This pipeline calculates clusters based on consensus hierarchical clustering with agglomerative average linkage,. This pipeline has the following features:
Classify samples into consensus clusters.
Determine differentially expressed marker miRs for each subtype.
We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 16 samples and 150 miRs identified 2 subtypes with the stability of the clustering increasing for k = 2 to k = 8 and the average silhouette width calculation for selecting the robust clusters.
Samples most representative of the clusters, hereby called core samples were identified based on positive silhouette width, indicating higher similarity to their own class than to any other class member. Core samples were used to select differentially expressed marker miRs for each subtype by comparing the subclass versus the other subclasses, using Student's t-test.
miRseq of RPKM value was made TMM normalization and then log2 transformed as the input RNAseq data for the clustering
Consensus Hierarchical clustering is a resampling-based clustering. It provides for a method to represent the consensus across multiple runs of a clustering algorithm and to assess the stability of the discovered clusters. To this end, perturbations of the original data are simulated by resampling techniques ,.
Silhouette width is defined as the ratio of average distance of each sample to samples in the same cluster to the smallest distance to samples not in the same cluster. If silhouette width is close to 1, it means that sample is well clustered. If silhouette width is close to -1, it means that sample is misclassified .
This is an experimental feature. Location of data archives could not be determined.