timeclust: time couse data clustering

Description Usage Arguments Details Value Author(s) See Also

View source: R/timeclust.R

Description

This function performs clustering analysis of time course data.

Usage

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timeclust(
  x,
  algo,
  k,
  dist = "euclidean",
  centers = NULL,
  standardize = TRUE,
  ...
)

Arguments

x

a TCA object returned from timecourseTable or a matrix

algo

character string giving a clustering method. Options are km' (kmeans), 'pam' (partitioning around medoids), 'hc' (hierachical clustering), 'cm' (cmeans).

k

numeric value between 1 and n - 1 ( n is the number of data points to be clustered ).

dist

character string specifying method for distance(dissimilarity) calculation. It should be one of 'correlation' or one of the distance measure method in dist function (for example 'euclidean', 'manhattan')

centers

a numeric matrix giving intial centers for kmeams, pam or cmeans. If given, Number of rows of centers must be equal to k.

standardize

logical, if TRUE, z-score transformation will performed on the data before clustering. See 'Details' below.

...

additional arguments passing to kmeans, pam, hclust, cmeans

Details

two types of clustering methods are provided: hard clustering (kmeans, pam, hclust) and soft clustering(cmeans). In Hard clustering, a data point can only be allocated to exactly one cluster (for hclust, cutree is used to cut a tree into clusters), while in soft clustering (also known as fuzzy clustering), a data point can be assigned to multiple clusters, membership values are used to indicate to what degree a data point belongs to each cluster. For more details, see the help() page of each function.

To avoid the influence of expression level to the clustering analysis, z-score transformation can be applied to covert the expression values to z-scores by performing the following formula:

z = \frac{x - μ}{σ}

x is value to be converted (e.g., a expression value of a genomic feature in one condition), μ is the population mean (e.g., average expression value of a genomic feature in different conditions), σ is the standard deviation (e.g., standard deviation of expression of a genomic feature in different conditions).

Value

If x is a TCA object, a TCA object will be returned. If x is a matrix, a clust object will be returned

Author(s)

Mengjun Wu

See Also

clust, kmeans, pam, hclust, cutree


TCseq documentation built on Nov. 8, 2020, 5:46 p.m.