R/generics.R, R/components-intclust.R
generateComponentsIntClust.RdGenerates components based on intensity profiles of feature groups.
generateComponentsIntClust(fGroups, ...)
# S4 method for class 'featureGroups'
generateComponentsIntClust(
fGroups,
method = "complete",
metric = "euclidean",
normalized = TRUE,
average = TRUE,
maxTreeHeight = 1,
deepSplit = TRUE,
minModuleSize = 1
)featureGroups object for which components should be generated.
Any parameters to be passed to the selected component generation algorithm.
Clustering method that should be applied (passed to
fastcluster::hclust).
Distance metric used to calculate the distance matrix (passed to daisy).
Passed to as.data.table to perform
normalization and averaging of data.
Arguments used by
cutreeDynamicTree.
The components are stored in objects derived from componentsIntClust.
This function uses hierarchical clustering of intensity profiles to generate components. This function is called when calling generateComponents with
algorithm="intclust".
Hierarchical clustering is performed on normalized (and optionally replicate averaged) intensity data and
the resulting dendrogram is automatically cut with cutreeDynamicTree. The distance matrix is
calculated with daisy and clustering is performed with
fastcluster::hclust. The clustering of the resulting components can be further
visualized and modified using the methods defined for componentsIntClust.
In a sets workflow normalization of feature intensities occur per set.
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 .
Schollee JE, Bourgin M, von Gunten U, McArdell CS, Hollender J (2018). “Non-target screening to trace ozonation transformation products in a wastewater treatment train including different post-treatments.” Water Research, 142, 267–278. doi:10.1016/j.watres.2018.05.045 .
generateComponents for more details and other algorithms.