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metaCorrelate feature grouping

Feature list methods → Feature grouping → Correlation grouping (metaCorrelate)

This module groups features based on various properties: - Retention time - Feature shape Pearson correlation (optional: Only apply if at least 5 data points) - Feature height correlation


When using this modules, please consider citing the corresponding publications:
Schmid, R., Petras, D., Nothias, LF. et al. Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment. Nat Commun 12, 3832 (2021).

Schmid R., Heuckeroth S., Korf A., et al. Integrative analysis of multimodal mass spectrometry data in MZmine 3, In Review (2023)


metaCorr dialog

RT tolerance

First filter that only checks features that fall within the same retention time window. Should be strict (~FWHM / 3) when correlation grouping is disabled. With correlation grouping as a strict filter, the RT tolerance can be wider.

Min height

Minimum height of features to consider. Leave at 0 to use all features that passed the feature detection workflow criteria.

Intensity correlation threshold

Minimum intensity of data points in a feature to compare during feature shape Pearson correlation. All data points below this value are disregarded. Leave at 0 to use the noise levels set in the mass detection steps.

Sample set (optional)

Grouping of samples, only when using Min samples filter. Needs project metadata to be set.

Min samples filter

Only group features if they were detected in a minimum number of samples (absolute and relative minimum). Values are provided for - Min samples in all (all samples) and - Min samples in group (for groups from Sample set parameter) - Min %-intensity overlap defines the percentage of intensity (sum of data point intensity) of the lower abundant feature needs to fall within the RT range of the larger feature - Exclude estimated features (gap-filled) excludes gap-filled features from the comparison

Correlation grouping (optional)

Applies a feature shape correlation filter in retention time dimension.


Only use when having enough data points, i.e., 5 data points total and 2 on each side of the apex. Otherwise,

use feature height correlation and a more narrow RT tolerance

Parameters: - Min data points: Minimum number of correlated data points - Min data points on edge: Minimum number of points on each sides of the apex - Measure: Similarity measure (default: Pearson) - Min feature shape correlation: Minimum similarity of two features (within the same sample) to be grouped. Pearson r=85% is default. - Min total correlation (optional): Minimum similarity when taking all the data points from all samples into account

Feature height correlation

Applies a correlation filter by taking all the feature heights across samples for feature pairs.


Only applicable if the heights are comparable across samples: Similar matrix and ionization conditions

Parameters: - Min data points: Minimum number of correlated data points (samples) - Measure: Similarity measure (default: Pearson) - Min correlation: Minimum similarity

Suffix (or auto) (optional)

Add a suffix to the feature list or just use an automatically generated suffix based on the parameters.

Robin Schmid

Last update: January 17, 2023 13:51:01