Analysis Overview for Prostate Adenocarcinoma
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
Overview
Introduction

This is the analysis overview for Firehose run "13 September 2012".

Summary

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.

Results

- Sequence and Copy Number Analyses

- Copy number analysis (GISTIC2)
  View Report | There were 152 tumor samples used in this analysis: 18 significant arm-level results, 15 significant focal amplifications, and 29 significant focal deletions were found.

- Mutation Analysis (MutSig)
  View Report |

- Mutation Analysis (MutSig)
  View Report |

- Clustering Analyses

- Clustering of copy number data: consensus NMF
  View Report | The most robust consensus NMF clustering of 152 samples using the 44 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 3143 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 153 samples and 3143 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 mRNAseq gene expression: consensus NMF
  View Report | The most robust consensus NMF clustering of 53 samples using the 1500 most variable genes was identified for k = 9 clusters. We computed the clustering for k = 2 to k = 8 and used the cophenetic correlation coefficient to determine the best solution.

- Clustering of mRNAseq gene expression: consensus hierarchical
  View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 53 samples and 1500 genes 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 miRseq expression: consensus NMF
  View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 81 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 miRseq expression: consensus hierarchical
  View Report | We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 81 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 20 arm-level results and 4 clinical features across 123 patients, 2 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 43 peak regions and 4 clinical features across 123 patients, one significant finding detected with Q value < 0.25.

- Correlation between gene methylation status and clinical features
  View Report | Testing the association between 17263 genes and 3 clinical features across 50 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

- Correlation between molecular cancer subtypes and selected clinical features
  View Report | Testing the association between subtypes identified by 6 different clustering approaches and 4 clinical features across 124 patients, 2 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 46 genes and 3 clinical features across 83 patients, no significant finding detected with Q value < 0.25.

- Correlation between mRNAseq expression and clinical features
  View Report | Testing the association between 18155 genes and 3 clinical features across 53 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.

- Correlation between miRseq expression and clinical features
  View Report | Testing the association between 474 genes and 3 clinical features across 81 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

- Correlations between copy number and mRNAseq expression
  View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 714, 1715.2, 2727.8, 3313.4, 3895, 4492, 5150, 5917.8, 6846, respectively.

- Correlation between mRNA expression and DNA methylation
  View Report | The top 25 correlated methylation probes per gene are displayed. Total number of matched samples = 53. Number of gene expression samples = 53. Number of methylation samples = 53.

Methods & Data
Input

- Run Prefix = analyses__2012_09_13

- Summary Report Date = Mon Oct 29 23:10:32 2012

- Protection = FALSE

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