cmeans_clus {dPCP} | R Documentation |
Cluster analysis with fuzzy c-means algorithm
Description
This function carries out the c-means cluster analysis, using the centroids position as initial values for cluster centers.
Usage
cmeans_clus(centers.data)
## S3 method for class 'cmeans_clus'
plot(x, ..., sample = "all", color.blind = FALSE)
Arguments
centers.data |
an object of class |
x |
an object of class |
... |
Arguments to be passed to methods |
sample |
'all' to show all samples, or a numeric vector indicating the row number of samples in the sample table. |
color.blind |
logical. If TRUE colors optimized for colorblind readers are used. |
Value
An object of class cmeans_clus
containing a sublist for
each sample. Each sublist has the following components:
quality |
quality threshold used in |
reference |
reference ID. |
centers |
a data frame with the centroids coordinates. |
data |
a data frame with the fluorescence intensities and clusters name. |
membership |
a matrix with the membership values of the data elements
to the clusters. See also |
Examples
library(dPCP)
#Find path of sample table and location of reference and input files
sampleTable <- system.file("extdata", "Template_sampleTable.csv",
package = "dPCP")
fileLoc <- system.file("extdata",package = "dPCP")
#Read sample table file
sample.table <- read_sampleTable(sampleTable, system = "bio-rad",
file.location = fileLoc)
#Read reference files
ref <- read_reference(sample.table, system = "bio-rad",
file.location = fileLoc)
#Read samples files
samp <- read_sample(sample.table, system = "bio-rad",
file.location = fileLoc)
#Reference DBSCAN clustering
dbref <- reference_dbscan(ref, sample.table, save.template = FALSE)
#Predict position of clusters centroid from reference DBSCAN results
cent <- centers_data(samp, sample.table,dbref)
#Fuzzy c-means clustering
cmclus <- cmeans_clus(cent)
plot(cmclus, sample = "all")