PANCANCER: 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 564 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
miR expression patterns of molecular subtype
Consensus and correlation matrix
Samples assignment with silhouette width

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

SampleName cluster silhouetteValue
TCGA-04-1331-01A-01T-0438-07 1 0.21
TCGA-04-1335-01A-01T-0438-07 1 0.0095
TCGA-04-1338-01A-01T-0438-07 1 0.13
TCGA-04-1341-01A-01T-0438-07 1 -0.0021
TCGA-04-1343-01A-01T-0438-07 1 0.046
TCGA-04-1348-01A-01T-0455-07 1 0.17
TCGA-04-1351-01A-01T-0455-07 1 0.14
TCGA-04-1353-01A-01T-1051-07 1 0.13
TCGA-04-1362-01A-01T-0455-07 1 0.12
TCGA-04-1516-01A-01T-1051-07 1 0.35

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-04-1331-01A-01T-0438-07 1 1 1 1 1 1 1
TCGA-04-1332-01A-01T-0438-07 1 2 2 1 1 1 2
TCGA-04-1335-01A-01T-0438-07 1 1 1 1 1 1 2
TCGA-04-1338-01A-01T-0438-07 1 1 1 1 1 1 1
TCGA-04-1341-01A-01T-0438-07 1 1 1 1 4 4 5
TCGA-04-1343-01A-01T-0438-07 1 1 1 1 2 4 3
TCGA-04-1348-01A-01T-0455-07 1 1 1 1 1 1 1
TCGA-04-1351-01A-01T-0455-07 1 1 1 1 1 1 1
TCGA-04-1353-01A-01T-1051-07 1 1 1 1 1 1 7
TCGA-04-1361-01A-01T-0455-07 1 2 2 5 6 3 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).

HSA.MIR.494 X2.40355827175185e.58 X2.04407341944764 X4.93330335277067e.56 X1
HSA-MIR-1225-5P 3.8e-60 1.7 1e-57 1
HSA-MIR-638 1.3e-48 1.6 1.3e-46 1
EBV-MIR-BART19-3P 2.8e-34 1.6 7.2e-33 1
HSA-MIR-188-5P 3.1e-50 1.5 3.7e-48 1
HSA-MIR-801 2.1e-40 1.3 1.1e-38 1
KSHV-MIR-K12-3 1.5e-47 1.3 1.3e-45 1
HSA-MIR-135A* 2.1e-36 1.2 6.4e-35 1
HSV1-MIR-LAT 2.9e-39 1.2 1.4e-37 1
HSA-MIR-630 2.8e-26 1.2 3.9e-25 1
HSA-MIR-923 1.1e-53 1.1 1.7e-51 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/137070/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].