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FeatureLinkerUnlabeledKD

Group corresponding features across labelfree experiments.

Group corresponding features across labelfree experiments. This tool produces results similar to those of FeatureLinkerUnlabeledQT, since it optimizes a similar objective. However, this algorithm is more efficient than FLQT as it uses a kd-tree for fast 2D region queries in m/z - RT space and a sorted binary search tree to choose the best cluster among the remaining ones in O(1). Insertion and searching in this tree have O(log n) runtime. KD-tree insertion and search have O(log n) runtime. The overall complexity of the algorithm is O(n log(n)) time and O(n) space.

In practice, the runtime of FeatureLinkerUnlabeledQT is often not significantly worse than that of FeatureLinkerUnlabeledKD if the datasets are relatively small and/or the value of the -nr_partitions parameter is chosen large enough. If, however, the datasets are very large, and especially if they are so dense that a partitioning based on the specified m/z tolerance is not possible anymore, then this algorithm becomes orders of magnitudes faster than FLQT.

Notably, this algorithm can be used to align featureXML files containing unassembled mass traces (as produced by MassTraceExtractor), which is often impossible for reasonably large datasets using other aligners, as these datasets tend to be too dense and hence cannot be partitioned.

Prior to feature linking, this tool performs an (optional) retention time transformation on the features using LOWESS regression in order to minimize retention time differences between corresponding features across different maps. These transformed RTs are used only internally. In the results, original RTs will be reported.

The command line parameters of this tool are:

 

OpenMS / TOPP release 2.1.0 Documentation generated on Tue Jul 11 2017 14:38:46 using doxygen 1.8.13