| OpenMS 2.5.0

MapAlignerIdentification

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

potential predecessor tools | MapAlignerIdentification | potential successor tools
XTandemAdapter (or another search engine adapter) | IDMerger
IDFileConverter | FeatureLinkerUnlabeled or FeatureLinkerUnlabeledQT
IDMapper

Reference:
Weisser et al.: An automated pipeline for high-throughput label-free quantitative proteomics (J. Proteome Res., 2013, PMID: 23391308).

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 thus applicable to files containing peptide IDs (idXML, annotated featureXML/consensusXML). It finds peptide sequences that different input files have in common and uses them as points of correspondence between the inputs. For more details and algorithm-specific parameters (set in the INI file) see "Detailed Description" in the algorithm documentation.

See also
  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.

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 mostly 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 following parameters control the modeling of RT transformations (they can be set in the "model" section of the INI file):

Name | Type | Default | Restrictions | Description
--- | --- | --- | --- | ---
type | string | interpolated | linear, b_spline, lowess, interpolated | Type of model
linear:symmetric_regression | string | false | true, false | Perform linear regression on 'y - x' vs. 'y + x', instead of on 'y' vs. 'x'.
linear:x_weight | string |  | 1/x, 1/x2, ln(x), | Weight x values
linear:y_weight | string |  | 1/y, 1/y2, ln(y), | Weight y values
linear:x_datum_min | float | 1.0e-15 |  | Minimum x value
linear:x_datum_max | float | 1.0e15 |  | Maximum x value
linear:y_datum_min | float | 1.0e-15 |  | Minimum y value
linear:y_datum_max | float | 1.0e15 |  | Maximum y value
b_spline:wavelength | float | 0.0 | min: 0.0 | 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.
b_spline:num_nodes | int | 5 | min: 0 | Number of nodes for B-spline fitting. Overrides 'wavelength' if set (to two or greater). A lower value means more smoothing.
b_spline:extrapolate | string | linear | linear, b_spline, constant, global_linear | 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).
b_spline:boundary_condition | int | 2 | min: 0 max: 2 | Boundary condition at B-spline endpoints: 0 (value zero), 1 (first derivative zero) or 2 (second derivative zero)
lowess:span | float | 0.666666666666667 | min: 0.0 max: 1.0 | 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.
lowess:num_iterations | int | 3 | min: 0 | Number of robustifying iterations for lowess fitting.
lowess:delta | float | -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.
lowess:interpolation_type | string | cspline | linear, cspline, akima | 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 interpolation
lowess:extrapolation_type | string | four-point-linear | two-point-linear, four-point-linear, global-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.
interpolated:interpolation_type | string | cspline | linear, cspline, akima | Type of interpolation to apply.
interpolated:extrapolation_type | string | two-point-linear | two-point-linear, four-point-linear, global-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.

Note
  Currently mzIdentML (mzid) is not directly supported as an input/output format of this tool. Convert mzid files to/from idXML using IDFileConverter if necessary.

The command line parameters of this tool are:

    MapAlignerIdentification -- Corrects retention time distortions between maps based on common peptide identifi

    cations.

    Full documentation: http://www.openms.de/documentation/TOPP_MapAlignerIdentification.html

    Version: 2.5.0-GIT-NOTFOUND-GIT-NOTFOUND Sep 10 2020, 12:20:39, Revision: GIT-NOTFOUND

    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:

      MapAlignerIdentification <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: 'feature

                                 XML', 'consensusXML', 'idXML')

      -out <files>               Output files (same file type as 'in'). This option or 'trafo_out' has to be prov

                                 ided; they can be used together. (valid formats: 'featureXML', 'consensusXML',

                                 'idXML')

      -trafo_out <files>         Transformation output files. This option or 'out' has to be provided; they can

                                 be used together. (valid formats: 'trafoXML')

    Options to define a reference file (use either 'file' or 'index', not both):

      -reference:file <file>     File to use as reference (valid formats: 'featureXML', 'consensusXML', 'idXML')

      -reference:index <number>  Use one of the input files as reference ('1' for the first file, etc.).

                                 If '0', no explicit reference is set - the algorithm will select a reference. (

                                 default: '0' min: '0')

      -design <file>             Input file containing the experimental design (valid formats: 'tsv')

    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

     - model       Options to control the modeling of retention time transformations from data

    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/documentation/TOPP_MapAlignerIdentification.html

INI file documentation of this tool:

Legend:
required parameter
advanced parameter
+MapAlignerIdentification Corrects retention time distortions between maps based on common peptide identifications.
version 2.5.0-GIT-NOTFOUND-GIT-NOTFOUND Version of the tool that generated this parameters file.
++1 Instance '1' section for 'MapAlignerIdentification'
in[] Input files to align (all must have the same file type)input file*.featureXML,*.consensusXML,*.idXML
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,*.consensusXML,*.idXML
trafo_out[] Transformation output files. This option or 'out' has to be provided; they can be used together.output file*.trafoXML
design input file containing the experimental design input file*.tsv
log Name of log file (created only when specified)
debug 0 Sets the debug level
threads 1 Sets the number of threads allowed to be used by the TOPP tool
no_progress false Disables progress logging to command line true,false
force false Overwrite tool specific checks.true,false
test false Enables the test mode (needed for internal use only)true,false
+++reference Options to define a reference file (use either 'file' or 'index', not both)
file File to use as reference input file*.featureXML,*.consensusXML,*.idXML
index 0 Use one of the input files as reference ('1' for the first file, etc.).
If '0', no explicit reference is set - the algorithm will select a reference.0:∞
+++algorithm Algorithm parameters section
score_cutoff false If only IDs above a score cutoff should be used. Used together with min_score.true,false
min_score 0.05 Minimum score for an ID to be considered. Applies to the last score calculated.
Unless you have very few runs or identifications, increase this value to focus on more informative peptides.
min_run_occur 2 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_shift 0.5 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_peptides true 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_rt false 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
+++model Options to control the modeling of retention time transformations from data
type b_spline Type of model linear,b_spline,lowess,interpolated
++++linear Parameters for 'linear' model
symmetric_regression false Perform linear regression on 'y - x' vs. 'y + x', instead of on 'y' vs. 'x'.true,false
x_weight Weight x values 1/x,1/x2,ln(x),
y_weight Weight y values 1/y,1/y2,ln(y),
x_datum_min 1.0e-15 Minimum x value
x_datum_max 1.0e15 Maximum x value
y_datum_min 1.0e-15 Minimum y value
y_datum_max 1.0e15 Maximum y value
++++b_spline Parameters for 'b_spline' model
wavelength 0.0 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_nodes 5 Number of nodes for B-spline fitting. Overrides 'wavelength' if set (to two or greater). A lower value means more smoothing.0:∞
extrapolate linear 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_condition 2 Boundary condition at B-spline endpoints: 0 (value zero), 1 (first derivative zero) or 2 (second derivative zero)0:2
++++lowess Parameters for 'lowess' model
span 0.666666666666667 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_iterations 3 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_type cspline 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 interpolation linear,cspline,akima
extrapolation_type four-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
++++interpolated Parameters for 'interpolated' model
interpolation_type cspline Type of interpolation to apply.linear,cspline,akima
extrapolation_type two-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