This pipeline calculates clusters based on a consensus non-negative matrix factorization (NMF) clustering method ##REF##1,##REF##3. This pipeline has the following features:
Convert input data set to a non-negitive matrix by column rank normalization.
Classify samples into consensus clusters.
Determine differentially expressed focal events for each subtype.
The most robust consensus NMF clustering of 492 samples using the 98 copy number focal regions was identified for k = 5 clusters. We computed the clustering for k = 2 to k = 8 and used the cophenetic correlation coefficient to determine the best solution.
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 focal events for each subtype by comparing the subclass versus the other subclasses, using Student's t-test.
|DEL PEAK 42(19P13.3)||3.2e-30||0.43||3.1e-28||1|
|DEL PEAK 49(22Q13.32)||3.9e-20||0.38||4.8e-19||1|
|DEL PEAK 48(22Q13.31)||6.2e-21||0.38||8.6e-20||1|
|DEL PEAK 34(16Q21)||2e-22||0.37||5e-21||1|
|DEL PEAK 35(16Q23.1)||1.7e-22||0.36||5e-21||1|
|DEL PEAK 37(17P12)||7.7e-19||0.36||8.4e-18||1|
|DEL PEAK 12(4Q34.3)||2.2e-18||0.33||2.2e-17||1|
|DEL PEAK 31(15Q15.1)||3.2e-23||0.32||1.6e-21||1|
|DEL PEAK 21(9Q34.11)||4.3e-16||0.3||3.2e-15||1|
|DEL PEAK 38(17P11.2)||2e-08||0.26||7.4e-08||1|
Copy number data file = All Lesions File actual copy number part (all_lesions.conf_##.txt, where ## is the confidence level). The all lesions file is from GISTIC pipeline and summarizes the results from the GISTIC run. It contains data about the significant regions of amplification and deletion as well as which samples are amplified or deleted in each of these regions. The identified regions are listed down the first column, and the samples are listed across the first row, starting in column 10
Non-negative matrix factorization (NMF) is an unsupervised learning algorithm that has been shown to identify molecular patterns when applied to gene expression data ##REF##1,##REF##3. Rather than separating gene clusters based on distance computation, NMF detects contextdependent patterns of gene expression in complex biological systems.
We use the cophenetic correlation coefficient ##REF##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 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 ##REF##2.