gseapy 0.9.5

Mandatory

• Numpy >= 1.13.0

• Pandas

• Matplotlib

• Beautifulsoup4

• Requests(for enrichr API)

You may also need to install lxml, html5lib, if you could not parse xml files.

Before you start:

Unless you know exactly how GSEA works, you should convert all gene symobl names to uppercase first.

For command line usage:

# An example to reproduce figures using replot module.
$ gseapy replot -i ./Gsea.reports -o test


# An example to run GSEA using gseapy gsea module
$ gseapy gsea -d exptable.txt -c test.cls -g gene_sets.gmt -o test

# An example to run Prerank using gseapy prerank module
$ gseapy prerank -r gsea_data.rnk -g gene_sets.gmt -o test

# An example to run ssGSEA using gseapy ssgsea module
$ gseapy ssgsea -d expression.txt -g gene_sets.gmt -o test

# An example to use enrichr api
# see details of -g below, -d  is optional
$ gseapy enrichr -i gene_list.txt -g KEGG_2016 -d pathway_enrichment -o test

Run gseapy inside python console:

1. Prepare expression.txt, gene_sets.gmt and test.cls required by GSEA, you could do this

import gseapy

# run GSEA.
gseapy.gsea(data='expression.txt', gene_sets='gene_sets.gmt', cls='test.cls', outdir='test')

# run prerank
gseapy.prerank(rnk='gsea_data.rnk', gene_sets='gene_sets.gmt', outdir='test')

# run ssGSEA
gseapy.ssgsea(data="expression.txt", gene_sets= "gene_sets.gmt", outdir='test')


# An example to reproduce figures using replot module.
gseapy.replot(indir='./Gsea.reports', outdir='test')

2. If you prefer to use Dataframe, dict, list in interactive python console, you could do this.

see detail here: Example

# assign dataframe, and use enrichr library data set 'KEGG_2016'
expression_dataframe = pd.DataFrame()

sample_name = ['A','A','A','B','B','B'] # always only two group,any names you like

# assign gene_sets parameter with enrichr library name or gmt file on your local computer.
gseapy.gsea(data=expression_dataframe, gene_sets='KEGG_2016', cls= sample_names, outdir='test')

# using prerank tool
gene_ranked_dataframe = pd.DataFrame()
gseapy.prerank(rnk=gene_ranked_dataframe, gene_sets='KEGG_2016', outdir='test')

# using ssGSEA
gseapy.ssgsea(data=ssGSEA_dataframe, gene_sets='KEGG_2016', outdir='test')

3. For enrichr , you could assign a list, pd.Series, pd.DataFrame object, or a txt file (should be one gene name per row.)

# assign a list object to enrichr
gl = ['SCARA3', 'LOC100044683', 'CMBL', 'CLIC6', 'IL13RA1', 'TACSTD2', 'DKKL1', 'CSF1',
     'SYNPO2L', 'TINAGL1', 'PTX3', 'BGN', 'HERC1', 'EFNA1', 'CIB2', 'PMP22', 'TMEM173']

gseapy.enrichr(gene_list=gl, description='pathway', gene_sets='KEGG_2016', outdir='test')

# or a txt file path.
gseapy.enrichr(gene_list='gene_list.txt', description='pathway', gene_sets='KEGG_2016',
               outdir='test', cutoff=0.05, format='png' )

GSEAPY supported gene set libaries :

To see the full list of gseapy supported gene set libraries, please click here: Library

Or use get_library_name function inside python console.

 #see full list of latest enrichr library names, which will pass to -g parameter:
 names = gseapy.get_library_name()

 # show top 20 entries.
 print(names[:20])


['Genome_Browser_PWMs',
'TRANSFAC_and_JASPAR_PWMs',
'ChEA_2013',
'Drug_Perturbations_from_GEO_2014',
'ENCODE_TF_ChIP-seq_2014',
'BioCarta_2013',
'Reactome_2013',
'WikiPathways_2013',
'Disease_Signatures_from_GEO_up_2014',
'KEGG_2016',
'TF-LOF_Expression_from_GEO',
'TargetScan_microRNA',
'PPI_Hub_Proteins',
'GO_Molecular_Function_2015',
'GeneSigDB',
'Chromosome_Location',
'Human_Gene_Atlas',
'Mouse_Gene_Atlas',
'GO_Cellular_Component_2015',
'GO_Biological_Process_2015',
'Human_Phenotype_Ontology',]

Bug Report

If you would like to report any bugs when you running gseapy, don’t hesitate to create an issue on github here, or email me: fzq518@gmail.com