Kidney Chromophobe: Clustering of miRseq precursor expression: consensus NMF
(primary solid tumor cohort)
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
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 66 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.

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. Tumours were separated into 3 clusters on the basis of miR expression with 66 samples and 377 marker miRs. The color bar of the row indicates the marker miRs for the corresponding cluster.

Figure 3. The miR expression heatmap with a standard hierarchical clustering for 66 samples and 150 most variable miRs.

Consensus and correlation matrix

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

Figure 5. 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-KL-8323-01 | 1 | 0.014
TCGA-KL-8326-01 | 1 | -0.041
TCGA-KL-8328-01 | 1 | -0.017
TCGA-KL-8333-01 | 1 | 0.21
TCGA-KL-8334-01 | 1 | -0.016
TCGA-KL-8335-01 | 1 | -0.032
TCGA-KL-8338-01 | 1 | 0.11
TCGA-KL-8339-01 | 1 | 0.061
TCGA-KL-8343-01 | 1 | 0.22
TCGA-KL-8344-01 | 1 | 0.26

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-KL-8323-01 | 1 | 1 | 1 | 1 | 1 | 1 | 1
TCGA-KL-8326-01 | 1 | 1 | 3 | 2 | 4 | 4 | 4
TCGA-KL-8328-01 | 1 | 1 | 3 | 2 | 2 | 2 | 2
TCGA-KL-8333-01 | 1 | 1 | 3 | 5 | 5 | 6 | 7
TCGA-KL-8334-01 | 1 | 1 | 4 | 5 | 5 | 5 | 3
TCGA-KL-8335-01 | 1 | 1 | 3 | 5 | 5 | 6 | 7
TCGA-KL-8336-01 | 1 | 3 | 1 | 1 | 1 | 1 | 1
TCGA-KL-8338-01 | 1 | 1 | 3 | 5 | 5 | 6 | 7
TCGA-KL-8339-01 | 1 | 1 | 3 | 5 | 5 | 4 | 4
TCGA-KL-8343-01 | 1 | 1 | 3 | 2 | 5 | 6 | 4

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-409 | 0.000049 | 2.5 | 0.0046 | 1
HSA-MIR-654 | 0.00018 | 2.5 | 0.0047 | 1
HSA-MIR-758 | 0.0001 | 2.5 | 0.0047 | 1
HSA-MIR-136 | 0.000018 | 2.5 | 0.0033 | 1
HSA-MIR-134 | 0.0002 | 2.5 | 0.0047 | 1
HSA-MIR-127 | 0.000028 | 2.4 | 0.0033 | 1
HSA-MIR-493 | 0.00028 | 2.4 | 0.0057 | 1
HSA-MIR-379 | 0.00022 | 2.4 | 0.0049 | 1
HSA-MIR-337 | 0.00049 | 2.4 | 0.0081 | 1
HSA-MIR-369 | 0.00018 | 2.4 | 0.0047 | 1

Methods & Data
Input

miRseq (at precursor level) of RPM value (reads per million reads aligned to miRBase precursor) with log2 transformed was as the input data for the clustering

CNMF Method

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

Cophenetic Correlation Coefficient

We use the cophenetic correlation coefficient [1] to determine the cluster that yields the most robust clustering. The cophenetic correlation coefficient is computed based on the consensus matrix of the CNMF clustering, and measures how reliably the same samples are assigned to the same cluster across many iterations of the clustering lgorithm with random initializations. The cophenetic correlation coefficient lies between 0 and 1, with higher values indicating more stable cluster assignments. We select the number of clusters k based on the largest observed correlation coefficient for all tested values of k.

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][4].

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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