Visual Coding Neuropixels Metadata

Visual Coding Neuropixels Metadata#

import pandas as pd 
import numpy as np
from datetime import datetime, date

from aind_data_access_api.document_db import MetadataDbClient

API_GATEWAY_HOST = "api.allenneuraldynamics.org"
DATABASE = 'metadata_index'
COLLECTION = 'data_assets'

docdb_api_client = MetadataDbClient(
   host=API_GATEWAY_HOST,
    version="v2",
   database=DATABASE,
   collection=COLLECTION,
)
print(docdb_api_client._base_url)

https://api.allenneuraldynamics.org/v2/metadata_index/data_assets

aggregate = [
  {
    "$match": {
      "data_description.project_name": {
        "$regex": "Allen Brain Observatory - Visual Coding Neuropixels",
        "$options": "i"
      },
    }
  },
  {
    "$project": {
      "name": 1, 
      "subject_id": "$data_description.subject_id",
      "genotype": "$subject.subject_details.genotype", 
      "date_of_birth": "$subject.subject_details.date_of_birth", 
      "sex": "$subject.subject_details.sex", 
      "session_time": "$acquisition.acquisition_start_time",
      "project_name": "$data_description.project_name", 
      "modality": "$data_description.modalities.name",
      "session_type": "$acquisition.acquisition_type",
    }
  },
]
    
records = docdb_api_client.aggregate_docdb_records(
    pipeline = aggregate,
)

Create a dataframe to explore using pandas:

df = pd.DataFrame(records)

df['session_date'] = df.apply(lambda x: datetime.fromisoformat(x['session_time']).date(), axis=1)
df['session_time'] = df.apply(lambda x: datetime.fromisoformat(x['session_time']).time(), axis=1)
df['date_of_birth'] = df.apply(lambda x: datetime.strptime(x['date_of_birth'], '%Y-%m-%d').date(), axis=1)
df['age'] = df.apply(lambda x: (x['session_date'] - x['date_of_birth']).days, axis=1)

order = ['project_name','_id','name','subject_id','genotype','date_of_birth','sex','modality',
         'session_type','session_date','age','session_time']
df = df[order]

df.head()
project_name _id name subject_id genotype date_of_birth sex modality session_type session_date age session_time
0 Allen Brain Observatory - Visual Coding Neurop... 28a94fad-584c-42d3-8dea-8c2762d67075 433891_2019-02-27_13-09-23_nwb_2026-08-19_09-5... 433891 Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt 2018-11-08 Male [Behavior videos, Extracellular electrophysiol... functional_connectivity 2019-02-27 111 13:09:23
1 Allen Brain Observatory - Visual Coding Neurop... e580d6e8-7b56-4c62-befd-b35f66923fb4 405755_2018-09-12_13-32-59_nwb_2026-08-19_08-1... 405755 Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt 2018-06-12 Female [Behavior videos, Extracellular electrophysiol... brain_observatory_1.1 2018-09-12 92 13:32:59
2 Allen Brain Observatory - Visual Coding Neurop... f00b7e0f-4d0b-4c7d-89d5-aa4d63d98959 424448_2018-12-20_13-43-28_nwb_2026-08-19_09-4... 424448 wt/wt 2018-08-14 Male [Behavior videos, Extracellular electrophysiol... brain_observatory_1.1 2018-12-20 128 13:43:28
3 Allen Brain Observatory - Visual Coding Neurop... 3a85289f-f7d6-4107-a51b-79767ca97a1a 386129_2018-06-27_14-07-11_nwb_2026-08-19_07-4... 386129 Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt 2018-03-02 Male [Behavior videos, Extracellular electrophysiol... brain_observatory_1.1 2018-06-27 117 14:07:11
4 Allen Brain Observatory - Visual Coding Neurop... 1a251996-1a48-45f4-a5cb-27da1c6909f1 437660_2019-03-20_14-36-29_nwb_2026-08-19_10-0... 437660 Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt 2018-11-26 Male [Behavior videos, Extracellular electrophysiol... functional_connectivity 2019-03-20 114 14:36:29

Genotypes#

Cre lines were used to drive the expression of Channelrhodopsin to units of a given transcriptomic type to be identified using optotagging. We can see which cell types were targeted by looking at the unique genotypes:

genotypes = df.genotype.unique().tolist()
genotypes

['Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt',
 'Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt',
 'wt/wt',
 'Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt']

Note

Not all units recorded from in a mouse of a given genotype will be Cre+. Only a subset will be optotagged.

See Transgenic tools to learn more about these Cre lines and reporters.

Session types#

session_types = df.session_type.unique().tolist()
session_types

['functional_connectivity', 'brain_observatory_1.1']

How many sessions are there with each genotype for each session type?#

df2 = pd.DataFrame(columns=session_types, index=genotypes)
for gen in genotypes:
    for st in session_types:
        df2.loc[gen, st] = len(df[(df.session_type==st)&(df['genotype']==gen)])
df2
functional_connectivity brain_observatory_1.1
Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt 3 5
Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt 3 5
wt/wt 14 16
Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt 6 6
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