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
Go to www.wormbase.org/tea, input your gene list and enjoy the results!
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')
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.
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, Juancarlos Chan, Paul W. Sternberg
With special thanks to the entire worm community!