Description Usage Arguments Details Value See Also Examples
Clustering each index, that was predifined by
PreparingTheIndexes
, with kmeans
.
1  ClustIndexes(x, scaling = TRUE)

x 
list of expression data and their indexes after running

scaling 
Boolean parameter, does the data should be standardized before clustered. Default = TRUE 
The clustering is done with Kmeans. To choose an optimal k for Kmeans clustering, the Elbow method was applied, this method looks at the percentage of variance explained as a function of the number of clusters: the chosen number of clusters should be such that adding another cluster does not give much better modeling of the data. First, the ratio of the withincluster sum of squares (WSS) to the total sum of squares (TSS) is computed for different values of k (i.e., 1, 2, 3 ...). The WSS, also known as sum of squared error (SSE), decreases as k gets larger. The Elbow method chooses the k at which the SSE decreases abruptly. This happens when the computed value of the WSStoTSS ratio first drops from 0.2.
Running kmeans
and calculating the optimal k for each one of
the indexes in the data could take a long time. To shorten the procedure the
user can skip this step altogether and directly view a specific index and
its clusters by running either the PlotIndexesClust
or the
ctsGEShinyApp
function.
By default data is standardize before clustering,for clustering
the raw counts set the scaling
parameter to FALSE.
list object is returned as output, with the relative culstered indexes table in object$ClusteredIdxTable, and the number of clusters for each index in object$optimalK
1 2 3 4 5 6 7 8 9 10  data_dir < system.file("extdata", package = "ctsGE")
files < dir(path=data_dir,pattern = "\\.xls$")
rts < readTSGE(files, path = data_dir,
labels = c("0h","6h","12h","24h","48h","72h"), skip = 10625 )
prts < PreparingTheIndexes(rts)
tsCI < ClustIndexes(prts)
head(tsCI$ClusteredIdxTable) #the table with the clusterd indexes
head(tsCI$optimalK) #the table with the number of clusters for each index

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