alignment

deimos.alignment.agglomerative_clustering(features, dims=['mz', 'drift_time', 'retention_time'], tol=[2e-05, 0.03, 0.3], relative=[True, True, False])[source]

Cluster features within provided linkage tolerances. Recursively merges the pair of clusters that minimally increases a given linkage distance. See sklearn.cluster.AgglomerativeClustering.

Parameters:

  • features (DataFrame or DataFrame) – Input feature coordinates and intensities per sample.

  • dims (str or list) – Dimensions considered in clustering.

  • tol (float or list) – Tolerance in each dimension to define maximum cluster linkage distance.

  • relative (bool or list) – Whether to use relative or absolute tolerances per dimension.

Returns:

features – Features concatenated over samples with cluster labels.

Return type:

DataFrame

deimos.alignment.fit_spline(a, b, align='retention_time', **kwargs)[source]

Fit a support vector regressor to matched features.

Parameters:

  • a (DataFrame) – First set of input feature coordinates and intensities.

  • b (DataFrame) – Second set of input feature coordinates and intensities.

  • align (str) – Dimension to align.

  • kwargs – Keyword arguments for support vector regressor (sklearn.svm.SVR).

Returns:

Interpolated fit of the SVR result.

Return type:

interp1d

deimos.alignment.match(a, b, dims=['mz', 'drift_time', 'retention_time'], tol=[5e-06, 0.015, 0.3], relative=[True, True, False])[source]

Identify features in b within tolerance of those in a . Matches are bidirectionally one-to-one by highest intensity.

Parameters:

  • a (DataFrame) – First set of input feature coordinates and intensities.

  • b (DataFrame) – Second set of input feature coordinates and intensities.

  • dims (str or list) – Dimensions considered in matching.

  • tol (float or list) – Tolerance in each dimension to define a match.

  • relative (bool or list) – Whether to use relative or absolute tolerances per dimension.

Returns:

a, b – Features matched within tolerances. E.g., a[i..n]`and `b[i..n] each represent matched features.

Return type:

DataFrame

deimos.alignment.tolerance(a, b, dims=['mz', 'drift_time', 'retention_time'], tol=[5e-06, 0.025, 0.3], relative=[True, True, False])[source]

Identify features in b within tolerance of those in a. Matches are potentially many-to-one.

Parameters:

  • a (DataFrame) – First set of input feature coordinates and intensities.

  • b (DataFrame) – Second set of input feature coordinates and intensities.

  • dims (str or list) – Dimensions considered in matching.

  • tol (float or list) – Tolerance in each dimension to define a match.

  • relative (bool or list) – Whether to use relative or absolute tolerances per dimension.

Returns:

a, b – Features matched within tolerances. E.g., a[i..n] and b[i..n] each represent matched features.

Return type:

DataFrame