Glioblastoma Multiforme: Clustering of miR expression: consensus NMF
Maintained by Hailei Zhang (Broad Institute)
Overview
Introduction

This pipeline calculates clusters based on a consensus non-negative matrix factorization (NMF) clustering method [1][2]. This pipeline has the following features:

1. Convert input data set to non-negativity matrix by column rank normalization.

2. Classify samples into consensus clusters.

3. Determine differentially expressed marker miRs for each subtype.

Summary

We filtered the data to 150 most variable miRs. Consensus NMF clustering of 491 samples and 150 miRs identified 4 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
Silhouette width of each sample in robust cluster

Figure 1. 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 blue 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. The miR expression heatmap with a standard hierarchical clustering for 491 samples and 150 most variable miRs.

Consensus and correlation matrix

Figure 3. The consensus matrix after clustering shows 4 robust clusters with limited overlap between clusters.

Figure 4. The correlation matrix also shows 4 robust clusters.

Samples assignment with silhouette width

Table 1. Get Full Table List of samples with 4 subtypes and silhouette width.

SampleName | cluster | silhouetteValue
--- | --- | ---
TCGA-02-0001-01C-01T-0179-07 | 1 | 0.087
TCGA-02-0004-01A-01T-0301-07 | 1 | 0.18
TCGA-02-0006-01B-01T-0179-07 | 1 | -0.028
TCGA-02-0009-01A-01T-0179-07 | 1 | 0.078
TCGA-02-0015-01A-01T-0301-07 | 1 | -0.0071
TCGA-02-0023-01B-01T-0301-07 | 1 | -0.15
TCGA-02-0027-01A-01T-0179-07 | 1 | 0.078
TCGA-02-0033-01A-01T-0179-07 | 1 | 0.07
TCGA-02-0037-01A-01T-0179-07 | 1 | 0.087
TCGA-02-0038-01A-01T-0179-07 | 1 | 0.027

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

SampleName | K=2 | K=3 | K=4 | K=5 | K=6 | K=7 | K=8
--- | --- | --- | --- | --- | --- | --- | ---
TCGA-02-0001-01C-01T-0179-07 | 1 | 1 | 1 | 1 | 1 | 1 | 1
TCGA-02-0003-01A-01T-0179-07 | 1 | 2 | 2 | 2 | 2 | 2 | 2
TCGA-02-0004-01A-01T-0301-07 | 1 | 1 | 1 | 1 | 3 | 3 | 3
TCGA-02-0007-01A-01T-0179-07 | 1 | 2 | 2 | 1 | 1 | 1 | 4
TCGA-02-0009-01A-01T-0179-07 | 1 | 1 | 1 | 2 | 1 | 1 | 4
TCGA-02-0010-01A-01T-0179-07 | 1 | 2 | 2 | 2 | 2 | 2 | 2
TCGA-02-0011-01B-01T-0179-07 | 1 | 2 | 3 | 2 | 2 | 2 | 4
TCGA-02-0014-01A-01T-0179-07 | 1 | 2 | 2 | 2 | 2 | 2 | 2
TCGA-02-0016-01A-01T-0301-07 | 1 | 2 | 2 | 2 | 1 | 1 | 4
TCGA-02-0026-01B-01T-0301-07 | 1 | 2 | 2 | 2 | 2 | 2 | 2

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-222 | 9.3e-22 | 1.2 | 1.2e-19 | 1
HSA-MIR-204 | 7.8e-12 | 1.1 | 1.1e-10 | 1
HSA-MIR-34A | 1.4e-20 | 1 | 1.3e-18 | 1
HSA-MIR-221 | 8.8e-22 | 1 | 1.2e-19 | 1
HSA-MIR-21 | 2.8e-19 | 0.94 | 1.9e-17 | 1
HSA-MIR-146B | 1.4e-24 | 0.93 | 7.7e-22 | 1
HSA-MIR-223 | 1.3e-15 | 0.87 | 3.6e-14 | 1
HSA-MIR-142-3P | 5.7e-18 | 0.86 | 2.5e-16 | 1
HSA-MIR-148A | 5.2e-13 | 0.85 | 9.5e-12 | 1
HSA-MIR-34B | 7e-19 | 0.83 | 4.2e-17 | 1

Methods & Data
Input

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

- miR expression file = /xchip/cga/gdac-prod/tcga-gdac-genepattern/jobResults/222516/outputprefix.expclu.gct

CNMF Method

Non-negative matrix factorization (NMF) is an unsupervised learning algorithm that has been shown to identify molecular patterns when applied to miR expression data [1],[2]. Rather than separating miR clusters based on distance computation, NMF detects contextdependent patterns of miR expression in complex biological systems.

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. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] Brunet, J.P., Tamayo, P., Golub, T.R. & Mesirov, J.P., Metagenes and molecular pattern discovery using matrix factorization, Proc Natl Acad Sci U S A 12(101):4164-9 (2004)
[2] Broad Genepattern: NMFConsensus
[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
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