bioflex: Python package for calling BioTuring API

bioflex allows scientists to use simple yet powerful commands to retrieve gene expression data,
cell metadata from thousands of single-cell studies in BioTuring Database.

Installation

pip install bioflex

For access token, register at BioTuring Data Science

Requirements

• Requests
• NumPy
• SciPy
• tqdm
• H5Py

Examples

Create a connection using access token:

import bioflex
connection = bioflex.connect('70d2acfda3a54ca6a4390699394****')

List available databases:

databases = connection.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="1557",hash_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>

For further information please check the documentation.