nf-core/metaboigniter

set whether you want to do quantification with OpenMS (openms) or XCMS (xcms) in negative ionization (for library)

Controls how to perform IPO

Quantification methods for IPO

lowest level of noise

highest level of noise

lowest level of signal to noise threshold

highest level of signal to noise threshold

Function for centering the mz

Integration method

logical, if TRUE a Gaussian is fitted

lower minimum width of peaks

higher minimum width of peaks

lower maximum width of peaks

higher maximum width of peaks

lower ppm mass deviation

higher ppm mass deviation

lower minimum difference in m/z for peaks with overlapping retention times, can be negative to allow overlap

higher minimum difference in m/z for peaks with overlapping retention times, can be negative to allow overlap

maximum charge of molecules (only used in individual setting)

ppm mass deviation for adducts (only used in individual setting)

lower value of K in ‘prefilter_library_neg=c(k,I)’. Prefilter step for the first phase. Mass traces are only retained if they contain at least ‘k’ peaks with intensity >_library_neg= ‘I’.

higher value of K in ‘prefilter_library_neg=c(k,I)’. Prefilter step for the first phase. Mass traces are only retained if they contain at least ‘k’ peaks with intensity >_library_neg= ‘I’.

lower I in prefilter

higher I in prefilter

number of cores used in IPO

lower Penalty for Gap opening

higher Penalty for Gap opening

lower Penalty for Gap enlargement

higher Penalty for Gap enlargement

lower step size (in m/z) to use for profile generation from the raw data files

higher step size (in m/z) to use for profile generation from the raw data files

lower Responsiveness of warping. 0 will give a linear warp based on start and end points. 100 will use all bijective anchors

higher Responsiveness of warping. 0 will give a linear warp based on start and end points. 100 will use all bijective anchors

lower Local weighting applied to diagonal moves in alignment.

higher Local weighting applied to diagonal moves in alignment.

lower Local weighting applied to gap moves in alignment.

higher Local weighting applied to gap moves in alignment.

Local rather than global alignment

lower bandwidth (consider something like retention time differences)

higher bandwidth (consider something like retention time differences)

lower minimum fraction of samples necessary in at least one of the sample groups for it to be a valid group

higher minimum fraction of samples necessary in at least one of the sample groups for it to be a valid group

lower mz width (mz differences)

higher mz width (mz differences)

lower minimum number of samples necessary in at least one of the sample groups for it to be a valid group

higher minimum number of samples necessary in at least one of the sample groups for it to be a valid group

lower maximum number of groups to identify in a single m/z slice

higher maximum number of groups to identify in a single m/z slice

DistFunc function: cor (Pearson’s R) or cor_opt (default, calculate only 10% diagonal band of distance matrix, better runtime), cov (covariance), prd (product), euc (Euclidean distance)

Only obiwarp is supported

mass trace deviation in ppm

lower width of peaks

highest width of peaks

level of noise

minimum difference in m/z for peaks with overlapping retention times, can be negative to allow overlap

signal to noise ratio cutoff, definition see below.

K in ‘prefilter=c(k,I)’. Prefilter step for the first phase. Mass traces are only retained if they contain at least ‘k’ peaks with intensity >= ‘I’.

I in prefilter

Function to calculate the m/z center of the feature: ‘wMean’ intensity weighted mean of the feature m/z values, ‘mean’ mean of the feature m/z values, ‘apex’ use m/z value at peak apex, ‘wMeanApex3’ intensity weighted mean of the m/z value at peak apex and the m/z value left and right of it, ‘meanApex3’ mean of the m/z value at peak apex and the m/z value left and right of it.

Integration method. If ‘=1’ peak limits are found through descent on the mexican hat filtered data, if ‘=2’ the descent is done on the real data. Method 2 is very accurate but prone to noise, while method 1 is more robust to noise but less exact.

logical, if TRUE a Gaussian is fitted

A name for the class of sample

sigma value for grouping the peaks across chromatogram

full width at half maximum for finding overlaping peaks

which intensity value to use

ppm deviation between theoritical adduct mass and the experimental one

this has to be negative (for testing only)!

number of changes to consider (most often 1 is enough)

ppm deviation when mapping MS2 parent ion to a mass trace

rt difference (in second) for mapping MS2 parent ion to a mass trace (the mass trace is a range, star and end of the trace)

name of the column showing which raw files contain which metabolite in the library_description_neg csv file

name of the column showing id of the metabolites in the library_description_neg csv file

name of the compount column in the library_description_neg csv file

name of the mz column in the library_description_neg csv file

“f” or “c”, showing whether the Feature range or centroid of the feature should be used for mapping

Number of cores for mapping the features

ppm error for mapping the library characterization masses to the experimental one

set to true if you want to remove signal from blank

method of sumarization of signal in blank samples

Name of the class of the blank samples

Name of the class of the biological samples

set to T to compare blanks only to rest of the samples

Whether blank filtereing should be done or not?

This series will used for calculation of correlation. For example if this parameter is set like 1,2,3 and the class of dilution trends is set as D1,D2,D3 the following the pairs will be used for calculating the correlation: (D1,1),(D2,2),(D3,3)

The class of the samples represneting dilution. This has to be separated by comma!

p-value of the correlation. Anything higher than this will be removed!

minimum expected correlation. Aniything lower than this will be removed!

If the tool should consider absolute correlation rather than the typical one from [-1 to 1] (F or T)

select to whether perfrom cv filtering or not

class of your QC samples

Maximum coefficient of variation you expect. Anything higher than this will be removed!

set whether you want to do quantification with OpenMS (openms) or XCMS (xcms) in positive ionization (for library)

controls how to perform IPO possible values: “none”: don’t perform IPO, “global”: performs IPO on all or selected number of samples. “local”: performs IPO on individual samples one at the time.

Quantification methods for IPO. Only centWave is supported at this stage.

lowest level of noise

highest level of noise

lowest level of signal to noise threshold

highest level of signal to noise threshold

Function for centering the mz

Integration method. If ‘=1’ peak limits are found through descent on the mexican hat filtered data, if ‘=2’ the descent is done on the real data. Method 2 is very accurate but prone to noise, while method 1 is more robust to noise but less exact.

logical, if TRUE a Gaussian is fitte

lower minimum width of peaks

higher minimum width of peaks

lower maximum width of peaks

higher maximum width of peaks

lower ppm mass deviation

higher ppm mass deviation

lower minimum difference in m/z for peaks with overlapping retention times, can be negative to allow overlap

higher minimum difference in m/z for peaks with overlapping retention times, can be negative to allow overlap

maximum charge of molecules (only used in individual setting)

ppm mass deviation for adducts (only used in individual setting)

lower value of K in ‘prefilter_library_pos=c(k,I)’. Prefilter step for the first phase. Mass traces are only retained if they contain at least ‘k’ peaks with intensity >_library_pos= ‘I’.

higher value of K in ‘prefilter_library_pos=c(k,I)’. Prefilter step for the first phase. Mass traces are only retained if they contain at least ‘k’ peaks with intensity >_library_pos= ‘I’.

lower I in prefilter

higher I in prefilter

number of cores used in IPO

lower Penalty for Gap opening

higher Penalty for Gap opening

lower Penalty for Gap enlargement

higher Penalty for Gap enlargement

lower step size (in m/z) to use for profile generation from the raw data files

higher step size (in m/z) to use for profile generation from the raw data files

lower Responsiveness of warping. 0 will give a linear warp based on start and end points. 100 will use all bijective anchors

higher Responsiveness of warping. 0 will give a linear warp based on start and end points. 100 will use all bijective anchors

lower Local weighting applied to diagonal moves in alignment.

higher Local weighting applied to diagonal moves in alignment.

lower Local weighting applied to gap moves in alignment.

higher Local weighting applied to gap moves in alignment.

Local rather than global alignment

lower bandwidth (consider something like retention time differences)

higher bandwidth (consider something like retention time differences)

lower minimum fraction of samples necessary in at least one of the sample groups for it to be a valid group

higher minimum fraction of samples necessary in at least one of the sample groups for it to be a valid group

lower mz width (mz differences)

higher mz width (mz differences)

lower minimum number of samples necessary in at least one of the sample groups for it to be a valid group

higher minimum number of samples necessary in at least one of the sample groups for it to be a valid group

lower maximum number of groups to identify in a single m/z slice

higher maximum number of groups to identify in a single m/z slice

DistFunc function: cor (Pearson’s R) or cor_opt (default, calculate only 10% diagonal band of distance matrix, better runtime), cov (covariance), prd (product), euc (Euclidean distance)

Only obiwarp is supported

mass trace deviation in ppm

lower width of peaks

highest width of peaks

level of noise

minimum difference in m/z for peaks with overlapping retention times, can be negative to allow overlap

signal to noise ratio cutoff, definition see below.

K in ‘prefilter=c(k,I)’. Prefilter step for the first phase. Mass traces are only retained if they contain at least ‘k’ peaks with intensity >= ‘I’.

I in prefilter

Function to calculate the m/z center of the feature: ‘wMean’ intensity weighted mean of the feature m/z values, ‘mean’ mean of the feature m/z values, ‘apex’ use m/z value at peak apex, ‘wMeanApex3’ intensity weighted mean of the m/z value at peak apex and the m/z value left and right of it, ‘meanApex3’ mean of the m/z value at peak apex and the m/z value left and right of it.

Integration method. If ‘=1’ peak limits are found through descent on the mexican hat filtered data, if ‘=2’ the descent is done on the real data. Method 2 is very accurate but prone to noise, while method 1 is more robust to noise but less exact.

logical, if TRUE a Gaussian is fitted

A name for the class of sample

sigma value for grouping the peaks across chromatogram

full width at half maximum for finding overlaping peaks

which intensity value to use

ppm deviation between theoritical adduct mass and the experimental one

this has to be positive (for testing only)!

number of changes to consider (most often 1 is enough)

ppm deviation when mapping MS2 parent ion to a mass trace

rt difference (in second) for mapping MS2 parent ion to a mass trace (the mass trace is a range, star and end of the trace)

name of the column showing which raw files contain which metabolite in the library_description_pos csv file

name of the column showing id of the metabolites in the library_description_pos csv file

name of the compount column in the library_description_pos csv file

name of the mz column in the library_description_pos csv file

“f” or “c”, showing whether the Feature range or centroid of the feature should be used for mapping

Number of cores for mapping the features

ppm error for mapping the library characterization masses to the experimental one

set to true if you want to remove signal from blank

method of sumarization of signal in blank samples. Must be one of ‘max’, ‘mean’ or ‘median’. For example, if ‘max’ is selected, a signal will be removed if it maximum abundance in the blank samples is higher than maximum abundance in biological samples.

Name of the class of the blank samples. This must show the class of blank samples exactly as you refer to them in your phenotype file

Name of the class of the biological samples

set to T to compare the blanks only to rest of the samples. If F, the blank signals will be compared with the samples with class sample_blankfilter_pos_xcms

Select whether you want to do dilution filtering

This series will used for calculation of correlation. For example if this parameter is set like 1,2,3 and the class of dilution trends is set as D1,D2,D3 the following the pairs will be used for calculating the correlation: (D1,1),(D2,2),(D3,3)

The class of the samples represneting dilution. This has to be separated by comma! The samples are correlated to the exact order of the sequence provided here

p-value of the correlation. Anything higher than this will be removed!

minimum expected correlation. Aniything lower than this will be removed!

If the tool should consider absolute correlation rather than the typical one from [-1 to 1] (F or T)

select to whether perfrom cv filtering or not

class of your QC samples

Maximum coefficient of variation you expect. Anything higher than this will be removed!

if you have already charaztrized your negative library set this to true and specify the path for library_charactrization_file_neg

path to the file from charaztrized library (negative)

Path to a folder containing library mzML files used for doing adduct calcculation (MS1 data in megative ionization method)

Path to a folder containing mzML files used for doing identification (MS2 data in negative ionization method)

Path to a csv file containing description of the library for negative (see the help)

if you have already charaztrized your positive library set this to true and specify the path for library_charactrization_file_pos

path to the file from charaztrized library (positive)

Path to a folder containing library mzML files used for doing adduct calcculation (MS1 data in positive ionization method)

Path to a folder containing mzML files used for doing identification (MS2 data in positive ionization method)

Path to a csv file containing description of the library for positive (see the help)

Path to the ini file for PeakPickerHiRes

Path to the ini file for PeakPickerHiRes

Path to the ini file for OpenMS FeatureFinderMetabo in positive mode

Path to the ini file for OpenMS FeatureFinderMetabo in negative mode

Path to the ini file for PeakPickerHiRes (for library)

Path to the ini file for PeakPickerHiRes (for library)

Path to the ini file for OpenMS FeatureFinderMetabo in positive mode (for library)

Path to the ini file for OpenMS FeatureFinderMetabo in negative mode (for library)

Display help text.

Method used to save pipeline results to output directory.

Boolean whether to validate parameters against the schema at runtime

Email address for completion summary, only when pipeline fails.

Send plain-text email instead of HTML.

Do not use coloured log outputs.

Directory to keep pipeline Nextflow logs and reports.

Show all params when using --help

Maximum number of CPUs that can be requested for any single job.

Maximum amount of memory that can be requested for any single job.

Maximum amount of time that can be requested for any single job.

Git commit id for Institutional configs.

Base directory for Institutional configs.

Institutional configs hostname.

Institutional config name.

Institutional config description.

Institutional config contact information.

Institutional config URL link.