IconMapAlignerIdentification0 ×

Generic Workflow Nodes for KNIME: OpenMS version 2.3.0.201712211252 by Freie Universitaet Berlin, Universitaet Tuebingen, and the OpenMS Team

Corrects retention time distortions between maps based on common peptide identifications.

Web Documentation for MapAlignerIdentification

Options

version
Version of the tool that generated this parameters file.
log
Name of log file (created only when specified)
debug
Sets the debug level
threads
Sets the number of threads allowed to be used by the TOPP tool
no_progress
Disables progress logging to command line
force
Overwrite tool specific checks.
test
Enables the test mode (needed for internal use only)
index
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.
min_run_occur
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.
max_rt_shift
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.
use_unassigned_peptides
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.
use_feature_rt
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'.
type
Type of model
symmetric_regression
Perform linear regression on 'y - x' vs. 'y + x', instead of on 'y' vs. 'x'.
wavelength
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.
num_nodes
Number of nodes for B-spline fitting. Overrides 'wavelength' if set (to two or greater). A lower value means more smoothing.
extrapolate
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).
boundary_condition
Boundary condition at B-spline endpoints: 0 (value zero), 1 (first derivative zero) or 2 (second derivative zero)
span
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.
num_iterations
Number of robustifying iterations for lowess fitting.
delta
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
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
extrapolation_type
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.
interpolation_type
Type of interpolation to apply.
extrapolation_type
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.

Input Ports

Input files to align (all must have the same file type) [featureXML,consensusXML,idXML]
input file containing the experimental design [tsv,opt.]
File to use as reference [featureXML,consensusXML,idXML,opt.]

Output Ports

Output files (same file type as 'in'). Either this option or 'trafo_out' has to be provided; they can be used together. [featureXML,consensusXML,idXML,Inactive]
Transformation output files. Either this option or 'out' has to be provided; they can be used together. [trafoXML,Inactive]

Views

MapAlignerIdentification Std Output
The text sent to standard out during the execution of MapAlignerIdentification.
MapAlignerIdentification Error Output
The text sent to standard error during the execution of MapAlignerIdentification. (If it appears in gray, it's the output of a previously failing run which is preserved for your trouble shooting.)

Best Friends (Outgoing)

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