Global FoodOmics package

Current functionality includes generating food counts tables from a molecular network (sample data networked with reference data using classical molecular networking). Follow Reference Data-Driven Analysis tutorial to run the networking job in GNPS.

Dependencies

Make sure you have Python 3.6 or newer installed. You will also need the following packages installed:

• numpy

  pip3 install numpy
  

• pandas

  pip3 install pandas
  

Usage

1. Clone main branch of this repository

   git clone -b main https://github.com/global-foodomics/gfop.git
   

2. Navigate to the package directory

   cd gfop
   

3. Start Python

   python
   

4. Import functions

   import gfop.get_food_counts as gfop
   

5. Generate food counts table.

   f_counts = gfop.get_dataset_food_counts(gnps_network = 'METABOLOMICS-SNETS-V2-07f85565-view_all_clusters_withID_beta-main.tsv',
                                           metadata = 'sample_metadata.csv',
                                           filename_col = 'filenames',
                                           sample_types = 'simple',
                                           all_groups = ['G1'],
                                           some_groups = ['G4'],
                                           level = 5)
   

   • The argument gnps_network is the path to the tsv file you will have downloaded from your molecular networking job. Be sure to use the absolute path.
   • The argument metadata is the path to your sample metadata (csv or tsv file). It must contain a column with the sample filenames. This will be used to match the files added to G1/G2 in the molecular network. Again, use the absolute path.
   • filename_col is the column header in metadata that contains the sample filenames.
   • The argument sample_types can be one of: simple, complex, all. Simple foods/beverages are those containing a single ingredient (e.g. apple, milk) whereas complex foods contain multiple ingredients (e.g. granola bar, meal preparation). sample_types = 'all' will return both simple and complex foods.
   • all_groups is the molecular networking group(s) used for your samples.
   • some_groups is the molecular networking group(s) used for the reference samples.
   • level is an integer from 0-6 and dictates how broad or specific you want your food categories to be. Level 1 uses the most broad groupings (e.g. plant, animal). Level 6 uses the most specific groupings (e.g. strawberry, green grape). Level 0 indicates no groupings, counts will be returned for all individual reference samples. Note: this will require matching reference sample names to the food metadata

   You can type help(gfop.get_dataset_food_counts) for more information about the required arguments.

6. Save food counts as csv file

   f_counts.to_csv('food_counts.csv')