Cluster Features — cluster

cluster_data() allows users to cluster features inside the mass data object. This is done by creating a sparse matrix using the distMs2() function and inputting that inside the clutur package. This allows us to easily cluster features that contain an ms2 spectra.

Usage

cluster_data(
  distance_df,
  ms2_match_data,
  cutoff = 0.3,
  cluster_method = "opticlust"
)

Arguments

distance_df: a distance df that was generated from the distMs2() function.
ms2_match_data: your mass data set object generated from ms2_ms1_compare().
cutoff: the cutoff value you wish to cluster to.
cluster_method: the clustering algorithm you wish to use. The options are: furthest, nearest, weighted, average, and opticlust.

Value

a shared data.frame (or a mothur_cluster object) displaying all the clustered and abundance data.

Examples

data <-
   import_all_data(peak_table =
                   mums2::mums2_example("botryllus_pt_small.csv"),
                   metadata =
                   mums2::mums2_example("boryillus_metadata.csv"),
                   format = "None")


matched_data <- ms2_ms1_compare(mums2_example("botryllus_v2.gnps.mgf"),
 data, 1, 6)
#> Reading: /home/runner/work/_temp/Library/mums2/extdata/botryllus_v2.gnps.mgf ...
#> 17/349 peaks have an MS2 spectra.

dist <- dist_ms2(data = matched_data, cutoff = 0.6, precursor_thresh = 100,
 score_params = modified_cosine_params(0.5), min_peaks = 0,
 number_of_threads = 2)

cluster_results <- cluster_data(distance_df = dist,
 ms2_match_data = matched_data, cutoff = 0.3, cluster_method = "opticlust")