geo-alchemy

a Python library and command line tool to make GEO data into gold.

1. why geo-alchemy
2. installation
3. use as Python library
   • parse metadata from GEO
     • parse platform
     • parse sample
     • parse series
   • serialization and deserialization
4. use as command line software
   • using OCM
   • preprocessing

why geo-alchemy

GEO is like a gold mine that contains a huge many gold ore. But processing these gold ore(GEO series) into gold(expression matrix, clinical data) is not very easy:

1. how to map microarray probe to gene?
2. how about multiple probes map to same gene?
3. hot to get clinical data?
4. ...

geo-alchemy was born to deal with it.

installation

If you only want use as Python library:

pip install geo-alchemy

If you also want use as command line software:

pip install 'geo-alchemy[cmd]'

use as Python library

parse metadata from GEO

parse platform

from geo_alchemy import PlatformParser


parser = PlatformParser.from_accession('GPL570')
platform1 = parser.parse()


# or
platform2 = PlatformParser.from_accession('GPL570').parse()


print(platform1 == platform2)

# get platform annotation data
platform = PlatformParser.from_accession('GPL570', view='full').parse()
print(platform.internal_data)

parse sample

from geo_alchemy import SampleParser


parser = SampleParser.from_accession('GSM1885279')
sample1 = parser.parse()

# or
sample2 = SampleParser.from_accession('GSM1885279').parse()

print(sample1 == sample2)

parse series

from geo_alchemy import SeriesParser


parser = SeriesParser.from_accession('GSE73091')
series1 = parser.parse()

# or
series2 = SeriesParser.from_accession('GSE73091').parse()


print(series1 == series2)
print(series1.platforms)
print(series1.samples)
print(series1.organisms)

serialization and deserialization

For the convenience of saving, all objects in geo-alchemy can be converted to dict, and this dict can be directly saved to a file in json form.

Moreover, geo-alchemy also provides methods to convert these dicts into objects.

from geo_alchemy import SeriesParser


series1 = SeriesParser.from_accession('GSE73091').parse()
data = series1.to_dict()
series2 = SeriesParser.parse_dict(data)


print(series1 == series2)

use as command line software

using OCM

OCM(object command mapping) is a Python framework mapping Python object to command line software. It can capture intermediate results of command, you can enable OCM output like this:

geo-alchemy xxx --ocmir

probe reannotation

Prerequisites:

1. NCBI BLAST must be installed.
2. BLAST Index must be generated.

for more details, refer to this page.

geo-alchemy -d reanno -p GPL15303 -s 9 -d /Users/dev/Data/blast-indexes/GRCh38.p13/GRCh38.p13

1. -p GPL15303 probe reannotation for GPL15303
2. -s 9 the 9th column of platform annotation file is probe sequence
3. -d xxx blast indexes location

if your reference sequences are download from GENCODE, enable --gencode can extract gene symbol from gene ID:

geo-alchemy -d reanno -p GPL15303 -s 9 -d /Users/dev/Data/blast-indexes/GRCh38.p13/GRCh38.p13 --gencode

preprocessing(microarray series only)

download metadata using network:

geo-alchemy pp -s GSE174772 -p GPL570 -g 11

1. -s GSE174772 preprocessing for GSE174772
2. -p GPL570 preprocessing samples who use GPL570 of GSE174772
3. -g 11 NO.11 column of GPL570 annotation file is gene

this command generate 2 files under current directory:

1. clinical file GSE174772_clinical.txt
2. gene expression file GSE174772_expression.txt

use existed series metadata:

import json
from geo_alchemy import SeriesParser


series = SeriesParser.from_accession('GSE174772').parse()
data = series.to_dict()


with open('GSE174772.json', 'w') as fp:
   json.dump(data, fp)

geo-alchemy pp -sf GSE174772.json -g 11

using existing probe gene mapping file. usually you use geo-alchemy reanno do probe reannotation, this make you get a probe gene mapping file, you can:

geo-alchemy reanno -p GPL6480 -s 17 -d /Users/dev/Data/blast-indexes/GRCh38.p13/GRCh38.p13 --gencode
geo-alchemy pp -s GSE12435 -m GPL6480_reanno.txt