Metadata-Version: 2.1
Name: pymean
Version: 0.22
Summary: The pyMEAN package is designed to facilitate semi-automated enrichment analysis for metabolomic experiments.
Home-page: https://github.com/AberystwythSystemsBiology/pyMEAN
Author: Keiron O'Shea
License: UNKNOWN
Description: # pyMEAN: Metabolomic Enrichment ANalysis in Python
        
        The pyMEAN package is designed to facilitate semi-automated enrichment analysis for metabolomic experiments.
        
        ## Installation
        
        pyMEAN requires Python 3+ and is unfortunately not compatible with Python 2. If you are still using Python 2, a clever workaround is to install Python 3 and use that instead.
        
        The easiest way of installing pyMEAN is using pip:
        
        ```
        pip install pymean
        ```
        
        Alternatively, you can use git and pip in unison to get the development branch:
        
        ```
        pip install https://github.com/KeironO/pyMEAN
        ```
        
        ## Usage
        
        Here's a starting template to get you started:
        
        ```python
        # Import pyMEAN module into Python.
        from pymean import EnrichmentAnalysis
        
        # A compound list of inchikeys.
        compound_list = [
            "WDJHALXBUFZDSR-UHFFFAOYSA-N", # acetoacetic acid
            "UCMIRNVEIXFBKS-UHFFFAOYSA-N", # beta-alanine
            "CVSVTCORWBXHQV-UHFFFAOYSA-N", # creatine
            "FFDGPVCHZBVARC-UHFFFAOYSA-N", # dimethylglycine
            "VZCYOOQTPOCHFL-OWOJBTEDSA-N", # fumaric acid
            "DHMQDGOQFOQNFH-UHFFFAOYSA-N", # glycine
            "FFFHZYDWPBMWHY-UHFFFAOYSA-N", # l-homocysteine
            "XUJNEKJLAYXESH-REOHCLBHSA-N", # l-cysteine
            "COLNVLDHVKWLRT-QMMMGPOBSA-N", # l-phenylalanine
            "BTNMPGBKDVTSJY-UHFFFAOYSA-N" # phenylpyruvic acid
        ]
        
        
        # Create an EnrichmentAnalysis object for the analysis of hsa
        ea = EnrichmentAnalysis(compound_list, organism="hsa")
        
        # Obtain results
        results = ea.run_analysis(pvalue_cutoff=0.005)
        
        ```
        
        If you'd like to plot out your results, take inspiration from the following method:
        
        ```python
        
        def plot_enrichment_analysis_results(results: pd.DataFrame, adj_method:str):
            fold_enrichment = np.abs(np.log(results["%s adj. p-value" % (adj_method)]))
            plt.figure()
            plt.title("Metabolite Sets Enrichment Overview")
            plt.barh(results["Pathway Name"], fold_enrichment, height=0.5)
            plt.xlabel("Fold Enrichment")
        
            plt.yticks(fontsize=6)
            plt.tight_layout()
        
            plt.show()
        
        plot_enrichment_analysis_results(results, "fdr_bh")
        
        ```
        
        Which will return the following chart:
        
        ![Plot of results](https://github.com/AberystwythSystemsBiology/pyMEAN/raw/master/example.png)
        
        ## License
        
        Code released under the [GPLv3](https://github.com/AberystwythSystemsBiology/pyMEAN/blob/master/LICENSE).
        
        
Keywords: metabolomics,enrichment analysis,omics
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Requires-Python: >= 3.6
Description-Content-Type: text/markdown
