Corrects retention time distortions between maps, using information from peptides identified in different maps.

potential predecessor tools	→ MapAlignerTreeGuided →	potential successor tools
IDMapper (or any other source of FDR-filtered featureXMLs)	→ MapAlignerTreeGuided →	FeatureLinkerUnlabeledKD or FeatureLinkerUnlabeledQT

This tool provides an algorithm to align the retention time scales of multiple input files, correcting shifts and distortions between them. Retention time adjustment may be necessary to correct for chromatography differences e.g. before data from multiple LC-MS runs can be combined (feature grouping), or when one run should be annotated with peptide identifications obtained in a different run.

All map alignment tools (MapAligner...) collect retention time data from the input files and - by fitting a model to this data - compute transformations that map all runs to a common retention time scale. They can apply the transformations right away and return output files with aligned time scales (parameter out), and/or return descriptions of the transformations in trafoXML format (parameter trafo_out). Transformations stored as trafoXML can be applied to arbitrary files with the MapRTTransformer tool.

The map alignment tools differ in how they obtain retention time data for the modeling of transformations, and consequently what types of data they can be applied to. The alignment algorithm implemented here is based on peptide identifications and applicable to annotated featureXML files. It finds peptide sequences that each pair of input files have in common, uses them as points of correspondence between the inputs and to evaluate the distances between the maps for hierarchical clustering. Tree based, the alignment of each cluster pair is performed with the method align() of the OpenMS::MapAlignmentAlgorithmIdentification. For more details and algorithm-specific parameters (set in the INI file) see "Detailed Description" in the algorithm documentation.

See also: MapAlignerIdentification MapAlignerPoseClustering MapAlignerSpectrum MapRTTransformer

Note that alignment is based on the sequence including modifications, thus an exact match is required. I.e., a peptide with oxidised methionine will not be matched to its unmodified version. This behavior is generally desired since (some) modifications can cause retention time shifts.

Also note that convex hulls are removed for alignment and are therefore missing in the output files.

Since OpenMS 1.8, the extraction of data for the alignment has been separate from the modeling of RT transformations based on that data. It is now possible to use different models independently of the chosen algorithm. This algorithm has been tested with the "b_spline" model. The different available models are:

linear: Linear model.
b_spline: Smoothing spline (non-linear).
lowess: Local regression (non-linear).
interpolated: Different types of interpolation.

The command line parameters of this tool are:

MapAlignerTreeGuided -- Tree guided correction of retention time distortions between maps.
Full documentation: http://www.openms.de/doxygen/release/3.0.0/html/TOPP_MapAlignerTreeGuided.html
Version: 3.0.0 Jul 14 2023, 11:57:33, Revision: be787e9
To cite OpenMS:
 + Rost HL, Sachsenberg T, Aiche S, Bielow C et al.. OpenMS: a flexible open-source software platform for 
   mass spectrometry data analysis. Nat Meth. 2016; 13, 9: 741-748. doi:10.1038/nmeth.3959.

Usage:
  MapAlignerTreeGuided <options>

This tool has algorithm parameters that are not shown here! Please check the ini file for a detailed descript
ion or use the --helphelp option

Options (mandatory options marked with '*'):
  -in <files>*        Input files to align (all must have the same file type) (valid formats: 'featureXML')
  -out <files>        Output files (same file type as 'in'). This option or 'trafo_out' has to be provided; 
                      they can be used together. (valid formats: 'featureXML')
  -trafo_out <files>  Transformation output files. This option or 'out' has to be provided; they can be used 
                      together. (valid formats: 'trafoXML')
  -copy_data String   Copy data (faster, more memory required) or reload data (slower, less memory required) 
                      when aligning many files. (default: 'true') (valid: 'true', 'false')
                      
Common TOPP options:
  -ini <file>         Use the given TOPP INI file
  -threads <n>        Sets the number of threads allowed to be used by the TOPP tool (default: '1')
  -write_ini <file>   Writes the default configuration file
  --help              Shows options
  --helphelp          Shows all options (including advanced)

The following configuration subsections are valid:
 - algorithm   Algorithm parameters section

You can write an example INI file using the '-write_ini' option.
Documentation of subsection parameters can be found in the doxygen documentation or the INIFileEditor.
For more information, please consult the online documentation for this tool:
  - http://www.openms.de/doxygen/release/3.0.0/html/TOPP_MapAlignerTreeGuided.html

INI file documentation of this tool:

Legend:

required parameter

advanced parameter

+MapAlignerTreeGuidedTree guided correction of retention time distortions between maps.

version3.0.0 Version of the tool that generated this parameters file.

++1Instance '1' section for 'MapAlignerTreeGuided'

in[] Input files to align (all must have the same file type)input file*.featureXML

out[] Output files (same file type as 'in'). This option or 'trafo_out' has to be provided; they can be used together.output file*.featureXML

trafo_out[] Transformation output files. This option or 'out' has to be provided; they can be used together.output file*.trafoXML

copy_datatrue Copy data (faster, more memory required) or reload data (slower, less memory required) when aligning many files.true, false

log Name of log file (created only when specified)

debug0 Sets the debug level

threads1 Sets the number of threads allowed to be used by the TOPP tool

no_progressfalse Disables progress logging to command linetrue, false

forcefalse Overrides tool-specific checkstrue, false

testfalse Enables the test mode (needed for internal use only)true, false

+++algorithmAlgorithm parameters section

model_typeb_spline Options to control the modeling of retention time transformations from datalinear, b_spline, lowess, interpolated

++++model

typeb_spline Type of modellinear, b_spline, lowess, interpolated

+++++linearParameters for 'linear' model

symmetric_regressionfalse Perform linear regression on 'y - x' vs. 'y + x', instead of on 'y' vs. 'x'.true, false

x_weightx Weight x values1/x, 1/x2, ln(x), x

y_weighty Weight y values1/y, 1/y2, ln(y), y

x_datum_min1.0e-15 Minimum x value

x_datum_max1.0e15 Maximum x value

y_datum_min1.0e-15 Minimum y value

y_datum_max1.0e15 Maximum y value

+++++b_splineParameters for 'b_spline' model

wavelength0.0 Determines the amount of smoothing by setting the number of nodes for the B-spline. The number is chosen so that the spline approximates a low-pass filter with this cutoff wavelength. The wavelength is given in the same units as the data; a higher value means more smoothing. '0' sets the number of nodes to twice the number of input points.0.0:∞

num_nodes5 Number of nodes for B-spline fitting. Overrides 'wavelength' if set (to two or greater). A lower value means more smoothing.0:∞

extrapolatelinear Method to use for extrapolation beyond the original data range. 'linear': Linear extrapolation using the slope of the B-spline at the corresponding endpoint. 'b_spline': Use the B-spline (as for interpolation). 'constant': Use the constant value of the B-spline at the corresponding endpoint. 'global_linear': Use a linear fit through the data (which will most probably introduce discontinuities at the ends of the data range).linear, b_spline, constant, global_linear

boundary_condition2 Boundary condition at B-spline endpoints: 0 (value zero), 1 (first derivative zero) or 2 (second derivative zero)0:2

+++++lowessParameters for 'lowess' model

span0.666666666666667 Fraction of datapoints (f) to use for each local regression (determines the amount of smoothing). Choosing this parameter in the range .2 to .8 usually results in a good fit.0.0:1.0

num_iterations3 Number of robustifying iterations for lowess fitting.0:∞

delta-1.0 Nonnegative parameter which may be used to save computations (recommended value is 0.01 of the range of the input, e.g. for data ranging from 1000 seconds to 2000 seconds, it could be set to 10). Setting a negative value will automatically do this.

interpolation_typecspline Method to use for interpolation between datapoints computed by lowess. 'linear': Linear interpolation. 'cspline': Use the cubic spline for interpolation. 'akima': Use an akima spline for interpolationlinear, cspline, akima

extrapolation_typefour-point-linear Method to use for extrapolation outside the data range. 'two-point-linear': Uses a line through the first and last point to extrapolate. 'four-point-linear': Uses a line through the first and second point to extrapolate in front and and a line through the last and second-to-last point in the end. 'global-linear': Uses a linear regression to fit a line through all data points and use it for interpolation.two-point-linear, four-point-linear, global-linear

+++++interpolatedParameters for 'interpolated' model

interpolation_typecspline Type of interpolation to apply.linear, cspline, akima

extrapolation_typetwo-point-linear Type of extrapolation to apply: two-point-linear: use the first and last data point to build a single linear model, four-point-linear: build two linear models on both ends using the first two / last two points, global-linear: use all points to build a single linear model. Note that global-linear may not be continuous at the border.two-point-linear, four-point-linear, global-linear

++++align_algorithm

score_type Name of the score type to use for ranking and filtering (.oms input only). If left empty, a score type is picked automatically.

score_cutofffalse Use only IDs above a score cut-off (parameter 'min_score') for alignment?true, false

min_score0.05 If 'score_cutoff' is 'true': Minimum score for an ID to be considered.
Unless you have very few runs or identifications, increase this value to focus on more informative peptides.

min_run_occur2 Minimum number of runs (incl. reference, if any) in which a peptide must occur to be used for the alignment.
Unless you have very few runs or identifications, increase this value to focus on more informative peptides.2:∞

max_rt_shift0.5 Maximum realistic RT difference for a peptide (median per run vs. reference). Peptides with higher shifts (outliers) are not used to compute the alignment.
If 0, no limit (disable filter); if > 1, the final value in seconds; if <= 1, taken as a fraction of the range of the reference RT scale.0.0:∞

use_unassigned_peptidestrue Should unassigned peptide identifications be used when computing an alignment of feature or consensus maps? If 'false', only peptide IDs assigned to features will be used.true, false

use_feature_rttrue When aligning feature or consensus maps, don't use the retention time of a peptide identification directly; instead, use the retention time of the centroid of the feature (apex of the elution profile) that the peptide was matched to. If different identifications are matched to one feature, only the peptide closest to the centroid in RT is used.
Precludes 'use_unassigned_peptides'.true, false

use_adductstrue If IDs contain adducts, treat differently adducted variants of the same molecule as different.true, false