gsea-api 0.3.4

MSigDB integration

Molecular Signatures Database (MSigDB) can be downloaded from the Broad Institute GSEA website. It provides expert-curated gene set collections, as well as curated subset of pathway databases (Reactome, KEGG, Biocarta, Gene Ontology) trimmed to remove redundant, overlapping and and otherwise little-value terms (if needed).

You can download all the pathways collections at once (search for ZIPped MSigDB on the download page). After downloading and un-zipping (e.g. to a local directory named msigdb), you can access the gene sets from MSigDB with:

from gsea_api.molecular_signatures_db import MolecularSignaturesDatabase

msigdb = MolecularSignaturesDatabase('msigdb', version=7.1)
msigdb.gene_sets

msigdb.gene_sets returns a list of dictionaries describing auto-detected pathways:

[
    {'name': 'c1.all', 'id_type': 'symbols'},
    {'name': 'c1.all', 'id_type': 'entrez'},
    {'name': 'c2.cp.reactome', 'id_type': 'symbols'},
    {'name': 'c2.cp.reactome', 'id_type': 'entrez'}
    # etc..
]

Information about the location on disk and version are avilable in msigdb.path and msigdb.version.

msigdb.load loads the specific collection into a GeneSets object:

> kegg_pathways = msigdb.load('c2.cp.kegg', 'symbols')
> print(kegg_pathways)
<GeneSets 'c2.cp.kegg' with 186 gene sets>

This object can be passed to any of the supporteed GSEA implementations; please see below for a detailed description of the GeneSets object.

GeneSets objects

GeneSets represents a collection of sets of genes, where each set is represented as GeneSet object.

You can check the number of sets contained within a collection with:

> len(kegg_pathways)
186

The gene sets are accessible with gene_sets (tuple) and gene_sets_by_name (dict) properties:

> kegg_pathways.gene_sets[:2]
(<GeneSet 'KEGG_TIGHT_JUNCTION' with 132 genes>, <GeneSet 'KEGG_RNA_DEGRADATION' with 59 genes>)
> kegg_pathways.gene_sets_by_name
{
    'KEGG_TIGHT_JUNCTION': <GeneSet 'KEGG_TIGHT_JUNCTION' with 132 genes>,
    'KEGG_RNA_DEGRADATION': <GeneSet 'KEGG_RNA_DEGRADATION' with 59 genes>
    # etc.
 }

> measured_genes = {'APOE', 'CYB5R1', 'FCER1G', 'PVR', 'HK2'}
> measured_subset = kegg_pathways.subset(measured_genes)
> print(measured_subset)
<GeneSets with 12 gene sets>

> kegg_pathways.trim(min_genes=10, max_genes=20)
<GeneSets with 21 gene sets>

def prettify_kegg_name(name):
    return name.replace('KEGG_', '').replace('_', ' ')

kegg_pathways_pretty = kegg_pathways.format_names(prettify_kegg_name)
kegg_pathways_pretty.gene_sets[:2]
# (<GeneSet 'TIGHT JUNCTION' with 132 genes>, <GeneSet 'RNA DEGRADATION' with 59 genes>)

GSEA from Broad Institute

Login/register on the official GSEA website and download the gsea_3.0.jar file (or a newer version).

Provide the location of the downloaded file to GSEADesktop() using gsea_jar_path argument, e.g.:

gsea = GSEADesktop(gsea_jar_path='downloads/gsea_3.0.jar')

GSEApy

To use gsea.py please install it with:

pip3 install gseapy

Use it with:

from gsea_api.gsea import GSEApy

gsea = GSEApy()

cudaGSEA

Please clone this fork of cudaGSEA to thirdparty directory and compile the binary version (using the instructions from this repository):

git clone https://github.com/krassowski/cudaGSEA

or use the original version, which does not implement FDR calculations.

Use it with:

from gsea_api.gsea import cudaGSEA

# CPU implementation can be used with use_cpu=True
gsea = cudaGSEA(fdr='full', use_cpu=False)