This is the analysis overview for Firehose run "24 October 2012".
Note: These results are offered to the community as an additional reference point, enabling a wide range of cancer biologists, clinical investigators, and genome and computational scientists to easily incorporate TCGA into the backdrop of ongoing research. While every effort is made to ensure that Firehose input data and algorithms are of the highest possible quality, these analyses have not been reviewed by domain experts.
-
Sequence and Copy Number Analyses
-
Copy number analysis (GISTIC2)
View Report | There were 102 tumor samples used in this analysis: 23 significant arm-level results, 24 significant focal amplifications, and 27 significant focal deletions were found. -
Mutation Analysis (MutSig v2.0)
View Report | -
Mutation Analysis (MutSig vS2N)
View Report | -
Clustering Analyses
-
Clustering of copy number data: consensus NMF
View Report | The most robust consensus NMF clustering of 102 samples using the 51 copy number focal regions 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 Methylation: consensus NMF
View Report | The 9129 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 122 samples and 9129 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 miRseq expression: consensus NMF
View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 122 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. -
Clustering of miRseq expression: consensus hierarchical
View Report | We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 122 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 Analyses
-
Correlation between copy number variations of arm-level result and selected clinical features
View Report | Testing the association between copy number variation 35 arm-level results and 4 clinical features across 26 patients, no significant finding 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 46 peak regions and 4 clinical features across 26 patients, no significant finding detected with Q value < 0.25. -
Correlation between molecular cancer subtypes and selected clinical features
View Report | Testing the association between subtypes identified by 4 different clustering approaches and 4 clinical features across 32 patients, one significant finding detected with P value < 0.05. -
Correlation between miRseq expression and clinical features
View Report | Testing the association between 564 genes and 4 clinical features across 32 samples, statistically thresholded by Q value < 0.05, no clinical feature related to at least one genes.
-
Run Prefix = analyses__2012_10_24
-
Summary Report Date = Fri Nov 16 15:56:02 2012
-
Protection = FALSE