Breast Invasive Carcinoma: Clustering of mRNAseq gene expression: consensus hierarchical
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
Overview
Introduction

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 genes for each subtype.

Summary

The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 777 samples and 1500 genes 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.

Results
Gene expression patterns of molecular subtypes

Figure 1. Get High-res Image Samples were separated into 3 clusters. Shown are 777 samples and 900 marker genes. The color bar of the row indicates the marker genes for the corresponding cluster.

Figure 2. Get High-res Image Heatmap with a standard hierarchical clustering for 777 samples and the 1500 most variable genes.

Consensus and correlation matrix

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

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

Silhouette widths

Figure 5. Get High-res Image The silhouette width was calculated for each sample and each value of k. The left panel shows the average silhouette width across all samples for each tested k (left panel). The right panels shows assignments of clusters to samples and the silhouette width of each sample for the most robust clustering.

Samples assignment with silhouette width

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

SampleName | cluster | silhouetteValue
--- | --- | ---
TCGA-A1-A0SD-01 | 1 | 0.1
TCGA-A1-A0SE-01 | 1 | 0.14
TCGA-A1-A0SF-01 | 1 | 0.12
TCGA-A1-A0SG-01 | 1 | 0.0076
TCGA-A2-A04N-01 | 1 | -0.012
TCGA-A2-A0CP-01 | 1 | 0.14
TCGA-A2-A0CT-01 | 1 | -0.068
TCGA-A2-A0CV-01 | 1 | 0.11
TCGA-A2-A0CZ-01 | 1 | 0.088
TCGA-A2-A0D3-01 | 1 | 0.011

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-A1-A0SB-01 | 1 | 2 | 3 | 2 | 3 | 3 | 3
TCGA-A1-A0SI-01 | 1 | 2 | 3 | 2 | 3 | 3 | 3
TCGA-A1-A0SK-01 | 1 | 2 | 3 | 2 | 3 | 3 | 3
TCGA-A1-A0SO-01 | 1 | 2 | 3 | 2 | 3 | 3 | 3
TCGA-A1-A0SP-01 | 1 | 2 | 3 | 2 | 3 | 3 | 3
TCGA-A2-A04P-01 | 1 | 2 | 3 | 2 | 3 | 3 | 3
TCGA-A2-A04Q-01 | 1 | 2 | 3 | 2 | 3 | 3 | 3
TCGA-A2-A04T-01 | 1 | 2 | 3 | 2 | 3 | 3 | 3
TCGA-A2-A04U-01 | 1 | 2 | 3 | 2 | 3 | 3 | 3
TCGA-A2-A0CM-01 | 1 | 2 | 3 | 2 | 3 | 3 | 3

Marker genes 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 genes for each subtype by comparing the subclass versus the other subclasses, using Student's t-test.

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

Composite.Element.REF | p | difference | q | subclass
--- | --- | --- | --- | ---
ADH1B|125 | 1.6e-76 | 4.8 | 2.5e-74 | 1
FABP4|2167 | 2.3e-54 | 3.7 | 5.9e-53 | 1
TMEM132C|92293 | 1.4e-81 | 3.7 | 3.5e-79 | 1
TUSC5|286753 | 3.6e-41 | 3.6 | 3.3e-40 | 1
C7|730 | 1.2e-97 | 3.6 | 2.2e-94 | 1
PI16|221476 | 7.8e-62 | 3.5 | 3.5e-60 | 1
PLIN1|5346 | 2.3e-55 | 3.5 | 6.2e-54 | 1
DARC|2532 | 2.6e-98 | 3.4 | 9.2e-95 | 1
CIDEA|1149 | 3.1e-45 | 3.4 | 4e-44 | 1
ANKRD30A|91074 | 2.6e-33 | 3.4 | 1.3e-32 | 1

Methods & Data
Input

mRNAseq of RPKM value was made TMM normalization[5] and then log2 transformed as the input RNAseq data for the clustering

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

This is an experimental feature. Location of data archives could not be determined.

References
[1] Bmonti, 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:91-118 (2003)
[2] Broad Genepattern: ConsensusClustering
[3] Rousseeuw, P.J., Silhouettes: A graphical aid to the interpretation and validation of cluster analysis., J. Comput. Appl. Math. 20:53-65 (1987)
[4] R silhouette package
[5] R edgeR package
Meta

- Maintainer = TCGA GDAC Team

Copyright © 2011 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
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