MapAlignerTreeGuided

Options

version

Version of the tool that generated this parameters file.

copy_data

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

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

Overrides tool-specific checks

test

Enables the test mode (needed for internal use only)

model_type

Options to control the modeling of retention time transformations from data

type

Type of model

symmetric_regression

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

x_weight

Weight x values

y_weight

Weight y values

x_datum_min

Minimum x value

x_datum_max

Maximum x value

y_datum_min

Minimum y value

y_datum_max

Maximum y value

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.

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_cutoff

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

min_score

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_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'.

use_adducts

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

Views

MapAlignerTreeGuided Std Output

The text sent to standard out during the execution of MapAlignerTreeGuided.

MapAlignerTreeGuided Error Output

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

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