Input data details

• M2M output for each sample: Directory structure example:

  sample_1/
    community_analysis/
      addedvalue.json
      comm_scopes.json
      contributions_of_microbes.json
      mincom.json
      rev_cscope.json
      rev_cscope.tsv
      targets.sbml
    indiv_scopes/
      indiv_scopes.json
      rev_iscope.json
      rev_iscope.tsv
      seeds_in_indiv_scopes.json
    m2m_metacom.log
    producibility_targets.json
  sample_2/
    ...
  

• 📄 Metadata associated to samples: Tabulated file, first column is the sample identifier matching the output of M2M.

  smplID	Age	Country
  sample_1	2	France
  sample_2	30	Canada
  sample_3	68	Germany

• 📄 Taxonomy of the MAGs/genomes: Tabulated file, first column matches the IDs of the metabolic networks.

• 📊 Abundance of the MAGs/genomes in the samples/communities: Tabulated file, normalized by column sum during processing.

  identifier	Sample_1	Sample_2	Sample_3
  MAG_1	12.5	8.3	15.2
  Genome_1	5.8	10.1	7.6
  MAG_2	20.3	14.7	18.9

• 🚀 Precomputed data for M2M-PostAViz: Can be stored when running the tool with the -o flag and loaded for future runs.

  m2m_postaviz -d Metage2metabo/samples/scopes/directory/path \
               -m metadata/file/path \
               -a abundance/file/path \
               -t taxonomy/file/path \
               -o save/directory/path
  
  # For future runs:
  m2m_postaviz -l save/directory/path
  

  The preprocessed dataset is stored in a directory in the form of dataframes and files in Parquet format. Example structure:

  saved_data_postaviz/
    abundance_file_normalised.tsv
    abundance_file.tsv
    ...
    sample_cscope_directory/
      Sample1.parquet.gzip
      ...
    sample_iscope_directory/
      Sample1.parquet.gzip
      ...
    ...