RSA_by_cm: Reporter score analysis after C-means clustering

In ReporterScore: Generalized Reporter Score-Based Enrichment Analysis for Omics Data

Reporter score analysis after C-means clustering

Description

Reporter score analysis after C-means clustering

Extract one cluster from rs_by_cm object

Plot c_means result

Usage

RSA_by_cm(
  kodf,
  group,
  metadata = NULL,
  k_num = NULL,
  filter_var = 0.7,
  verbose = TRUE,
  method = "pearson",
  ...
)

extract_cluster(rsa_cm_res, cluster = 1)

plot_c_means(
  rsa_cm_res,
  filter_membership,
  mode = 1,
  show.clust.cent = TRUE,
  show_num = TRUE,
  ...
)

Arguments

kodf	KO_abundance table, rowname is ko id (e.g. K00001),colnames is samples.
group	The comparison groups (at least two categories) in your data, one column name of metadata when metadata exist or a vector whose length equal to columns number of kodf. And you can use factor levels to change order.
metadata	sample information data.frame contains group
k_num	if NULL, perform the cm_test_k, else an integer
filter_var	see c_means
verbose	verbose
method	method from reporter_score
...	additional
rsa_cm_res	a cm_res object
cluster	integer
filter_membership	filter membership 0~1.
mode	1~2
show.clust.cent	show cluster center?
show_num	show number of each cluster?

Value

rs_by_cm

reporter_score object

ggplot

See Also

Other C_means: cm_test_k()

Examples

message("The following example require some time to run:")

if (requireNamespace("e1071") && requireNamespace("factoextra")) {
  data("KO_abundance_test")
  rsa_cm_res <- RSA_by_cm(KO_abundance, "Group2", metadata,
    k_num = 3,
    filter_var = 0.7, method = "pearson", perm = 199
  )
  extract_cluster(rsa_cm_res, cluster = 1)
}

ReporterScore documentation built on Sept. 11, 2024, 6:44 p.m.