Brief documentation on the helper scripts included in the package in the /scripts directory. To use these scripts you will need to have a clone of the git repository, or they should also be installed with the prefix ‘poppunk’ (e.g to run, run the command

Easy run mode#

Previous versions of the software had an --easy-run mode which would run a pipeline of:

  • --create-db to sketch genomes

  • --fit-model --dbscan to fit a flexible model

  • --refine-model to improve this model

This is now available as which will chain calls to poppunk and poppunk_visualise to replicate this functionality.

Iterative PopPUNK#

You can combine the output from multiple to produce further analysis. For an easy way to create multiple clusters, try the --multi-boundary option (Running with multiple boundary positions).

The script to analyse these is Basic use is to provide the output directory as --db, but run --help for other common options. This relies on finding files named <db>/<db>_boundary<n>_clusters.csv, where <n> is the boundary iteration number (continuous integers increasing from zero). Clusters must contain at least two samples.

This script will do the following:

  1. Starting from the most specific clusters (nearest the origin), it will iteratively add new clusters which are either:

    1. totally new clusters

    2. subsets of existing clusters

    3. existing clusters are subsets of the new cluster.

  2. Remove duplicate clusters.

  3. Calculate average core distance within this cluster set.

  4. Create a tree by nesting smaller clusters within larger clusters they are subsets of.

  5. Output the combined clusters, average core distances, and tree.

  6. Cut this tree to pick a set of clusters under a similarity given by --cutoff.

Adding weights to the network#

Converts binary within-cluster edge weights to the Euclidean core-accessory distance. This is equivalent to running with --graph-weights:

poppunk_add_weights <name> <name>.dists <output>

Default output is a graph-tool file. Add --graphml to save as .graphml instead.

Writing the pairwise distances to an output file#

By default PopPUNK does not write the calculated \(\pi_n\) and \(a\) distances out, as this contains \(\frac{1}{2}n*(n-1)\) rows, which gives a multi Gb file for large datasets.

However, if needed, there is a script available to extract these distances as a text file: --distances strain_db.dists --output strain_db.dists.out

Writing network components to an output file#

Visualisation of large networks with cytoscape may become challenging. It is possible to extract individual components/clusters for visualisation as follows: strain_db_graph.gpickle strain_db

Calculating Rand indices#

This script allows the clusters formed by different runs/fits/modes of PopPUNK to be compared to each other. 0 indicates the clusterings are totally discordant, and 1 indicates they are identical.

Run: --input poppunk_gmm_clusters.csv,poppunk_dbscan_cluster.csv

The script will calculate the Rand index and the adjusted Rand index between all pairs of files provided (comma separated) to the --input argument. These will be written to the file rand.out, which can be changed using --output.

The --subset argument can be used to restrict comparisons to include only specific samples listed in the provided file.

Calculating silhouette indices#

This script can be used to find how well the clusters project into core-accessory space by calculating the silhoutte index, which measures how close samples are to others in their own cluster compared to samples from other clusters. The silhoutte index is calculated for every sample and takes a value between -1 (poorly matched) to +1 (well matched). The script reports the average of these indices across all samples, using Euclidean distances between the (normalised) core and accessory divergences calculated by PopPUNK.

To run: --distances strain_db.dists --cluster-csv strain_db_clusters.csv

The following additional options are available for use with external clusterings (e.g. from hierBAPS):

  • --cluster-col the (1-indexed) column index containing the cluster assignment

  • --id-col the (1-indexed) column index containing the sample names

  • --sub a string to remove from sample names to match them to those in --distances

Distributing PopPUNK models#

This script automatically generates compressed and uncompressed directories containing all files required for distribution and reuse of PopPUNK model fits.

To run:

python --dbdir database_directory --fitdir model_fit_directory --outpref output_prefix

The following additional arguments are available:

  • --lineage specify only if lineage fit was used.

  • --no-compress will not generate tar.bz2 archives

--dbdir and --fitdir can be the same directory, however both must still be specified. The output of this script is a directory and a compressed tar.bz2 archive for each of the full dataset and representative genomes dataset.