Here is the description of the parameters that could be modified by the user.

Defines the algorithms and methods to used for signal extraction and deistotping.

• Start Extraction from XIC from :
  1. MS/MS Events: supervised strategy where each feature extraction is targeted for each acquired MS/MS spectrum.
  2. Validated Peptides: same strategy but with a filtering of MS/MS events based on the list of validated peptides.
  3. Raw MS signal analysis: unsupervised strategy which tries to detect LC-MS features using a signal recognition algorithm

• Deisotoping mode:
  1. Unsupervised: an algorithm combining time correlated isotopes elution peaks. In the final step, this algorithm checks the matching between experimental and theoretical isotopes ratios.
  2. Identification based: the charge state of each PSM (Peptide Spectrum Match) is used to combine isotopes signals into an LC-MS feature.

Parameters use by signal extraction algorithms

Extraction m/z Tolerance: In supervised algorithms this correpsonds to the error tolerance between the precursor ion m/z and peaks extracted in the mzDB file.
In unsupervised algorithms this corresponds to the the error tolerance between each peak apex and other extracted peaks.

Clustering must be applied to the imported LC-MS maps to group features that are close in time and m/z. This step reduces ambiguities and errors that could occur during the feature mapping phase.

• Time tolerance: features that are close in time are grouped. If delta time between two features is lower than time tolerance, features are grouped.
• m/z tolerance: features that are close in m/z are grouped. If delta m/z between two features is lower than m/z tolerance, features are grouped.
• m/z tolerance unit: m/z tolerance can be provided in PPM or Dalton.
• Cluster time computation: you have the choice between 2 computation methods: most intense or median. For most intense method, the cluster time corresponds to the time of the most intense feature composing the cluster. For median method, cluster time is the median of the feature times forming the cluster.
• Cluster intensity computation: you have the choice between 2 computation methods: most intense or sum. For most intense method, the cluster intensity corresponds to the intensity of the most intense feature of features forming the cluster. For sum method, cluster intensity is the sum of the intensities of features composing the cluster.

This is an important step in the LCMS process. It consists of aligning maps of the map set to correct the RT values. RT shifts of shared features between the compared maps follow a curve reflecting the fluctuations of the LC separation. The time deviation trend is obtained by computing a moving median using a smoothing algorithm. This trend is then used as a model of the alignment of the compared LC-MS maps. This model provides a basis for the correction of RT values.

• Method : You have the choice between 2 alignment methods
  1. Comprehensive: the comprehensive algorithm computes the distance between maps for each possible couple of maps and selects the map with the lowest sum of distances to be the reference map.
  2. Iterative: for the iterative algorithm, first a reference map is chosen randomly, then each other map is aligned against the reference and the algorithm computes the distance for each couple of maps. The map that has the smallest distance becomes the reference map. The 2 previous steps are re-iterated until either reference map stays the same between two iterations or the maximum number of iterations is reached.

Then all other maps are aligned to this computed reference map and their retention times are corrected.

• Maximum number of iterations: this option is available only for iterative method. This is a stop condition of the iterative algorithm, when the algorithm has reached its maximum number of iterations, it stops.

• m/z tolerance: m/z window used to match features between two compared maps.

• m/z tolerance unit: m/z tolerance can be provided in PPM or Dalton.

• Time tolerance in seconds: time window used to match features between two compared maps.

When alignment is done, a trend can be extracted with a smoothing method permitting the correction of the aligned map retention time.

• Smoothing method: you have the choice between 2 smoothing methods, time window or landmark range.

• Number of landmarks/time interval: if selected smoothing method is landmark range, time of aligned map is corrected using median computed on windows containing a specified number of landmarks. The run is divided in windows of size the specified number of landmarks. You have to provide the number of landmarks by window. The smoothing method is applied considering the number of landmarks present in the window, and computes the median point for this window.

If selected smoothing method is set to time window, time of aligned map is corrected using median in a time window. You have to provide the time interval. This time interval corresponds to the window size in which time median will be computed.

• Minimum number of landmarks in window: this option is only available for time window smoothing method. This allows you to specify the minimum number of landmarks a window must contain to compute a median on it, it is not significant to compute a median on less landmarks.

• Sliding window overlap: overlap is used to compute the step to move the smoothing window forward to calculate a smoothing point for this new smoothing window. Overlap gives the percentage of overlapping between two consecutive windows. For example, if window size is 200 (seconds or landmarks depending on which smoothing method is selected) and overlap is 20%, the step forward = 200*((100-20)/100) = 160 seconds or landmarks, i.e. the smoothing window will be moved forward by a step of 160, so two successive windows will overlap each other by a step of 40 seconds or landmarks corresponding to 20% of 200.

This step consists in creating the “master map” (also called consensus map), this map resulting from the superimposition of all compared maps.

• m/z tolerance: when mapping features from 2 different maps of the map set, delta m/z between features must be lower than the m/z tolerance to be considered as the same feature seen on 2 different maps.

• m/z tolerance unit: the m/z tolerance unit can be provided in PPM or Dalton.

• Time tolerance (seconds): when mapping features from 2 different maps of the map set, delta time between features must be lower than the time tolerance to be considered as the same feature seen on 2 different maps.

• Normalization method: sometimes the ratio distribution is not centered around zero as we could have expected if data were exactly reproducible. Intensity normalization (by applying a mathematical transformation) is thus needed to reduce the impact of experimental artifacts and ensure accurate quantification. Three methods are available:
  1. Median ratio normalization method algorithm: first, compute sum of feature intensities for each map of the map set and sort maps by computed intensities. The map ranking nearest from the median is taken as the reference map. Then for each master map feature, compute ratio as reference map feature intensity / feature intensity for the considered map. The normalization factor corresponds to the median of the computed ratios.
  2. Median normalization method algorithm: first compute median intensity for each map, set the reference map to median map, normalization factor for map M = reference map median intensity / map M median intensity.
  3. Sum normalization method algorithm: first, compute feature intensities sums for each map, set the reference map to the median map, normalization factor for map M = intensities sum of reference map / intensities sum of map M.

• Master feature filter type: a filter can be applied to the map features to keep the best features (above threshold) to build the master map.

Two methods are available to filter features: the filter can be applied directly on intensity values (Intensity method) or it can be a proportion of the map median intensity (Relative intensity method).

• Relative intensity threshold/Intensity threshold: this provides the threshold for one or the other filtering method, depending on which method you have selected. Only features above this threshold will be considered for the master map building process.

• Relative intensity/Intensity method: this option depends on which filtering method you select.

If you choose Relative intensity for master feature filter type, the only possibility you have is percent, so you will remove features which intensities are beyond the relative intensity threshold in percentage of the median intensity. If you choose Intensity for master feature filter type, you also have only one possibility at the moment of the intensity method: basic. Features which intensities are beyond the intensity threshold set will be removed and not considered for the master map building process.