Skip to main content

ReCIPE package description

Project description

Steps for running ReCIPE

Install ReCIPE

pip install recipe-cluster

Download Gene Ontology Data Base

Download gene ontology

Generate DSD file

fastDSD is recommended. It can be used simply by running command:

fastDSD -c --converge -t 0.5 --outfile dscript_distances network-filepath.csv

Generate Cluster File (if necessary)

ReCIPE accepts both CSV and JSON formats for cluster files.

  • CSV format: Each line in the CSV represents a cluster of proteins, with each cluster containing a comma-separated list of protein identifiers.

  • JSON format: Each key represents a unique cluster ID. The value associated with each key is an object containing a members array, which lists the protein identifiers for that cluster.

Run ReCIPE

Reconnect Clusters

This method analyses cluster, network and DSD files to determine the which proteins qualify to be introduced to clusters in order to create overlapping clusters.

usage: recipe-cluster cook [-h] -cfp CLUSTER_FILEPATH [-cfl CLUSTERS_LABELED] -nfp NETWORK_FILEPATH --outfile OUTFILE [--lb LB] [--ub UB] [--lr LR]
                           [--connectivity-threshold CONNECTIVITY_THRESHOLD] [--metric {degree,components_connected,score}] [--max_proteins MAX_PROTEINS] [--protein_cap PROTEIN_CAP]

options:
  -h, --help            show this help message and exit
  -cfp, --cluster-filepath CLUSTER_FILEPATH
                        Cluster filepath
  -cfl, --clusters-labeled CLUSTERS_LABELED
                        If a CSV file of clusters is passed, clusters have labels. Default: False
  -nfp, --network-filepath NETWORK_FILEPATH
                        Network filepath
  --outfile OUTFILE     Output file to save results
  --lb LB               Lower bound (inclusive) for cluster size. Default: 3
  --ub UB               Upper bound (exclusive) for cluster size. Default: 100
  --lr LR               Linear ratio (if not using sqrt). Default = None
  --connectivity-threshold, -cthresh CONNECTIVITY_THRESHOLD
                        Connectivity threshold to add proteins until. Default = -1.0 (yields connectivity thresholds [0.1, 0.25, 0.5, 0.75, 1.0]) (if only a single option is desired, 0.75
                        is recommended)
  --metric, -wm {degree,components_connected,score}
                        Which metric to use to rank proteins to be added back. Default: degree. Options: degree, components_connected, score
  --max_proteins MAX_PROTEINS
                        Maximises number of proteins to added to a cluster. Default = None
  --protein_cap PROTEIN_CAP
                        Adds at most the number of proteins defined by parameter. Default = 20

In addition to command line access, the cook method can be accessed programmatically with the same arguments as follows:

import recipe-cluster as recipe

recipe.cook(...)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

recipe_cluster-0.0.6.tar.gz (21.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

recipe_cluster-0.0.6-py3-none-any.whl (24.4 kB view details)

Uploaded Python 3

File details

Details for the file recipe_cluster-0.0.6.tar.gz.

File metadata

  • Download URL: recipe_cluster-0.0.6.tar.gz
  • Upload date:
  • Size: 21.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.5

File hashes

Hashes for recipe_cluster-0.0.6.tar.gz
Algorithm Hash digest
SHA256 5b35ff3a43febbf37efea7c7c3a6cfd16d2f12bdce6b63d5e64e18a09b6f72d7
MD5 2e9a2cf357bf3963fda2560295e7013a
BLAKE2b-256 e6a2ca96eaa930af6086802cab5ade0cc6ad5b8c182ab908da37bc57016a816f

See more details on using hashes here.

File details

Details for the file recipe_cluster-0.0.6-py3-none-any.whl.

File metadata

  • Download URL: recipe_cluster-0.0.6-py3-none-any.whl
  • Upload date:
  • Size: 24.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.5

File hashes

Hashes for recipe_cluster-0.0.6-py3-none-any.whl
Algorithm Hash digest
SHA256 347489f7acdbd729923c85b8a592fba0048fdfd18f44e91603e19d28fd089254
MD5 c33eb2e2a13805cd62f29c3d389f765a
BLAKE2b-256 7046a81f05572b6f4fc74b690ec0ce6e3e7ba7aa89257e3d0d8af64f06f751f6

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page