Load Analyses for a MGnify Study

Python

Author

Affiliation

Sandy Rogers

MGnify team at EMBL-EBI

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Load a Study from the MGnify API and fetch its Analyses

The MGnify API returns JSON data. The jsonapi_client package can help you load this data into Python, e.g. into a Pandas dataframe.

This example shows you how to load a MGnify Study’s Analyses from the MGnify API

You can find all of the other “API endpoints” using the Browsable API interface in your web browser. The URL you see in the browsable API is exactly the same as the one you can use in this code.

This is an interactive code notebook (a Jupyter Notebook). To run this code, click into each cell and press the ▶ button in the top toolbar, or press shift+enter.

Select a Study

Pick a particular Study of interest. If you followed a link to this notebook, we might already know the Study Accession. Otherwise, you can enter one or use the example:

from lib.variable_utils import get_variable_from_link_or_input

# You can also just directly set the accession variable in code, like this:
# accession = "MGYS00005292"
accession = get_variable_from_link_or_input('MGYS', 'Study Accession', 'MGYS00005292')

Using Study Accession MGYS00005292 from the link you followed.

Using "MGYS00005292" as Study Accession

Fetch data

Fetch Analyses for this study from the MGnify API, into a Pandas dataframe

from jsonapi_client import Session
import pandas as pd

with Session("https://www.ebi.ac.uk/metagenomics/api/v1") as mgnify:
    analyses = map(lambda r: r.json, mgnify.iterate(f'studies/{accession}/analyses'))
    analyses = pd.json_normalize(analyses)

Inspect the data

The .head() method prints the first few rows of the table

analyses.head()

	type	id	attributes.accession	attributes.analysis-status	attributes.pipeline-version	attributes.analysis-summary	attributes.experiment-type	attributes.is-private	attributes.complete-time	attributes.instrument-platform	attributes.instrument-model	relationships.study.data.id	relationships.study.data.type	relationships.run.data.id	relationships.run.data.type	relationships.sample.data.id	relationships.sample.data.type
0	analysis-jobs	MGYA00448077	MGYA00448077	completed	4.1	[{'key': 'Submitted nucleotide sequences', 'va...	amplicon	False	2020-01-31T08:26:49	ILLUMINA	Illumina HiSeq 2500	MGYS00005292	studies	SRR6132556	runs	SRS2065862	samples
1	analysis-jobs	MGYA00448078	MGYA00448078	completed	4.1	[{'key': 'Submitted nucleotide sequences', 'va...	amplicon	False	2020-01-31T08:27:25	ILLUMINA	Illumina HiSeq 2500	MGYS00005292	studies	SRR6132555	runs	SRS2065861	samples
2	analysis-jobs	MGYA00448079	MGYA00448079	completed	4.1	[{'key': 'Submitted nucleotide sequences', 'va...	amplicon	False	2020-01-31T08:28:04	ILLUMINA	Illumina HiSeq 2500	MGYS00005292	studies	SRR6132554	runs	SRS2065860	samples
3	analysis-jobs	MGYA00448080	MGYA00448080	completed	4.1	[{'key': 'Submitted nucleotide sequences', 'va...	amplicon	False	2020-01-31T08:28:42	ILLUMINA	Illumina HiSeq 2500	MGYS00005292	studies	SRR6132553	runs	SRS2065859	samples
4	analysis-jobs	MGYA00448081	MGYA00448081	completed	4.1	[{'key': 'Submitted nucleotide sequences', 'va...	amplicon	False	2020-01-31T08:29:18	ILLUMINA	Illumina HiSeq 2500	MGYS00005292	studies	SRR6132552	runs	SRS2065858	samples

Example: distribution of instruments used for the Analysed Samples

import matplotlib.pyplot as plt
analyses.groupby('attributes.instrument-model').size().plot(kind='pie')
plt.title('Number of Analysed Samples by instrument type');