A set of python modules for accessing BioTuring Ecosystem on BioColab private server
Project description
How to use biocolabsdk ?
The package only allows data submission via BioColab private server. Please configure your tokens in the User Settings
page.
1. Access to BBrowserX private server via biocolabsdk:
1.1. Test the connection to BBrowserX private server:
from biocolabsdk.connector import EConnector
connector = EConnector(
private_host="https://yourcompany/t2d_index_tool/,
private_token="<input your token here>"
)
connector.get_bbrowserx().test_connection()
Example output:
Connecting to host at https://yourcompany/t2d_index_tool/api/v1/test_connection
Connection successful
1.2. Get user groups available for your token in BBrowserX private server:
from biocolabsdk.connector import EConnector
connector = EConnector(
private_host="https://yourcompany/t2d_index_tool/,
private_token="<input your token here>"
)
user_groups = connector.get_bbrowserx().get_user_groups()
print(user_groups)
Example output:
[{'id': 'all_members', 'name': 'All members'}, {'id': 'personal', 'name': 'Personal workspace'}]
1.3. Submit your data to BBrowserX private server:
from biocolabsdk.connector import EConnector
from bioturing_connector.typing import InputMatrixType
from bioturing_connector.typing import Species
from bioturing_connector.typing import StudyType
connector = EConnector(
private_host="https://yourcompany/t2d_index_tool/,
private_token="<input your token here>"
)
# Call this function first to get available groups and their id.
user_groups = connector.get_bbrowserx().get_user_groups()
# Example: user_groups is now [{'id': 'all_members', 'name': 'All members'}, {'id': 'personal', 'name': 'Personal workspace'}]
# Submitting the scanpy object from s3:
batch_info = [{
'matrix': 's3_path/GSE128223_1.h5ad',
}, {
'matrix': 's3_path/GSE128223_2.h5ad',
}]
connector.get_bbrowserx().submit_study_from_s3(
group_id='personal',
batch_info=batch_info,
study_id='GSE128223',
name='This is my first study',
authors=['Huy Nguyen'],
species=Species.HUMAN.value,
input_matrix_type=InputMatrixType.RAW.value,
study_type=StudyType.H5AD.value
)
# Submitting the scanpy object from local machine:
batch_info = [{
'matrix': 'local_path/GSE128223_1.h5ad',
}, {
'matrix': 'local_path/GSE128223_2.h5ad',
}]
connector.get_bbrowserx().submit_study_from_local(
group_id='personal',
batch_info=batch_info,
study_id='GSE128223',
name='This is my first study',
authors=['Huy Nguyen'],
species=Species.HUMAN.value,
input_matrix_type=InputMatrixType.RAW.value,
study_type=StudyType.H5AD.value
)
2. Access Talk2Data public server via biocolabsdk:
from biocolabsdk.connector import EConnector
connector = EConnector(
public_token="<input your token here>"
)
connector.get_talk2data().test_connection()
Example output:
Connecting to host at https://talk2data.bioturing.com/t2d_index_tool/api/v1/test_connection
Connection successful
You can utilize all the features in a similar way to accessing a BBroserX private server.
3. Access bioflex public server via biocolabsdk:
You can get a Bioflex token at: https://colab.bioturing.com/request/bioflex/token
Create a connection using access token:
from biocolabsdk.connector import EConnector
connector = EConnector(
bioflex_token="<input your token here>"
)
List available databases:
databases = connector.get_bioflex().databases()
[DataBase(id="5010c7d573ae4ff2b9691422b99aa2cd", name="BioTuring database",species="human",version=1), DataBase(id="5010c7d573ae4ff2b9691422b99aa2cd", name="BioTuring database",species="human",version=2), DataBase(id="5010c7d573ae4ff2b9691422b99aa2cd", name="BioTuring database",species="human",version=3)]
Get database cell types gene expression summary
database = databases[2]
database.get_celltypes_expression_summary(['CD3D', 'CD3E'])
{'CD3D': [Summary(name="B cell",sum=707108874.0,mean=4192.7096,rate=0.035,count=168652.0,total=4812967), Summary(name="CD4-positive, alpha-beta T cell",sum=9489987442.0,mean=4657.5619,rate=0.5283,count=2037544.0,total=3856590), ... Summary(name="corneal progenitor",sum=0.0,mean=0.0,rate=0.0,count=0.0,total=3973), Summary(name="nucleus pulposus progenitor cell",sum=0.0,mean=0.0,rate=0.0,count=0.0,total=2310)]}
Create study instance, using study hash ID from BioTuring studies:
study = database.get_study('GSE96583_batch2')
study
Study(id="GSE96583_batch2", title="Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (Batch 2)", reference="https://www.nature.com/articles/nbt.4042")
Take a peek at study metadata:
study.metalist
[Metadata(id=0,name="Number of mRNA transcripts",type="Numeric"), Metadata(id=1,name="Number of genes",type="Numeric"), Metadata(id=2,name="Batch id",type="Category"), Metadata(id=3,name="Stimulation",type="Category"), Metadata(id=4,name="Author's cell type",type="Category")]
Fetch a study metadata:
metadata = study.metalist[4]
metadata
Metadata(id=4,name="Author's cell type",type="Category")
metadata.fetch()
metadata.values
array(['CD8 T cells', 'Dendritic cells', 'CD4 T cells', ..., 'CD8 T cells', 'B cells', 'CD4 T cells'], dtype='<U17')
Query genes:
exp_mtx = study.query_genes(['CD3D', 'CD3E'], bioflex.UNIT_LOGNORM)
exp_mtx
<29065x2 sparse matrix of type '<class 'numpy.float32'>' with 15492 stored elements in Compressed Sparse Column format>
Get study barcodes:
study.barcodes()
['GSM2560249_AAACATACCAAGCT-1', 'GSM2560249_AAACATACCCCTAC-1', ... 'GSM2560249_AATTGTGATTCACT-1', 'GSM2560249_AATTGTGATTTCGT-1', ...]
Get study features:
study.features()
['5S_RRNA', '5_8S_RRNA', ... 'AC006273', 'AC006277', ...]
Get study full matrix:
study.matrix(bioflex.UNIT_LOGNORM)
<29065x64642 sparse matrix of type '<class 'numpy.float32'>' with 17570739 stored elements in Compressed Sparse Column format>
Export Study:
study.export_study(bioflex.EXPORT_H5AD)
{'download_link': 'https://talk2data.bioturing.com/api/export/a1003bad3dd146b28c7bda913a2fc3f0', 'study_hash_id': 'GSE96583_batch2'}
For further information please check the documentation.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
biocolabsdk-1.0.4.tar.gz
(4.9 kB
view hashes)
Built Distribution
Close
Hashes for biocolabsdk-1.0.4-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5722574551eb9c92d00173fcf551b7fc77a088a7c101d0feb50e2c7794be7c30 |
|
MD5 | edfa36804698433f46ebfd62f1ed4693 |
|
BLAKE2b-256 | 4add00029cdb1892a92a137f70dadf760ecb3f7bfa01b7b93a3040e47f3d1eb0 |