Description Usage Arguments Details Value Author(s) See Also
This function performs clustering analysis of time course data.
1 2 3 4 5 6 7 8 9 |
x |
a |
algo |
character string giving a clustering method. Options
are |
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
' |
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 |
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).
If x is a TCA
object, a TCA
object will be returned.
If x is a matrix, a clust
object will be returned
Mengjun Wu
clust
, kmeans
,
pam
, hclust
, cutree
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