, and other information. MouseConnectivityCache provides methods for querying and storing this data. To access to this signal projection data, you must call the `get_structure_unionizes()` method on our MouseConnectivityCache instance.
# For more information on the outputs of `get_structure_unionizes()` please visit <a href="https://alleninstitute.github.io/AllenSDK/unionizes.html">here</a>. For documentation on `get_structure_unionizes()` and other MouseConnectivityCache helper methods, please click <a href="https://alleninstitute.github.io/AllenSDK/allensdk.core.mouse_connectivity_cache.html">here</a>
# In[13]:
# find wild-type injections into primary motor cortex
VISp = structure_tree.get_structures_by_name(['Primary visual area'])[0]
VISp_experiments = mcc.get_experiments(cre=False,
injection_structure_ids=[VISp['id']])
print("%d Primary visual area experiments" % len(VISp_experiments))
structure_unionizes = mcc.get_structure_unionizes(
experiment_ids=[exp['id'] for exp in VISp_experiments],
is_injection=False,
structure_ids=[isocortex['id']],
include_descendants=True)
print("%d Primary visual area non-injection, cortical structure unionizes" %
len(structure_unionizes))
# In[14]:
structure_unionizes.head()
# This is a very large dataframe filled with all the signal projection data for our experiments of interest. We can filter this like any other dataframe. For example, filter the dataframe to only include experiments with a large projection density and volume. For the purposes of this lesson, we will consider any `projection_density` and `volume` greater than 0.5 to be *large*.
# In[15]:
# Only include experiments that have a large projection density
# wild type experiments primary injection at PAG
tmp = pd.DataFrame()
count = 0
for i in wt_pag_df['primary_injection_structure']:
count += 1
tmp[count] = summary_structures_df[summary_structures_df['id'] ==
i]['name'].to_numpy()
tmp = tmp.transpose()
wt_pag_df['primary_injection_name'] = tmp.to_numpy()
pag_prime_inj_wt = wt_pag_df[wt_pag_df['primary_injection_structure'] ==
pag['id']]
# print(summary_structures_df['id']==pag['id'])
structure_unionizes_prime_pag = mcc.get_structure_unionizes(
[e for e in pag_prime_inj_cre['id']],
is_injection=False,
include_descendants=True)
print("%d PAG non-injection, cortical structure unionizes" %
len(structure_unionizes_prime_pag))
dense_unionizes_prime_pag = structure_unionizes_prime_pag[
structure_unionizes_prime_pag.projection_density > .13]
large_unionizes_prime_pag = dense_unionizes_prime_pag[
dense_unionizes_prime_pag.volume > .5]
large_structures_prime_pag = pd.DataFrame(
structure_tree.nodes(large_unionizes_prime_pag.structure_id))
print("%d large, dense, cortical, non-injection unionizes, %d structures" %
(len(large_unionizes_prime_pag), len(large_structures_prime_pag)))
print(large_structures_prime_pag.name)
