The Connectome Annotation Versioning Engine (CAVE) is a suite of tools developed at the Allen Institute and Seung Lab to manage large connectomics data.
Before using any programmatic access to the data, you first need to set up your CAVEclient token.
Most programmatic access to the CAVE services occurs through CAVEclient, a Python client to access various types of data from the online services.
Full documentation for CAVEclient is available here.
To initialize a caveclient, we give it a datastack, which is a name that defines a particular combination of imagery, segmentation, and annotation database. For the MICrONs public data, we use the datastack name minnie65_public.
from caveclient import CAVEclient
datastack_name = 'minnie65_public'
client = CAVEclient(datastack_name)
# Show the description of the datastack
client.info.get_datastack_info()['description']'This is the publicly released version of the minnie65 volume and segmentation. 'Data in CAVE is timestamped and periodically versioned - each (materialization) version corresponds to a specific timestamp. Individual versions are made publicly available. The materialization service provides annotation queries to the dataset. It is available under client.materialize.
Periodic updates are made to the public datastack, which will include updates to the available tables. Some cells will have different pt_root_id because they have undergone proofreading.
It is worth checking the version of the data you are using, and specifying the version for analysis consistency.
[1078, 117, 661, 343, 1181, 795, 943]And these are their associated timestamps (all timestamps are in UTC):
for version in client.materialize.get_versions():
print(f"Version {version}: {client.materialize.get_timestamp(version)}")Version 1078: 2024-06-05 10:10:01.203215+00:00
Version 117: 2021-06-11 08:10:00.215114+00:00
Version 661: 2023-04-06 20:17:09.199182+00:00
Version 343: 2022-02-24 08:10:00.184668+00:00
Version 1181: 2024-09-16 10:10:01.121167+00:00
Version 795: 2023-08-23 08:10:01.404268+00:00
Version 943: 2024-01-22 08:10:01.497934+00:00The most frequent use of the CAVEclient is to query the database for annotations like synapses. All database functions are under the client.materialize property. To see what tables are available, use the get_tables function:
['nucleus_alternative_points',
'allen_column_mtypes_v2',
'bodor_pt_cells',
'aibs_metamodel_mtypes_v661_v2',
'allen_v1_column_types_slanted_ref',
'aibs_column_nonneuronal_ref',
'nucleus_ref_neuron_svm',
'apl_functional_coreg_vess_fwd',
'vortex_compartment_targets',
'baylor_log_reg_cell_type_coarse_v1',
'functional_properties_v3_bcm',
'l5et_column',
'pt_synapse_targets',
'coregistration_auto_phase3_fwd_apl_vess_combined',
'coregistration_manual_v4',
'vortex_manual_myelination_v0',
'synapses_pni_2',
'nucleus_detection_v0',
'vortex_manual_nodes_of_ranvier',
'vortex_astrocyte_proofreading_status',
'bodor_pt_target_proofread',
'nucleus_functional_area_assignment',
'coregistration_auto_phase3_fwd',
'synapse_target_structure',
'proofreading_status_and_strategy',
'aibs_metamodel_celltypes_v661']For each table, you can see the metadata describing that table. For example, let’s look at the nucleus_detection_v0 table:
{'aligned_volume': 'minnie65_phase3',
'created': '2020-11-02T18:56:35.530100',
'id': 45664,
'schema': 'nucleus_detection',
'table_name': 'nucleus_detection_v0',
'valid': True,
'schema_type': 'nucleus_detection',
'user_id': '121',
'description': 'A table of nuclei detections from a nucleus detection model developed by Shang Mu, Leila Elabbady, Gayathri Mahalingam and Forrest Collman. Pt is the centroid of the nucleus detection. id corresponds to the flat_segmentation_source segmentID. Only included nucleus detections of volume>25 um^3, below which detections are false positives, though some false positives above that threshold remain. ',
'notice_text': None,
'reference_table': None,
'flat_segmentation_source': 'precomputed://https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie65/nuclei',
'write_permission': 'PRIVATE',
'read_permission': 'PUBLIC',
'last_modified': '2022-10-25T19:24:28.559914',
'segmentation_source': '',
'pcg_table_name': 'minnie3_v1',
'last_updated': '2024-10-24T22:00:00.145632',
'voxel_resolution': [4.0, 4.0, 40.0]}You get a dictionary of values. Two fields are particularly important: the description, which offers a text description of the contents of the table and voxel_resolution which defines how the coordinates in the table are defined, in nm/voxel.
You can also find a semantic description of the most commonly used tables at the Annotation Tables page.
To get the contents of a table, use the query_table function. This will return the whole contents of a table without any filtering, up to for a maximum limit of 200,000 rows. The table is returned as a Pandas DataFrame and you can immediately use standard Pandas function on it.
| id | created | superceded_id | valid | volume | pt_supervoxel_id | pt_root_id | pt_position | bb_start_position | bb_end_position | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 730537 | 2020-09-28 22:40:41.780734+00:00 | NaN | t | 32.307937 | 0 | 0 | [381312, 273984, 19993] | [nan, nan, nan] | [nan, nan, nan] |
| 1 | 373879 | 2020-09-28 22:40:41.781788+00:00 | NaN | t | 229.045043 | 96218056992431305 | 864691136090135607 | [228816, 239776, 19593] | [nan, nan, nan] | [nan, nan, nan] |
| 2 | 601340 | 2020-09-28 22:40:41.782714+00:00 | NaN | t | 426.138010 | 0 | 0 | [340000, 279152, 20946] | [nan, nan, nan] | [nan, nan, nan] |
| 3 | 201858 | 2020-09-28 22:40:41.783784+00:00 | NaN | t | 93.753836 | 84955554103121097 | 864691135373893678 | [146848, 213600, 26267] | [nan, nan, nan] | [nan, nan, nan] |
| 4 | 600774 | 2020-09-28 22:40:41.785273+00:00 | NaN | t | 135.189791 | 0 | 0 | [339120, 276112, 19442] | [nan, nan, nan] | [nan, nan, nan] |
While most tables are small enough to be returned in full, the synapse table has hundreds of millions of rows and is too large to download this way
Tables have a collection of columns, some of which specify point in space (columns ending in _position), some a root id (ending in _root_id), and others that contain other information about the object at that point. Before describing some of the most important tables in the database, it’s useful to know about a few advanced options that apply when querying any table.
desired_resolution : This parameter allows you to convert the columns specifying spatial points to different resolutions. Many tables are stored at a resolution of 4x4x40 nm/voxel, for example, but you can convert to nanometers by setting desired_resolution=[1,1,1].split_positions : This parameter allows you to split the columns specifying spatial points into separate columns for each dimension. The new column names will be the original column name with _x, _y, and _z appended.select_columns : This parameter allows you to get only a subset of columns from the table. Once you know exactly what you want, this can save you some cleanup.limit : This parameter allows you to limit the number of rows returned. If you are just testing out a query or trying to inspect the kind of data within a table, you can set this to a small number to make sure it works before downloading the whole table. Note that this will show a warning so that you don’t accidentally limit your query when you don’t mean to.For example, using all of these together:
cell_type_df = client.materialize.query_table('nucleus_detection_v0', split_positions=True, desired_resolution=[1,1,1], select_columns=['pt_position', 'pt_root_id'], limit=10)
cell_type_df| pt_position_x | pt_position_y | pt_position_z | pt_root_id | |
|---|---|---|---|---|
| 0 | 241856.0 | 374464.0 | 838720.0 | 0 |
| 1 | 227200.0 | 389120.0 | 797160.0 | 0 |
| 2 | 230144.0 | 422336.0 | 795320.0 | 0 |
| 3 | 239488.0 | 386432.0 | 794120.0 | 0 |
| 4 | 239744.0 | 423488.0 | 803120.0 | 864691136050815731 |
| 5 | 245888.0 | 384512.0 | 800120.0 | 0 |
| 6 | 249792.0 | 391680.0 | 807080.0 | 0 |
| 7 | 243328.0 | 403008.0 | 794280.0 | 0 |
| 8 | 247872.0 | 386816.0 | 805320.0 | 0 |
| 9 | 260352.0 | 416640.0 | 802360.0 | 864691135013273238 |
Filtering tables so that you only get data about certain rows back is a very common operation. While there are filtering options in the query_table function (see documentation for more details), a more unified filter interface is available through a “table manager” interface.
Rather than passing a table name to the query_table function, client.materialize.tables has a subproperty for each table in the database that can be used to filter that table.
The general pattern for usage is
where {table_name} is the name of the table you want to filter, {filter options} is a collection of arguments for filtering the query, and {format and timestamp options} are those parameters controlling the format and timestamp of the query.
For example, let’s look at the table aibs_metamodel_celltypes_v661, which has cell type predictions across the dataset. We can get the whole table as a DataFrame:
cell_type_df = client.materialize.tables.aibs_metamodel_celltypes_v661().query()
cell_type_df.head()| id | created | valid | volume | pt_supervoxel_id | pt_root_id | id_ref | created_ref | valid_ref | target_id | classification_system | cell_type | pt_position | bb_start_position | bb_end_position | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 336365 | 2020-09-28 22:42:48.966292+00:00 | t | 272.488202 | 93606511657924288 | 864691136274724621 | 36916 | 2023-12-19 22:47:18.659864+00:00 | t | 336365 | excitatory_neuron | 5P-IT | [209760, 180832, 27076] | [nan, nan, nan] | [nan, nan, nan] |
| 1 | 110648 | 2020-09-28 22:45:09.650639+00:00 | t | 328.533443 | 79385153184885329 | 864691135489403194 | 1070 | 2023-12-19 22:38:00.472115+00:00 | t | 110648 | excitatory_neuron | 23P | [106448, 129632, 25410] | [nan, nan, nan] | [nan, nan, nan] |
| 2 | 112071 | 2020-09-28 22:43:34.088785+00:00 | t | 272.929423 | 79035988248401958 | 864691136147292311 | 1099 | 2023-12-19 22:38:00.898837+00:00 | t | 112071 | excitatory_neuron | 23P | [103696, 149472, 15583] | [nan, nan, nan] | [nan, nan, nan] |
| 3 | 197927 | 2020-09-28 22:43:10.652649+00:00 | t | 91.308851 | 84529699506051734 | 864691136050858227 | 13259 | 2023-12-19 22:41:14.417986+00:00 | t | 197927 | nonneuron | oligo | [143600, 186192, 26471] | [nan, nan, nan] | [nan, nan, nan] |
| 4 | 198087 | 2020-09-28 22:41:36.677186+00:00 | t | 161.744978 | 83756261929388963 | 864691135809440972 | 13271 | 2023-12-19 22:41:14.685474+00:00 | t | 198087 | nonneuron | astrocyte | [137952, 190944, 27361] | [nan, nan, nan] | [nan, nan, nan] |
and we can add similar formatting options as in the last section to the query function:
cell_type_df = client.materialize.tables.aibs_metamodel_celltypes_v661().query(split_positions=True, desired_resolution=[1,1,1], select_columns=['pt_position', 'pt_root_id', 'cell_type'], limit=10)
cell_type_df| cell_type | pt_position_x | pt_position_y | pt_position_z | pt_root_id | |
|---|---|---|---|---|---|
| 0 | 23P | 257600.0 | 487936.0 | 802760.0 | 864691135724233643 |
| 1 | 23P | 260992.0 | 493568.0 | 801560.0 | 864691136436395166 |
| 2 | NGC | 256256.0 | 466432.0 | 831040.0 | 864691135462260637 |
| 3 | 23P | 255744.0 | 480640.0 | 833200.0 | 864691136723556861 |
| 4 | 23P | 262144.0 | 505856.0 | 824880.0 | 864691135776658528 |
| 5 | 23P | 257536.0 | 521728.0 | 804440.0 | 864691135941166708 |
| 6 | 23P | 251840.0 | 552896.0 | 832320.0 | 864691135545065768 |
| 7 | 23P | 251136.0 | 546048.0 | 821320.0 | 864691135479369926 |
| 8 | 23P | 256000.0 | 626368.0 | 814000.0 | 864691135697633557 |
| 9 | astrocyte | 324096.0 | 417920.0 | 658880.0 | 864691135937358133 |
However, now we can also filter the table to get only cells that are predicted to have cell type "BC" (for “basket cell”).
my_cell_type = "BC"
client.materialize.tables.aibs_metamodel_celltypes_v661(cell_type=my_cell_type).query()| id_ref | created_ref | valid_ref | target_id | classification_system | cell_type | id | created | valid | volume | pt_supervoxel_id | pt_root_id | pt_position | bb_start_position | bb_end_position | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 43009 | 2023-12-19 22:48:53.577191+00:00 | t | 369908 | inhibitory_neuron | BC | 369908 | 2020-09-28 22:40:41.814964+00:00 | t | 332.862751 | 96002690286851358 | 864691136522768017 | [227104, 207840, 20841] | [nan, nan, nan] | [nan, nan, nan] |
| 1 | 12051 | 2023-12-19 22:40:57.133228+00:00 | t | 193846 | inhibitory_neuron | BC | 193846 | 2020-09-28 22:40:41.897904+00:00 | t | 306.148966 | 82838443188669165 | 864691135684976823 | [131568, 168496, 16452] | [nan, nan, nan] | [nan, nan, nan] |
| 2 | 83044 | 2023-12-19 22:58:50.269173+00:00 | t | 615735 | inhibitory_neuron | BC | 615735 | 2020-09-28 22:40:41.957345+00:00 | t | 314.539540 | 112181247505371364 | 864691136311774525 | [344880, 161104, 17084] | [nan, nan, nan] | [nan, nan, nan] |
| 3 | 48718 | 2023-12-19 22:50:21.192138+00:00 | t | 401681 | inhibitory_neuron | BC | 401681 | 2020-09-28 22:40:42.066718+00:00 | t | 497.801462 | 98465046644219429 | 864691136052141043 | [245232, 203952, 21268] | [nan, nan, nan] | [nan, nan, nan] |
| 4 | 82324 | 2023-12-19 22:58:39.896999+00:00 | t | 613047 | inhibitory_neuron | BC | 613047 | 2020-09-28 22:40:41.982376+00:00 | t | 242.159780 | 113234168401651200 | 864691136065413528 | [352688, 141616, 25312] | [nan, nan, nan] | [nan, nan, nan] |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 3360 | 8968 | 2023-12-19 22:40:09.246333+00:00 | t | 170777 | inhibitory_neuron | BC | 170777 | 2020-09-28 22:45:25.310708+00:00 | t | 499.103662 | 81230957054577082 | 864691135065994564 | [119600, 250560, 15373] | [nan, nan, nan] | [nan, nan, nan] |
| 3361 | 15548 | 2023-12-19 22:41:48.382554+00:00 | t | 208056 | inhibitory_neuron | BC | 208056 | 2020-09-28 22:45:25.401800+00:00 | t | 521.621668 | 84540007091735344 | 864691135801456226 | [143472, 262944, 23693] | [nan, nan, nan] | [nan, nan, nan] |
| 3362 | 79472 | 2023-12-19 22:57:53.993099+00:00 | t | 591219 | inhibitory_neuron | BC | 591219 | 2020-09-28 22:45:25.526753+00:00 | t | 567.517839 | 110216764830845707 | 864691135279126177 | [330320, 204752, 25060] | [nan, nan, nan] | [nan, nan, nan] |
| 3363 | 55791 | 2023-12-19 22:52:02.582669+00:00 | t | 438586 | inhibitory_neuron | BC | 438586 | 2020-09-28 22:45:25.430745+00:00 | t | 529.501389 | 99807894274485381 | 864691135395662581 | [254912, 247440, 23680] | [nan, nan, nan] | [nan, nan, nan] |
| 3364 | 50504 | 2023-12-19 22:50:48.576826+00:00 | t | 419363 | inhibitory_neuron | BC | 419363 | 2020-09-28 22:45:25.436862+00:00 | t | 530.642698 | 99716496901116512 | 864691136691390838 | [254416, 90336, 20469] | [nan, nan, nan] | [nan, nan, nan] |
3365 rows × 15 columns
or maybe we just want the cell types for a particular collection of root ids:
my_root_ids = [864691135771677771, 864691135560505569, 864691136723556861]
client.materialize.tables.aibs_metamodel_celltypes_v661(pt_root_id=my_root_ids).query()| id | created | valid | volume | pt_supervoxel_id | pt_root_id | id_ref | created_ref | valid_ref | target_id | classification_system | cell_type | pt_position | bb_start_position | bb_end_position | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 19116 | 2020-09-28 22:41:51.767906+00:00 | t | 301.426115 | 74737997899501359 | 864691135771677771 | 11282 | 2023-12-19 22:40:43.249642+00:00 | t | 19116 | excitatory_neuron | 23P | [72576, 108656, 20291] | [nan, nan, nan] | [nan, nan, nan] |
| 1 | 21783 | 2020-09-28 22:41:59.966574+00:00 | t | 263.637074 | 75795590176519004 | 864691135560505569 | 15681 | 2023-12-19 22:41:50.365399+00:00 | t | 21783 | excitatory_neuron | 23P | [80128, 124000, 16563] | [nan, nan, nan] | [nan, nan, nan] |
| 2 | 4074 | 2020-09-28 22:42:41.341179+00:00 | t | 313.678234 | 73543309863605007 | 864691136723556861 | 50080 | 2023-12-19 22:50:42.474168+00:00 | t | 4074 | excitatory_neuron | 23P | [63936, 120160, 20830] | [nan, nan, nan] | [nan, nan, nan] |
You can get a list of all parameters than be used for querying with the standard IPython/Jupyter docstring functionality, e.g. client.materialize.tables.aibs_metamodel_celltypes_v661.
Use of this functionality will show a brief warning that the interface is experimental. This is because the interface is still being developed and may change in the near future in response to user feedback.
Proofreading is necessary to obtain accurate reconstructions of a cell. In the MICrONS dataset, the general rule is that dendrites onto cells with a single cell body are sufficiently proofread to trust synaptic connections onto a cell. Axons on the other hand require so much proofread that only ~1,000 cells have axons that were proofread to various degrees such that their outputs can be used for analysis.
The table proofreading_status_and_strategy contains proofreading information about ~1,300 neurons. This website provides the most detailed overview. In brief, axons annotated with any strategy_axon were cleaned of false mergers but not all were fully extended. The most important distinction is axons annotated with axon_column_truncated were only proofread within a certain volume wheras others were proofread without such bias.
strategy_axon
axon_partially_extended 979
axon_column_truncated 233
none 185
axon_interareal 144
axon_fully_extended 80
Name: count, dtype: int64We can filter our query to only return rows that match a condition by adding a filter to our query: