Feature Linking#

The pyOpenMS feature grouping algorithms group corresponding features (e.g., of same analyte) from multiple FeatureMap objects into a ConsensusMap. Linking is primarily done based on spatial proximity (e.g., similar retention time and m/z). It is, thus, advisable to perform a map alignment before feature linking.

Optionally, identification data can be considered to prevent linking of features with different identifications.

_images/linking_illustration.png

Different feature grouping algorithms with slightly different implementations are runtime characteristics are available in pyOpenMS:

  • FeatureGroupingAlgorithmQT

  • FeatureGroupingAlgorithmKD

  • FeatureGroupingAlgorithm

  • FeatureGroupingAlgorithmLabeled

  • FeatureGroupingAlgorithmUnlabeled

We now perform a feature linking using the FeatureGroupingAlgorithmQT algorithm.

Download Example Data#

from pyopenms import *
from urllib.request import urlretrieve

base_url = (
    "https://raw.githubusercontent.com/OpenMS/pyopenms-docs/master/src/data/"
)

feature_files = [
    "BSA1_F1.featureXML",
    "BSA2_F1.featureXML",
    "BSA3_F1.featureXML",
]

feature_maps = []

# download the feature files and store feature maps in list (feature_maps)
for feature_file in feature_files:
    urlretrieve(base_url + feature_file, feature_file)
    feature_map = FeatureMap()
    FeatureXMLFile().load(feature_file, feature_map)
    feature_maps.append(feature_map)

Feature Linking Algorithm#

All FeatureMap objects will be combined in a ConsensusMap.

feature_grouper = FeatureGroupingAlgorithmQT()

consensus_map = ConsensusMap()

file_descriptions = consensus_map.getColumnHeaders()

# collect information about input maps
for i, feature_map in enumerate(feature_maps):
    file_description = file_descriptions.get(i, ColumnHeader())
    file_description.filename = feature_map.getDataProcessing()[0].getMetaValue(
        "parameter: in"
    )[:-5]
    file_description.size = feature_map.size()
    file_description.unique_id = feature_map.getUniqueId()
    file_descriptions[i] = file_description

consensus_map.setColumnHeaders(file_descriptions)
feature_grouper.group(feature_maps, consensus_map)