https://github.com/atarashansky/lightgoea
A light-weight python function for performing GO term enrichment analysis
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Repository
A light-weight python function for performing GO term enrichment analysis
Basic Info
- Host: GitHub
- Owner: atarashansky
- Language: Python
- Default Branch: master
- Size: 24.4 KB
Statistics
- Stars: 4
- Watchers: 1
- Forks: 2
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
LightGOEA
A light-weight python function for performing GO term enrichment analysis
Documentation
```python def GOEA(targetgenes,GENESETS,goterms=None,fdrthresh=0.25,pthresh=1e-3): """Performs GO term Enrichment Analysis using the hypergeometric distribution.
Parameters
----------
target_genes - array-like
List of target genes from which to find enriched GO terms.
GENE_SETS - dictionary
Dictionary where the keys are GO terms and the values are lists of genes associated with each GO term.
Ex: {'GO:0000001': ['GENE_A','GENE_B'],
'GO:0000002': ['GENE_A','GENE_C','GENE_D']}
Make sure to include all available genes that have GO terms in your dataset.
goterms - array-list, optional, default None
If provided, only these GO terms will be tested.
fdr_thresh - float, optional, default 0.25
Filter out GO terms with FDR q value greater than this threshold.
p_thresh - float, optional, default 1e-3
Filter out GO terms with p value greater than this threshold.
Returns:
-------
enriched_goterms - pandas.DataFrame
A Pandas DataFrame of enriched GO terms with FDR q values, p values, and associated genes provided.
"""
```
Usage
All you need is a target list of genes and a dictionary of GO terms with associated genes:
Ex:
python
GENE_SETS = {'GO:0000001': ['GENE_A','GENE_B'],
'GO:0000002': ['GENE_A','GENE_C','GENE_D']}
Make sure this dictionary contains all genes with available GO terms in your dataset.
To run GOEA, simply do:
python
from light_goea import GOEA
enriched_goterms = GOEA(target_genes,GENE_SETS)
Owner
- Login: atarashansky
- Kind: user
- Repositories: 3
- Profile: https://github.com/atarashansky
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