ProteomicsLFQ

Options

version

Version of the tool that generated this parameters file.

proteinFDR

Protein FDR threshold (0.05=5%).

picked_proteinFDR

Use a picked protein FDR?

psmFDR

FDR threshold for sub-protein level (e.g. 0.05=5%). Use -FDR_type to choose the level. Cutoff is applied at the highest level. If Bayesian inference was chosen, it is equivalent with a peptide FDR

FDR_type

Sub-protein FDR level. PSM, PSM+peptide (best PSM q-value).

protein_inference

Infer proteins: aggregation = aggregates all peptide scores across a protein (using the best score) bayesian = computes a posterior probability for every protein based on a Bayesian network. Note: 'bayesian' only uses and reports the best PSM per peptide.

protein_quantification

Quantify proteins based on: unique_peptides = use peptides mapping to single proteins or a group of indistinguishable proteins(according to the set of experimentally identified peptides). strictly_unique_peptides = use peptides mapping to a unique single protein only. shared_peptides = use shared peptides only for its best group (by inference score)

quantification_method

feature_intensity: MS1 signal. spectral_counting: PSM counts.

targeted_only

true: Only ID based quantification. false: include unidentified features so they can be linked to identified ones (=match between runs).

transfer_ids

Requantification using mean of aligned RTs of a peptide feature. Only applies to peptides that were quantified in more than 50% of all runs (of a fraction).

mass_recalibration

Mass recalibration.

alignment_order

If star, aligns all maps to the reference with most IDs,if treeguided, calculates a guiding tree first.

keep_feature_top_psm_only

If false, also keeps lower ranked PSMs that have the top-scoring sequence as a candidate per feature in the same 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

Overrides tool-specific checks

test

Enables the test mode (needed for internal use only)

intThreshold

Peak intensity threshold applied in seed detection.

charge

Charge range considered for untargeted feature seeds.

traceRTTolerance

Combines all spectra in the tolerance window to stabilize identification of isotope patterns. Controls sensitivity (low value) vs. specificity (high value) of feature seeds.

signal_to_noise

Minimal signal-to-noise ratio for a peak to be picked (0.0 disables SNT estimation!)

spacing_difference_gap

The extension of a peak is stopped if the spacing between two subsequent data points exceeds 'spacing_difference_gap * min_spacing'. 'min_spacing' is the smaller of the two spacings from the peak apex to its two neighboring points. '0' to disable the constraint. Not applicable to chromatograms.

spacing_difference

Maximum allowed difference between points during peak extension, in multiples of the minimal difference between the peak apex and its two neighboring points. If this difference is exceeded a missing point is assumed (see parameter 'missing'). A higher value implies a less stringent peak definition, since individual signals within the peak are allowed to be further apart. '0' to disable the constraint. Not applicable to chromatograms.

missing

Maximum number of missing points allowed when extending a peak to the left or to the right. A missing data point occurs if the spacing between two subsequent data points exceeds 'spacing_difference * min_spacing'. 'min_spacing' is the smaller of the two spacings from the peak apex to its two neighboring points. Not applicable to chromatograms.

ms_levels

List of MS levels for which the peak picking is applied. If empty, auto mode is enabled, all peaks which aren't picked yet will get picked. Other scans are copied to the output without changes.

report_FWHM

Add metadata for FWHM (as floatDataArray named 'FWHM' or 'FWHM_ppm', depending on param 'report_FWHM_unit') for each picked peak.

report_FWHM_unit

Unit of FWHM. Either absolute in the unit of input, e.g. 'm/z' for spectra, or relative as ppm (only sensible for spectra, not chromatograms).

max_intensity

maximal intensity considered for histogram construction. By default, it will be calculated automatically (see auto_mode). Only provide this parameter if you know what you are doing (and change 'auto_mode' to '-1')! All intensities EQUAL/ABOVE 'max_intensity' will be added to the LAST histogram bin. If you choose 'max_intensity' too small, the noise estimate might be too small as well. If chosen too big, the bins become quite large (which you could counter by increasing 'bin_count', which increases runtime). In general, the Median-S/N estimator is more robust to a manual max_intensity than the MeanIterative-S/N.

auto_max_stdev_factor

parameter for 'max_intensity' estimation (if 'auto_mode' == 0): mean + 'auto_max_stdev_factor' * stdev

auto_max_percentile

parameter for 'max_intensity' estimation (if 'auto_mode' == 1): auto_max_percentile th percentile

auto_mode

method to use to determine maximal intensity: -1 --> use 'max_intensity'; 0 --> 'auto_max_stdev_factor' method (default); 1 --> 'auto_max_percentile' method

win_len

window length in Thomson

bin_count

number of bins for intensity values

min_required_elements

minimum number of elements required in a window (otherwise it is considered sparse)

noise_for_empty_window

noise value used for sparse windows

write_log_messages

Write out log messages in case of sparse windows or median in rightmost histogram bin

debug

Debug level for feature detection.

quantify_decoys

Whether decoy peptides should be quantified (true) or skipped (false).

min_psm_cutoff

Minimum score for the best PSM of a spectrum to be used as seed. Use 'none' for no cutoff.

batch_size

Nr of peptides used in each batch of chromatogram extraction. Smaller values decrease memory usage but increase runtime.

mz_window

m/z window size for chromatogram extraction (unit: ppm if 1 or greater, else Da/Th)

n_isotopes

Number of isotopes to include in each peptide assay.

isotope_pmin

Minimum probability for an isotope to be included in the assay for a peptide. If set, this parameter takes precedence over 'extract:n_isotopes'.

rt_quantile

Quantile of the RT deviations between aligned internal and external IDs to use for scaling the RT extraction window

rt_window

RT window size (in sec.) for chromatogram extraction. If set, this parameter takes precedence over 'extract:rt_quantile'.

min_peak_width

Minimum elution peak width. Absolute value in seconds if 1 or greater, else relative to 'peak_width'.

signal_to_noise

Signal-to-noise threshold for OpenSWATH feature detection

mapping_tolerance

RT tolerance (plus/minus) for mapping peptide IDs to features. Absolute value in seconds if 1 or greater, else relative to the RT span of the feature.

samples

Number of observations to use for training ('0' for all)

no_selection

By default, roughly the same number of positive and negative observations, with the same intensity distribution, are selected for training. This aims to reduce biases, but also reduces the amount of training data. Set this flag to skip this procedure and consider all available observations (subject to 'svm:samples').

kernel

SVM kernel

xval

Number of partitions for cross-validation (parameter optimization)

log2_C

Values to try for the SVM parameter 'C' during parameter optimization. A value 'x' is used as 'C = 2^x'.

log2_gamma

Values to try for the SVM parameter 'gamma' during parameter optimization (RBF kernel only). A value 'x' is used as 'gamma = 2^x'.

log2_p

Values to try for the SVM parameter 'epsilon' during parameter optimization (epsilon-SVR only). A value 'x' is used as 'epsilon = 2^x'.

epsilon

Stopping criterion

cache_size

Size of the kernel cache (in MB)

no_shrinking

Disable the shrinking heuristics

predictors

Names of OpenSWATH scores to use as predictors for the SVM (comma-separated list)

min_prob

Minimum probability of correctness, as predicted by the SVM, required to retain a feature candidate

type

Type of elution model to fit to features

add_zeros

Add zero-intensity points outside the feature range to constrain the model fit. This parameter sets the weight given to these points during model fitting; '0' to disable.

unweighted_fit

Suppress weighting of mass traces according to theoretical intensities when fitting elution models

no_imputation

If fitting the elution model fails for a feature, set its intensity to zero instead of imputing a value from the initial intensity estimate

each_trace

Fit elution model to each individual mass trace

min_area

Lower bound for the area under the curve of a valid elution model

boundaries

Time points corresponding to this fraction of the elution model height have to be within the data region used for model fitting

width

Upper limit for acceptable widths of elution models (Gaussian or EGH), expressed in terms of modified (median-based) z-scores. '0' to disable. Not applied to individual mass traces (parameter 'each_trace').

asymmetry

Upper limit for acceptable asymmetry of elution models (EGH only), expressed in terms of modified (median-based) z-scores. '0' to disable. Not applied to individual mass traces (parameter 'each_trace').

max_iteration

Maximum number of iterations for EMG fitting.

init_mom

Alternative initial parameters for fitting through method of moments.

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.

use_identifications

Never link features that are annotated with different peptides (only the best hit per peptide identification is taken into account).

nr_partitions

How many partitions in m/z space should be used for the algorithm (more partitions means faster runtime and more memory efficient execution).

min_nr_diffs_per_bin

If IDs are used: How many differences from matching IDs should be used to calculate a linking tolerance for unIDed features in an RT region. RT regions will be extended until that number is reached.

min_IDscore_forTolCalc

If IDs are used: What is the minimum score of an ID to assume a reliable match for tolerance calculation. Check your current score type!

noID_penalty

If IDs are used: For the normalized distances, how high should the penalty for missing IDs be? 0 = no bias, 1 = IDs inside the max tolerances always preferred (even if much further away).

ignore_charge

false [default]: pairing requires equal charge state (or at least one unknown charge '0'); true: Pairing irrespective of charge state

ignore_adduct

true [default]: pairing requires equal adducts (or at least one without adduct annotation); true: Pairing irrespective of adducts

exponent

Normalized RT differences ([0-1], relative to 'max_difference') are raised to this power (using 1 or 2 will be fast, everything else is REALLY slow)

weight

Final RT distances are weighted by this factor

max_difference

Never pair features with larger m/z distance (unit defined by 'unit')

unit

Unit of the 'max_difference' parameter

exponent

Normalized ([0-1], relative to 'max_difference') m/z differences are raised to this power (using 1 or 2 will be fast, everything else is REALLY slow)

weight

Final m/z distances are weighted by this factor

exponent

Differences in relative intensity ([0-1]) are raised to this power (using 1 or 2 will be fast, everything else is REALLY slow)

weight

Final intensity distances are weighted by this factor

log_transform

Log-transform intensities? If disabled, d = |int_f2 - int_f1| / int_max. If enabled, d = |log(int_f2 + 1) - log(int_f1 + 1)| / log(int_max + 1))

method

- top - quantify based on three most abundant peptides (number can be changed in 'top'). - iBAQ (intensity based absolute quantification), calculate the sum of all peptide peak intensities divided by the number of theoretically observable tryptic peptides (https://rdcu.be/cND1J). Warning: only consensusXML or featureXML input is allowed!

best_charge_and_fraction

Distinguish between fraction and charge states of a peptide. For peptides, abundances will be reported separately for each fraction and charge; for proteins, abundances will be computed based only on the most prevalent charge observed of each peptide (over all fractions). By default, abundances are summed over all charge states.

N

Calculate protein abundance from this number of proteotypic peptides (most abundant first; '0' for all)

aggregate

Aggregation method used to compute protein abundances from peptide abundances

include_all

Include results for proteins with fewer proteotypic peptides than indicated by 'N' (no effect if 'N' is 0 or 1)

normalize

Scale peptide abundances so that medians of all samples are equal

fix_peptides

Use the same peptides for protein quantification across all samples. With 'N 0',all peptides that occur in every sample are considered. Otherwise ('N'), the N peptides that occur in the most samples (independently of each other) are selected, breaking ties by total abundance (there is no guarantee that the best co-ocurring peptides are chosen!).

Views

ProteomicsLFQ Std Output

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

ProteomicsLFQ Error Output

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

Workflows

• Protein label-free quantificationKNIME Hub
• Protein label-free quantificationKNIME Hub
• ProteomicsLFQ_toolKNIME Hub
• ProteomicsLFQ_tool_and_MSstats_postprocessingOpenMS
• ProteomicsLFQ_tool_spectral_countingKNIME Hub

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