Copy number analysis (GISTIC2)
View Report | There were 563 tumor samples used in this analysis: 22 significant arm-level results, 23 significant focal amplifications, and 51 significant focal deletions were found.

Mutation Analysis (MutSig)
View Report | MAF used for this analysis: GBM.final_analysis_set.maf

Clustering of Methylation: consensus NMF
View Report | The 1139 most variable methylated genes were selected based on variation. The variation cutoff are set for each tumor type empirically by fitting a bimodal distriution. For genes with multiple methylation probes, we chose the most variable one to represent the gene. Consensus NMF clustering of 287 samples and 1139 genes 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.

Clustering of RPPA data: consensus NMF
View Report | The most robust consensus NMF clustering of 214 samples using the 150 most variable proteins was identified for k = 4 clusters. We computed the clustering for k = 2 to k = 8 and used the cophenetic correlation coefficient to determine the best solution.

Clustering of RPPA data: consensus hierarchical
View Report | The 150 most variable proteins were selected. Consensus average linkage hierarchical clustering of 214 samples and 150 proteins 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.

Clustering of mRNA expression: consensus NMF
View Report | The most robust consensus NMF clustering of 529 samples using the 1500 most variable genes 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.

Clustering of mRNA expression: consensus hierarchical
View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 529 samples and 1500 genes 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.

Clustering of miR expression: consensus NMF
View Report | 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.

Clustering of miR expression: consensus hierarchical
View Report | We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 491 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.

Correlation between copy number variations of arm-level result and selected clinical features
View Report | Testing the association between copy number variation 79 arm-level results and 6 clinical features across 544 patients, 10 significant findings detected with Q value < 0.25.

Correlation between copy number variation genes and selected clinical features
View Report | Testing the association between copy number variation of 74 peak regions and 6 clinical features across 544 patients, 15 significant findings detected with Q value < 0.25.

Correlation between molecular cancer subtypes and selected clinical features
View Report | Testing the association between subtypes identified by 5 different clustering approaches and 6 clinical features across 527 patients, 7 significant findings detected with P value < 0.05.

Correlation between gene mutation status and selected clinical features
View Report | Testing the association between mutation status of 338 genes and 6 clinical features across 269 patients, 39 significant findings detected with Q value < 0.25.

Correlation between mRNA expression and clinical features
View Report | Testing the association between 12042 genes and 6 clinical features across 519 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

Correlation between miR expression and clinical features
View Report | Testing the association between 534 miRs and 6 clinical features across 482 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one miRs.

Correlations between copy number and mRNA expression
View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are -0.02419, 0.0155, 0.05483, 0.1036, 0.1552, 0.20396, 0.25117, 0.30294, 0.37249, respectively.

Correlations between copy number and miR expression
View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are -0.0553, -0.0142, 0.00515, 0.02166, 0.0401, 0.07766, 0.13265, 0.2091, 0.2699, respectively.

Correlation between mRNA expression and DNA methylation
View Report | The top 25 correlated methylation probe(s) per gene are displayed. Total number of matched samples = 279 Number of gene expression samples = 289 Number of methylation samples = 529

Preprocessing of clinical data
View Report | Clinical data for tier 1 clinical variables are generated.

Association of mutation, copy number alteration, and subtype markers with pathways
View Report | There are 222 genes with significant mutation (Q value <= 0.1) and 159 genes with significant copy number alteration (Q value <= 0.25). The identified marker genes (Q value <= 0.01 or within top 2000) are 2000 for subtype 1, 2000 for subtype 2, 2000 for subtype 3. Pathways significantly enriched with these genes (Q value <= 0.01) are identified :

PARADIGM pathway analysis of mRNA expression data
View Report | There were 69 significant pathways identified in this analysis.

PARADIGM pathway analysis of mRNA expression and copy number data
View Report | There were 99 significant pathways identified in this analysis.

Identification of putative miR direct targets
View Report | This pipeline use a relevance network approach to infer putative miR:mRNA regulatory connections. All miR:mRNA pairs that have correlations < -0.3 and have predicted interactions in three sequence prediction databases (Miranda, Pictar, Targetscan) define the final network.