Clustering parameters

Source: R/main.R

Parameters for clustering data such as mass spectra and mobilograms.

Details

Different functionality within patRoon uses clustering to group similar data together, for instance, to average mass spectra. A fast C++ backend based on Rcpp is used to perform the clustering.

The clustering can be configured by the method and window parameter. The following clustering methods are available:

  • "hclust": uses hierarchical clustering to find similar data points (using hclust-cpp, which is based on the fastcluster package).

  • "distance_point": uses a maximum distance between adjacent sorted data points to form clusters.

  • "distance_mean": uses a maximum distance between the mean of the current cluster and the next sorted data point to form clusters.

  • "bin": uses a simple binning approach to cluster data points.

The hclust method may give more accurate results and was the default prior to patRoon 3.0, but is more computationally demanding and generally unsuitable for IMS workflows due to excessive use of RAM. The distance_* methods are now default and suit most cases.

The window parameter defines the clustering tolerance. For method="hclust" this corresponds to the cluster height, for method="distance_*" methods this value sets the maximum distance between compared data and for method="bin" it corresponds to the bin width. Too small windows will prevent clustering close data points (e.g. resulting in split mass peaks in averaged spectra), whereas too big windows may cluster unrelated data points together (e.g. resulting in mass inaccuracies).

Source

Averaging of mass spectra was originally based on algorithms from the msProcess R package (now archived on CRAN).

References

Eddelbuettel D (2013). Seamless R and C++ Integration with Rcpp. Springer, New York. doi:10.1007/978-1-4614-6868-4 , ISBN 978-1-4614-6867-7.

Eddelbuettel D, Balamuta J (2018). “Extending R with C++: A Brief Introduction to Rcpp.” The American Statistician, 72(1), 28-36. doi:10.1080/00031305.2017.1375990 .

Eddelbuettel D, François R (2011). “Rcpp: Seamless R and C++ Integration.” Journal of Statistical Software, 40(8), 1–18. doi:10.18637/jss.v040.i08 .

Eddelbuettel D, Francois R, Allaire J, Ushey K, Kou Q, Russell N, Ucar I, Bates D, Chambers J (2026). Rcpp: Seamless R and C++ Integration. R package version 1.1.1, https://www.rcpp.org.

Müllner D (2013). “fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python.” Journal of Statistical Software, 53(9), 1–18. doi:10.18637/jss.v053.i09 .