This pipeline calculates clusters based on a consensus nonnegative matrix factorization (NMF) clustering method , . This pipeline has the following features:

Convert input data set to a nonnegitive matrix by column rank normalization.

Classify samples into consensus clusters.

Determine differentially expressed marker proteins for each subtype.
The most robust consensus NMF clustering of 454 samples using the 150 most variable proteins was identified for k = 3 clusters. We computed the clustering for k = 2 to k = 8 and used the cophenetic correlation coefficient to determine the best solution.
SampleName  cluster  silhouetteValue 

TCGAA3330601A02173720  1  0.086 
TCGAA3330801A03173720  1  0.1 
TCGAA3331301A03173720  1  0.088 
TCGAA3331701A03173720  1  0.071 
TCGAA3331901A03173720  1  0.15 
TCGAA3333501A02173720  1  0.18 
TCGAA3333601A02173720  1  0.07 
TCGAA3334701A03173720  1  0.24 
TCGAA3336301A03173720  1  0.19 
TCGAA3337801A03173720  1  0.029 
SampleName  K=2  K=3  K=4  K=5  K=6  K=7  K=8 

TCGAA3330601A02173720  1  1  1  1  1  1  1 
TCGAA3330801A03173720  1  1  2  2  2  2  2 
TCGAA3331101A03173720  1  2  1  1  3  3  2 
TCGAA3331701A03173720  1  1  1  1  3  3  2 
TCGAA3331901A03173720  1  1  3  2  3  3  2 
TCGAA3332301A03173720  1  2  2  1  3  3  2 
TCGAA3332601A03173720  1  2  1  1  4  4  3 
TCGAA3332901A02173720  1  2  1  1  6  6  7 
TCGAA3333101A03173720  1  2  1  5  6  6  7 
TCGAA3333501A02173720  1  1  3  1  3  3  3 
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 proteins for each subtype by comparing the subclass versus the other subclasses, using Student's ttest.
ACACA.ACC1.R.C  X3.12670434734863e.23  X0.61097338753843  X1.29758230414968e.21  X1 

YBX1YB1_PS102RV  1.9e17  0.61  2.1e16  1 
CCNB1CYCLIN_B1RV  2.8e09  0.54  1.3e08  1 
DIABLOSMACMV  1.5e17  0.52  1.9e16  1 
ACACA ACACBACC_PS79RV  1.5e21  0.49  3.9e20  1 
RPS6S6RNA  8.1e19  0.46  1.5e17  1 
CDH3PCADHERINRC  2.2e15  0.42  2.2e14  1 
RPS6S6_PS235_S236RV  5.8e08  0.39  2.3e07  1 
PRKCA PKCALPHA_PS657RV  1.2e09  0.39  5.7e09  1 
PRKCA PKCALPHAMV  8.4e08  0.31  3.2e07  1 
EEF2EEF2RV  4.4e29  0.31  2.4e27  1 
The RPPA Level 3 data was as the input file for clustering, which is Protein measurements correted by using median centering across antibodies.
Nonnegative matrix factorization (NMF) is an unsupervised learning algorithm that has been shown to identify molecular patterns when applied to protein expression data , . Rather than separating protein clusters based on distance computation, NMF detects contextdependent patterns of protein expression in complex biological systems.
We use the cophenetic correlation coefficient 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 .
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.