Fuzzy C-Means (FCM) Clustering

Fuzzy C-Means (FCM) Clustering

Usage

run_cmeans(
  selected_tissues = c("all", "adipose", "blood", "muscle"),
  selected_omes = c("transcript-rna-seq", "prot-pr", "prot-ol", "prot-ph", "metab"),
  num_clusters_adipose = 13L,
  num_clusters_blood = 13L,
  num_clusters_muscle = 12L,
  modality = c("both", "Endur", "Resist")
)

Arguments

selected_tissues

character; passed to load_differential_analysis. One or more of the following: "all", "muscle", "adipose", or "blood".

selected_omes

character; one or more of "transcript-rna-seq", "prot-pr", "prot-ol", "prot-ph", or "metab".

num_clusters_adipose, num_clusters_blood, num_clusters_muscle

integer; the number of clusters desired for each tissue. Defaults to 13 for adipose, 13 for blood, and 12 for muscle. If more than one number is provided, Mfuzz::Dmin will be used to generate a plot of the minimum centroid distances, and the user will be asked to specify the optimal cluster number in the console for each tissue.

modality

character; which exercise modalities should be used for FCM? One of "both", "Endur", or "Resist".

Value

A named list of objects where names are tissues. Each object is of class "fclust" with additional list component "input" for the matrix of scaled z-scores used as input for FCM (both modalities included, even when modality != "both").

See also

plot_cmeans, run_cluster_cameraPR, run_cluster_ORA

Author

Tyler Sagendorf, Christopher Jin

Examples

if (FALSE) { # \dontrun{
  x1 <- run_cmeans()
  names(x1) # list available components

  # Try a range of cluster numbers for one tissue
  x2 <- run_cmeans(selected_tissues = "adipose",
                   num_clusters_adipose = 3:14)

  # FCM for a single modality
  x3 <- run_cmeans(selected_tissues = "adipose",
                   modality = "Endur")
} # }