helper_scripts

This directory contains a series of tools that can be used to help users with various miscellaneous tasks.

• clean_labels.py: For each read of the given sequence file or each leaf of a given phylogenetic tree, remove everything from the label except for the contact network individual's name

  • Usage: clean_sequence_headers.py [-h] [-i INPUT] [-o OUTPUT]
    • INPUT: Input FASTQ/FASTA/Newick File (default: standard input)
    • OUTPUT: Input FASTQ/FASTA/Newick File (default: standard output)
      • The output format (FASTA/FASTQ/Newick) matches the input format and is detected automatically
      • If you wish to convert a FASTQ file to FASTA, refer to fastq2fasta.py in my tools repository

• cluster_previous_time.py: Given a clustering from the simulation end time, a FAVITES-format transmission network, and a time, remove individuals who were not infected at the given time and output the resulting clusters.

  • Usage: cluster_previous_time.py [-h] -c CLUSTERING -tn TRANSMISSIONS -t TIME [-o OUTPUT]
    • CLUSTERING: Input clustering file (Cluster Picker format)
    • TRANSMISSIONS: Input transmission network (FAVITES format)
    • TIME: Time to remove all individuals infected after
    • OUTPUT: Output file (default: standard output)

• cn_adjacency_matrix_to_favites.py: Convert a given contact network from a binary adjacency matrix to the FAVITES format

  • Usage: cn_adjacency_matrix_to_favites.py [-h] -i INPUT [-o OUTPUT] [-d DELIM]
    • INPUT: Input contact network file (binary adjacency matrix)
    • OUT: Output contact network file (FAVITES format) (default: standard output)
    • DELIM: Column delimiter of input binary adjacency matrix (default: empty string)

• degree_stats.py: Given a contact or transmission network, compute various statistics of the node degree distribution

  • Usage: degree_stats.py [-h] -i INPUT
    • INPUT: Input contact or transmission network file (FAVITES format)

• FAVITES2GEXF.py: Convert a FAVITES contact network and transmission network to the GEXF format

  • Usage: FAVITES2GEXF.py [-h] -c CONTACT_NETWORK -t TRANSMISSION_NETWORK [-o OUTPUT]
    • CONTACT_NETWORK: FAVITES-format contact network
    • TRANSMISSION_NETWORK: FAVITES-format transmission network
    • OUTPUT Output file (default: standard output)

• label_internal_nodes.py: Label the internal nodes of a simulated tree with the corresponding infected individual

  • Usage: label_internal_nodes.py [-h] -tn TRANSMISSION_NETWORK -tt TREE_TIME [-tm TREE_MUTATION] -ot OUTPUT_TIME [-om OUTPUT_MUTATION]
    • TRANSMISSION_NETWORK: FAVITES-format transmission network
    • TREE_TIME: Simulated tree in unit of time
    • TREE_MUTATION: Simulated tree in unit of expected number of per-site mutations
    • OUTPUT_TIME: Output labeled tree in unit of time
    • OUTPUT_MUTATION: Output labeled tree in unit of expected number of per-site mutations

• PANGEA_transmissions_to_FAVITES.py: Convert a PANGEA transmission network into the FAVITES edge-list format

  • Usage: PANGEA_transmissions_to_FAVITES.py [-h] -i INPUT [-o OUTPUT]
    • INPUT: Input PANGEA transmission network (.csv)
    • OUTPUT: Output file (default: stdout)

• patristic_distances.py: Given a phylogenetic tree, compute the pairwise distances between leaves and output the resulting distance matrix as a CSV file

  • Usage: patristic_distances.py [-h] -t TREE [-s SCHEMA] [-o OUTPUT]
    • TREE: Input tree
    • SCHEMA: Input tree schema (default: Newick)
    • OUTPUT: Output file (default: standard output)

• scale_tree.py: Given a phylogenetic tree (in the Newick format), scale all branches

  • Usage: scale_tree.py [-h] -t TREE [-o OUTPUT] -m MODE [parameters]
    • TREE: Input tree
    • OUTPUT: Output file (default: stdout)
    • MODE: Scaling mode
      • Constant: scale_tree.py -t TREE [-o OUTPUT] -m c CONSTANT
      • Exponential: scale_tree.py -t TREE [-o OUTPUT] -m e SCALE
      • Gamma: scale_tree.py -t TREE [-o OUTPUT] -m g SHAPE SCALE
      • Log-Normal: Log-Normal Mode Usage: scale_tree.py -t TREE [-o OUTPUT] -m ln MU SIGMA

• score_clusters.py: Score a given query clustering against a given true reference clustering

  • Usage: score_clusters.py [-h] -q QUERY -r REFERENCE -m METRIC
    • QUERY: Query clustering file (Cluster Picker format)
    • REFERENCE: Reference clustering file (Cluster Picker format)
    • METRIC: Scoring metric
      • AMI: Adjusted Mutual Information
      • ARI: Adjusted Rand Index
      • COM: Completeness Score
      • FMI: Fowlkes-Mallows Index
      • HCV: Compute Homogeneity, Completeness, and V-Measure simultaneously
      • HOM: Homogeneity Score
      • MI: Mutual Information
      • NMI: Normalized Mutual Information
      • VM: V-Measure

• tn93_to_clusters.py: Convert tn93 output to the Cluster Picker clustering format

  • Usage: tn93_to_clusters.py [-h] -i INPUT [-t THRESHOLD] [-o OUTPUT]
    • INPUT: Input TN93 distances file
    • THRESHOLD: Distance threshold t (default: infinity)
      • Two individuals u and v are placed in a cluster together if their distance is less than or equal to t
    • OUTPUT: Output file (default: standard output)