Analysis Overview for Ovarian Serous Cystadenocarcinoma
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Overview
Introduction

This is the analysis overview for Firehose run "21 December 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 559 tumor samples used in this analysis: 31 significant arm-level results, 31 significant focal amplifications, and 39 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 559 samples using the 70 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 2363 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 582 samples and 2363 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 412 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.

    • Clustering of RPPA data: consensus hierarchical
      View Report | The 150 most variable proteins were selected. Consensus average linkage hierarchical clustering of 412 samples and 150 proteins 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 mRNA expression: consensus NMF
      View Report | The most robust consensus NMF clustering of 569 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 569 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 mRNAseq gene expression: consensus NMF
      View Report | The most robust consensus NMF clustering of 262 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 mRNAseq gene expression: consensus hierarchical
      View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 262 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 568 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 miR expression: consensus hierarchical
      View Report | We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 568 samples and 150 miRs identified 6 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 454 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 454 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 80 arm-level results and 6 clinical features across 552 patients, 8 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 70 peak regions and 6 clinical features across 552 patients, 12 significant findings detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 13026 genes and 4 clinical features across 262 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.

    • Correlation between molecular cancer subtypes and selected clinical features
      View Report | Testing the association between subtypes identified by 12 different clustering approaches and 6 clinical features across 578 patients, 13 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 9 genes and 4 clinical features across 316 patients, no significant finding detected with Q value < 0.25.

    • Correlation between RPPA expression and clinical features
      View Report | Testing the association between 165 genes and 5 clinical features across 407 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.

    • Correlation between mRNA expression and clinical features
      View Report | Testing the association between 18632 genes and 6 clinical features across 562 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

    • Correlation between mRNAseq expression and clinical features
      View Report | Testing the association between 18559 genes and 4 clinical features across 262 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.

    • Correlation between miR expression and clinical features
      View Report | Testing the association between 817 miRs and 6 clinical features across 560 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one miRs.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 415 genes and 6 clinical features across 454 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

    • Correlations between copy number and mRNA expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 0.00666, 0.0598, 0.1172, 0.19744, 0.2867, 0.37426, 0.4453, 0.51328, 0.5838, respectively.

    • Correlations between copy number and mRNAseq expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 1087.4, 1797, 2427, 3136.6, 3915, 4708, 5472.8, 6145.2, 6816, 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 -0.03696, -0.01514, -5e-04, 0.0203, 0.0452, 0.09412, 0.1859, 0.27658, 0.37064, 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 = 262. Number of gene expression samples = 262. Number of methylation samples = 262.

  • Other Analyses

    • PARADIGM pathway analysis of mRNA expression data
      View Report | There were 62 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNA expression and copy number data
      View Report | There were 76 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression data
      View Report | There were 38 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression and copy number data
      View Report | There were 30 significant pathways identified in this analysis.

    • The boxplot mRNA array based expression data
      View Report

    • 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.

Methods & Data
Input

  • Run Prefix = analyses__2012_12_21

  • Summary Report Date = Fri Jan 25 11:58:20 2013

  • Protection = FALSE

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