Tissue Enrichment Analysis (TEA)

This repository holds the scripts to calculate enrichment of a set of labels using hypergeometric statistics. Results are output to a dataframe. A standard plotting function is provided, but it is really just a thin wrapper around a seaborn plot.


This library has been developed in Python >= 3.5, using the Anaconda distribution. Requirements include pandas, matplotlib, numpy, scipy and seaborn.


Use pip install tissue_enrichment_analysis

Basic Usage

Web usage

Go to www.wormbase.org/tea, input your gene list and enjoy the results!

Within a Python Script

There are really just two main functions that are provided in TEA: enrichment_analysis and plot_enrichment_results.

A standard call to this library would be as follows:

import tissue_enrichment_analysis as tea

gene_list= some_gene_list

tissue_df= tea.fetch_dictionary()

df_results= tea.enrichment_analysis(tissue_df, gene_list, aname= 'FileName')

tea.plot_enrichment_results(df_results, title= 'FileName')

Calling from Terminal

Gene enrichment analysis can be generated easily by calling the program via terminal using: tea tissue_dictionary your_gene_list -[OPTIONS]

Type tea -h for help and full documentation.

Future Work

We may try to add support for other model organisms!


If you find any bugs, have suggestions or just want to say hi, feel free to contact me at dangeles@caltech.edu

Good luck!

David Angeles-Albores


David Angeles-Albores


Raymond Y. Lee, Paul W. Sternberg


With special thanks to the entire worm community!