Python

MOLGENIS Python API allows access to your MOLGENIS data from python. It is available on every MOLGENIS with version 1.14.0 and up on the URL http://molgenis.mydomain.example/molgenis.py.

As an example, let's create a plot for publicly available ASE data available on https://molgenis56.target.rug.nl/. For a description of the data, take a look at http://molgenis.org/ase.

We'll be creating a scatter plot so if you haven't already, install matplotlib from the commandline:

pip install matplotlib

Download the python api from a molgenis server, for instance https://molgenis01.target.rug.nl/molgenis.py and save it in molgenis.py.

Start an interactive python shell and create a molgenis connection:

import molgenis

This imports the molgenis package.

session = molgenis.Session("https://molgenis56.target.rug.nl/api/")

Instantiates a new Session pointing at the molgenis56 server. If you take a look at the connection by typing

dir(session)

you should see, amongst others, the methods you can call:

[...,'add', 'add_all', 'delete', 'get', 'get_attribute_meta_data', 'get_entity_meta_data', 'login', 'logout',...]

Let's load some data from the server using session.get:

session.get("ASE")

This retrieves the top 1000 rows from the ASE entity.

[{u'Alternative_allele': u'A', u'P_Value': 2.06504739339637e-17, u'Genes': {u'href': u'/api/v1/ASE/rs9901673/Genes'}, u'Fraction_alternative_allele': 0.479, u'Pos': 7484101, u'Reference_allele': u'C', u'Chr': u'17', u'href': u'/api/v1/ASE/rs9901673', u'Samples': u'145', u'Likelihood_ratio_test_D': 72.0813644150712, u'SNP_ID': u'rs9901673'}, {u'Alternative_allele': u'T', u'P_Value': 8.78109735398113e-18, u'Genes': {u'href': u'/api/v1/ASE/rs2597775/Genes'}, u'Fraction_alternative_allele': 0.479, u'Pos': 17503382, u'Reference_allele': u'C', u'Chr': u'4', u'href': u'/api/v1/ASE/rs2597775', u'Samples': u'359', u'Likelihood_ratio_test_D': 73.769089117417, u'SNP_ID': u'rs2597775'}, {u'Alternative_allele': u'C', u'P_Value': 1.4917458949834e-18, u'Genes': {u'href': u'/api/v1/ASE/rs3216/Genes'}, u'Fraction_alternative_allele': 0.479, u'Pos': 214421, u'Reference_allele': u'G', u'Chr': u'11', u'href': u'/api/v1/ASE/rs3216', u'Samples': u'301', u'Likelihood_ratio_test_D': 77.2691957930797, u'SNP_ID': u'rs3216'}, [...],{u'Alternative_allele': u'T', u'P_Value': 0.000132500824069775, u'Genes': {u'href': u'/api/v1/ASE/rs1056019/Genes'}, u'Fraction_alternative_allele': 0.482, u'Pos': 41337435, u'Reference_allele': u'C', u'Chr': u'12', u'href': u'/api/v1/ASE/rs1056019', u'Samples': u'47', u'Likelihood_ratio_test_D': 14.605874945467, u'SNP_ID': u'rs1056019'}]

Let's retrieve a specific SNP from the ASE entity:

print(session.get("ASE", q=[{"field":"SNP_ID", "operator":"EQUALS", "value":"rs12460890"}]))

[{u'Alternative_allele': u'T', u'P_Value': 7.1708540619282e-14, u'Genes': {u'href': u'/api/v1/ASE/rs12460890/Genes'}, u'Fraction_alternative_allele': 0.527, u'Pos': 829568, u'Reference_allele': u'C', u'Chr': u'19', u'href': u'/api/v1/ASE/rs12460890', u'Samples': u'21', u'Likelihood_ratio_test_D': 56.0207947348388, u'SNP_ID': u'rs12460890'}]

This SNP has a mild but significant allele-specific expression, based on expression counts in 21 samples.

Let's retrieve the samples for this SNP:

samples = session.get("SampleAse", q=[{"field":"SNP_ID", "operator":"EQUALS", "value":"rs12460890"}])
print(samples)

[{u'Ref_Counts': u'130', u'href': u'/api/v1/SampleAse/1418785', u'SampleIds': {u'href': u'/api/v1/SampleAse/1418785/SampleIds'}, u'Position': 829568, u'SNP_ID': {u'href': u'/api/v1/SampleAse/1418785/SNP_ID'}, u'Alt_Counts': u'121', u'ID': u'1418785', u'Chromosome': u'19'}, {u'Ref_Counts': u'4142', u'href': u'/api/v1/SampleAse/1418786', u'SampleIds': {u'href': u'/api/v1/SampleAse/1418786/SampleIds'}, u'Position': 829568, u'SNP_ID': {u'href': u'/api/v1/SampleAse/1418786/SNP_ID'}, u'Alt_Counts': u'4791', u'ID': u'1418786', u'Chromosome': u'19'}, {u'Ref_Counts': u'19', u'href': u'/api/v1/SampleAse/1418787', u'SampleIds': {u'href': u'/api/v1/SampleAse/1418787/SampleIds'}, u'Position': 829568, u'SNP_ID': {u'href': u'/api/v1/SampleAse/1418787/SNP_ID'}, u'Alt_Counts': u'28', u'ID': u'1418787', u'Chromosome': u'19'}, {u'Ref_Counts': u'19', u'href': u'/api/v1/SampleAse/1418788', u'SampleIds': {u'href': u'/api/v1/SampleAse/1418788/SampleIds'}, u'Position': 829568, u'SNP_ID': {u'href': u'/api/v1/SampleAse/1418788/SNP_ID'}, u'Alt_Counts': u'23', u'ID': u'1418788', u'Chromosome': u'19'}, {u'Ref_Counts': u'32', u'href': u'/api/v1/SampleAse/1418789', u'SampleIds': {u'href': u'/api/v1/SampleAse/1418789/SampleIds'}, u'Position': 829568, u'SNP_ID': {u'href': u'/api/v1/SampleAse/1418789/SNP_ID'}, u'Alt_Counts': u'11', u'ID': u'1418789', u'Chromosome': u'19'}, {u'Ref_Counts': u'639', u'href': u'/api/v1/SampleAse/1418790', u'SampleIds': {u'href': u'/api/v1/SampleAse/1418790/SampleIds'}, u'Position': 829568, u'SNP_ID': {u'href': u'/api/v1/SampleAse/1418790/SNP_ID'}, u'Alt_Counts': u'572', u'ID': u'1418790', u'Chromosome': u'19'}, {u'Ref_Counts': u'202', u'href': u'/api/v1/SampleAse/1418791', u'SampleIds': {u'href': u'/api/v1/SampleAse/1418791/SampleIds'}, u'Position': 829568, u'SNP_ID': {u'href': u'/api/v1/SampleAse/1418791/SNP_ID'}, u'Alt_Counts': u'309', u'ID': u'1418791', u'Chromosome': u'19'}, {u'Ref_Counts': u'423', u'href': u'/api/v1/SampleAse/1418792', u'SampleIds': {u'href': u'/api/v1/SampleAse/1418792/SampleIds'}, u'Position': 829568, u'SNP_ID': {u'href': u'/api/v1/SampleAse/1418792/SNP_ID'}, u'Alt_Counts': u'401', u'ID': u'1418792', u'Chromosome': u'19'}, {u'Ref_Counts': u'271', u'href': u'/api/v1/SampleAse/1418793', u'SampleIds': {u'href': u'/api/v1/SampleAse/1418793/SampleIds'}, u'Position': 829568, u'SNP_ID': {u'href': u'/api/v1/SampleAse/1418793/SNP_ID'}, u'Alt_Counts': u'234', u'ID': u'1418793', u'Chromosome': u'19'}, {u'Ref_Counts': u'806', u'href': u'/api/v1/SampleAse/1418794', u'SampleIds': {u'href': u'/api/v1/SampleAse/1418794/SampleIds'}, u'Position': 829568, u'SNP_ID': {u'href': u'/api/v1/SampleAse/1418794/SNP_ID'}, u'Alt_Counts': u'1081', u'ID': u'1418794', u'Chromosome': u'19'}, {u'Ref_Counts': u'213', u'href': u'/api/v1/SampleAse/1418795', u'SampleIds': {u'href': u'/api/v1/SampleAse/1418795/SampleIds'}, u'Position': 829568, u'SNP_ID': {u'href': u'/api/v1/SampleAse/1418795/SNP_ID'}, u'Alt_Counts': u'201', u'ID': u'1418795', u'Chromosome': u'19'}, {u'Ref_Counts': u'74', u'href': u'/api/v1/SampleAse/1418796', u'SampleIds': {u'href': u'/api/v1/SampleAse/1418796/SampleIds'}, u'Position': 829568, u'SNP_ID': {u'href': u'/api/v1/SampleAse/1418796/SNP_ID'}, u'Alt_Counts': u'96', u'ID': u'1418796', u'Chromosome': u'19'}, {u'Ref_Counts': u'730', u'href': u'/api/v1/SampleAse/1418797', u'SampleIds': {u'href': u'/api/v1/SampleAse/1418797/SampleIds'}, u'Position': 829568, u'SNP_ID': {u'href': u'/api/v1/SampleAse/1418797/SNP_ID'}, u'Alt_Counts': u'655', u'ID': u'1418797', u'Chromosome': u'19'}, {u'Ref_Counts': u'584', u'href': u'/api/v1/SampleAse/1418798', u'SampleIds': {u'href': u'/api/v1/SampleAse/1418798/SampleIds'}, u'Position': 829568, u'SNP_ID': {u'href': u'/api/v1/SampleAse/1418798/SNP_ID'}, u'Alt_Counts': u'699', u'ID': u'1418798', u'Chromosome': u'19'}, {u'Ref_Counts': u'331', u'href': u'/api/v1/SampleAse/1418799', u'SampleIds': {u'href': u'/api/v1/SampleAse/1418799/SampleIds'}, u'Position': 829568, u'SNP_ID': {u'href': u'/api/v1/SampleAse/1418799/SNP_ID'}, u'Alt_Counts': u'391', u'ID': u'1418799', u'Chromosome': u'19'}, {u'Ref_Counts': u'13', u'href': u'/api/v1/SampleAse/1418800', u'SampleIds': {u'href': u'/api/v1/SampleAse/1418800/SampleIds'}, u'Position': 829568, u'SNP_ID': {u'href': u'/api/v1/SampleAse/1418800/SNP_ID'}, u'Alt_Counts': u'14', u'ID': u'1418800', u'Chromosome': u'19'}, {u'Ref_Counts': u'70', u'href': u'/api/v1/SampleAse/1418801', u'SampleIds': {u'href': u'/api/v1/SampleAse/1418801/SampleIds'}, u'Position': 829568, u'SNP_ID': {u'href': u'/api/v1/SampleAse/1418801/SNP_ID'}, u'Alt_Counts': u'101', u'ID': u'1418801', u'Chromosome': u'19'}, {u'Ref_Counts': u'47', u'href': u'/api/v1/SampleAse/1418802', u'SampleIds': {u'href': u'/api/v1/SampleAse/1418802/SampleIds'}, u'Position': 829568, u'SNP_ID': {u'href': u'/api/v1/SampleAse/1418802/SNP_ID'}, u'Alt_Counts': u'35', u'ID': u'1418802', u'Chromosome': u'19'}, {u'Ref_Counts': u'19', u'href': u'/api/v1/SampleAse/1418803', u'SampleIds': {u'href': u'/api/v1/SampleAse/1418803/SampleIds'}, u'Position': 829568, u'SNP_ID': {u'href': u'/api/v1/SampleAse/1418803/SNP_ID'}, u'Alt_Counts': u'28', u'ID': u'1418803', u'Chromosome': u'19'}, {u'Ref_Counts': u'44', u'href': u'/api/v1/SampleAse/1418804', u'SampleIds': {u'href': u'/api/v1/SampleAse/1418804/SampleIds'}, u'Position': 829568, u'SNP_ID': {u'href': u'/api/v1/SampleAse/1418804/SNP_ID'}, u'Alt_Counts': u'47', u'ID': u'1418804', u'Chromosome': u'19'}, {u'Ref_Counts': u'60', u'href': u'/api/v1/SampleAse/1418805', u'SampleIds': {u'href': u'/api/v1/SampleAse/1418805/SampleIds'}, u'Position': 829568, u'SNP_ID': {u'href': u'/api/v1/SampleAse/1418805/SNP_ID'}, u'Alt_Counts': u'55', u'ID': u'1418805', u'Chromosome': u'19'
}]

There they are.

Let's format the expression counts

for sample in samples:
    print("{Ref_Counts:5} {Alt_Counts:5}".format(**sample))

130      121
4142     4791
19       28
19       23
32       11
639      572
202      309
423      401
271      234
806      1081
213      201
74       96
730      655
584      699
331      391
13       14
70       101
47       35
19       28
44       47
60       55

Let's plot the expression counts in these samples in a scatter plot.

import matplotlib.pyplot as plt
plt.scatter([sample["Ref_Counts"] for sample in samples], [sample["Alt_Counts"] for sample in samples])
plt.xlim([0, 5000])
plt.ylim([0, 5000])
plt.xlabel("Reference Allele")
plt.ylabel("Alternative Allele")
plt.title("Allele-Specific Expression for rs12460890")

And add a line for the non-specific expression.

plt.plot([0, 5000], [0, 5000])
plt.show()

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Last updated 3 years ago