MapAlignmentAlgorithmKD

class pyopenms.MapAlignmentAlgorithmKD

Bases: object

Cython implementation of _MapAlignmentAlgorithmKD

Original C++ documentation is available here

An efficient reference-free feature map alignment algorithm for unlabeled data

This algorithm uses a kd-tree to efficiently compute conflict-free connected components (CCC) in a compatibility graph on feature data. This graph is comprised of nodes corresponding to features and edges connecting features f and f’ iff both are within each other’s tolerance windows (wrt. RT and m/z difference). CCCs are those CCs that do not contain multiple features from the same input map, and whose features all have the same charge state

All CCCs above a user-specified minimum size are considered true sets of corresponding features and based on these, LOWESS transformations are computed for each input map such that the average deviation from the mean retention time within all CCCs is minimized

__init__()

Overload:

__init__(self, num_maps: int, param: Param) → None

Overload:

__init__(self, in_0: MapAlignmentAlgorithmKD) → None

Methods

__init__	Overload:
addRTFitData(self, kd_data)	Compute data points needed for RT transformation in the current kd_data, add to fit_data_
fitLOWESS(self)	Fit LOWESS to fit_data_, store final models in transformations_
transform(self, kd_data)	Transform RTs for kd_data

addRTFitData(self, kd_data: KDTreeFeatureMaps) → None

Compute data points needed for RT transformation in the current kd_data, add to fit_data_

fitLOWESS(self) → None

Fit LOWESS to fit_data_, store final models in transformations_

transform(self, kd_data: KDTreeFeatureMaps) → None

Transform RTs for kd_data