Clustering each index, that was predifined by
list of expression data and their indexes after running
Boolean parameter, does the data should be standardized before clustered. Default = TRUE
The clustering is done with K-means. To choose an optimal k for K-means 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 within-cluster 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 WSS-to-TSS ratio first drops from 0.2.
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
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
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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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