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Cluster correlated features originating from the same metabolite

Source: R/feature_clustering.R
cluster_features.Rd

Clusters features potentially originating from the same compound. Features with high Pearson correlation coefficient and small retention time difference are linked together. Then clusters are formed by setting a threshold for the relative degree that each node in a cluster needs to fulfil. Each cluster is named after the feature with the highest median peak area (median abundance). This is a wrapper around numerous functions that are based on the MATLAB code by David Broadhurst.

Usage

cluster_features(
  object,
  mz_col = NULL,
  rt_col = NULL,
  all_features = FALSE,
  rt_window = 1/60,
  corr_thresh = 0.9,
  d_thresh = 0.8,
  plotting = TRUE,
  min_size_plotting = 3,
  prefix = NULL,
  assay.type = NULL
)

Arguments

object

a SummarizedExperiment or MetaboSet object

mz_col

the column name in feature data that holds mass-to-charge ratios

rt_col

the column name in feature data that holds retention times

all_features

logical, should all features be included in the clustering? If FALSE, as the default, flagged features are not included in clustering

rt_window

the retention time window for potential links NOTE: use the same unit as the retention time

corr_thresh

the correlation threshold required for potential links between features

d_thresh

the threshold for the relative degree required by each node

plotting

should plots be drawn for each cluster?

min_size_plotting

the minimum number of features a cluster needs to have to be plotted

prefix

the prefix to the files to be plotted

assay.type

character, assay to be used in case of multiple assays

Value

a SummarizedExperiment or MetaboSet object, with median peak area (MPA), the cluster ID, the features in the cluster, and cluster size added to feature data.

Examples

data(example_set)
# The parameters are really weird because example data is imaginary
clustered <- cluster_features(example_set, rt_window = 1, corr_thresh = 0.5, 
  d_thresh = 0.6)
#> INFO [2025-06-23 22:36:26] Identified m/z column Average_Mz and retention time column Average_Rt_min
#> INFO [2025-06-23 22:36:26] Identified m/z column Average_Mz and retention time column Average_Rt_min
#> INFO [2025-06-23 22:36:26] Identified m/z column Average_Mz and retention time column Average_Rt_min
#> INFO [2025-06-23 22:36:26] 
#> Starting feature clustering at 2025-06-23 22:36:26.484164
#> INFO [2025-06-23 22:36:26] Finding connections between features in HILIC_neg
#> INFO [2025-06-23 22:36:26] Found 1 connections in HILIC_neg
#> INFO [2025-06-23 22:36:26] Finding connections between features in HILIC_pos
#> INFO [2025-06-23 22:36:26] Found 4 connections in HILIC_pos
#> INFO [2025-06-23 22:36:26] Finding connections between features in RP_neg
#> INFO [2025-06-23 22:36:26] Found 1 connections in RP_neg
#> INFO [2025-06-23 22:36:26] Finding connections between features in RP_pos
#> INFO [2025-06-23 22:36:26] Found 2 connections in RP_pos
#> INFO [2025-06-23 22:36:26] Found 8 connections
#> 5 components found
#> 1 components found
#> INFO [2025-06-23 22:36:26] Found 5 clusters of 2 or more features, clustering finished at 2025-06-23 22:36:26.968162
#> INFO [2025-06-23 22:36:27] Saved cluster plots to: