Clustering of miR expression: consensus hierarchical
PANCANCER cohort with 12 disease types (Primary solid tumor)
23 September 2013  |  analyses__2013_09_23
Maintainer Information
Citation Information
Maintained by Hailei Zhang (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Clustering of miR expression: consensus hierarchical. Broad Institute of MIT and Harvard. doi:10.7908/C1MP51NC

This pipeline calculates clusters based on consensus hierarchical clustering with agglomerative average linkage[1],[2]. This pipeline has the following features:

  1. Classify samples into consensus clusters.

  2. Determine differentially expressed marker miRs for each subtype.


We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 569 samples and 150 miRs identified 3 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.

Silhouette width of each sample in robust cluster

Figure 1.  Get High-res Image Silhouette width was calculated and the average silhouette width for all samples within one cluster was shown below according to different clusters (left panel). The robust cluster was pointed out by the red symbol (left panel) and the silhouette width of each sample in robust cluster was shown on right panel.

miR expression patterns of molecular subtype

Figure 2.  Get High-res Image The miR expression heatmap with a standard hierarchical clustering for 569 samples and 150 most variable miRs.

Consensus and correlation matrix

Figure 3.  Get High-res Image The consensus matrix after clustering shows 3 robust clusters with limited overlap between clusters.

Figure 4.  Get High-res Image The correlation matrix also shows 3 robust clusters.

Samples assignment with silhouette width

Table 1.  Get Full Table List of samples with 3 subtypes and silhouette width.

SampleName cluster silhouetteValue
TCGA-04-1347-01A-01T-0438-07 1 0.07
TCGA-04-1350-01A-01T-0455-07 1 0.092
TCGA-04-1360-01A-01T-0455-07 1 0.18
TCGA-04-1364-01A-01T-0455-07 1 0.23
TCGA-04-1371-01A-01T-0455-07 1 0.17
TCGA-04-1514-01A-01T-0505-07 1 0.2
TCGA-04-1519-01A-01T-0541-07 1 0.081
TCGA-04-1525-01A-01T-0541-07 1 0.14
TCGA-04-1646-01A-01T-0585-07 1 0.084
TCGA-04-1655-01A-01T-0567-07 1 0.15

Table 2.  Get Full Table List of samples belonging to each cluster in different k clusters.

TCGA_ID K.2 K.3 K.4 K.5 K.6 K.7 K.8
TCGA-04-1332-01A-01T-0438-07 1 3 1 4 4 4 5
TCGA-04-1348-01A-01T-0455-07 1 3 1 1 1 3 8
TCGA-04-1353-01A-01T-1051-07 1 3 1 1 1 3 8
TCGA-04-1361-01A-01T-0455-07 1 3 1 1 1 3 8
TCGA-04-1362-01A-01T-0455-07 1 3 1 1 1 3 8
TCGA-04-1516-01A-01T-1051-07 1 3 1 1 1 3 8
TCGA-04-1530-01A-02T-0505-07 1 3 1 1 1 3 8
TCGA-04-1542-01A-01T-0505-07 1 3 1 1 1 3 8
TCGA-04-1644-01B-01T-1051-07 1 3 1 1 1 3 8
TCGA-04-1648-01A-01T-0585-07 1 3 1 4 1 3 8
Marker miRs of each subtype

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.

Table 3.  Get Full Table List of marker miRs with p<= 0.05 (The positive value of column difference means miR is upregulated in this subtype and vice versa).

Composite.Element.REF p difference q subclass
HSA-MIR-449A 1.2e-12 1.9 9.6e-12 1
HSA-MIR-514 3.1e-21 1.6 9e-20 1
HSA-MIR-509-3P 2.1e-17 1.1 3.1e-16 1
HSA-MIR-509-3-5P 3e-15 1.1 3.5e-14 1
HSA-MIR-96 2.5e-15 1.1 3e-14 1
HSA-MIR-20A 8.7e-26 0.98 6e-24 1
HSA-MIR-34C-5P 4.3e-11 0.97 3e-10 1
HSA-MIR-509-5P 4.7e-17 0.96 6.7e-16 1
HSA-MIR-513C 4.8e-13 0.96 4e-12 1
HSA-MIR-508-3P 5.3e-19 0.9 1.1e-17 1
Methods & Data

miR Array Platforms Agilent H-miR_8x15K and H-miR_8x15Kv2.

  • miR expression file = /xchip/cga/gdac-prod/tcga-gdac-genepattern/jobResults/475311/outputprefix.expclu.gct

Consensus Hierarchical 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 [1],[2].

Silhouette Width

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 [3].

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

[1] Monti, S., Tamayo, P., Mesirov, J. & Golub, T.R., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52:91-118 (2003)
[3] Rousseeuw, P.J., Silhouettes: A graphical aid to the interpretation and validation of cluster analysis., J. Comput. Appl. Math. 20:53-65 (1987)