Analysis Overview
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C198852M
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Analysis Overview for Kidney Renal Clear Cell Carcinoma (Primary solid tumor cohort) - 23 May 2013. Broad Institute of MIT and Harvard. doi:10.7908/C198852M
Overview
Introduction

This is an overview of Kidney Renal Clear Cell Carcinoma analysis pipelines from Firehose run "23 May 2013".

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 504 tumor samples used in this analysis: 29 significant arm-level results, 13 significant focal amplifications, and 18 significant focal deletions were found.

    • Mutation Analysis (MutSig v1.5)
      View Report | 

    • Mutation Analysis (MutSig v2.0 and MutSigCV v0.9 merged result)
      View Report | 

    • Mutation Analysis (MutSig v2.0)
      View Report | 

    • Mutation Analysis (MutSigCV v0.9)
      View Report | 

  • Correlations to Clinical Parameters

    • Correlation between aggregated molecular cancer subtypes and selected clinical features
      View Report | Testing the association between subtypes identified by 12 different clustering approaches and 8 clinical features across 502 patients, 33 significant findings detected with P value < 0.05 and Q value < 0.25.

    • Correlation between copy number variation genes (focal) and selected clinical features
      View Report | Testing the association between copy number variation 32 arm-level results and 8 clinical features across 493 patients, 19 significant findings detected with Q value < 0.25.

    • Correlation between copy number variations of arm-level result and selected clinical features
      View Report | Testing the association between copy number variation 71 arm-level results and 8 clinical features across 493 patients, 4 significant findings detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 20182 genes and 7 clinical features across 283 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

    • Correlation between gene mutation status and selected clinical features
      View Report | Testing the association between mutation status of 10 genes and 8 clinical features across 293 patients, 5 significant findings detected with Q value < 0.25.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 455 genes and 7 clinical features across 481 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

    • Correlation between mRNA expression and clinical features
      View Report | Testing the association between 17814 genes and 6 clinical features across 72 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

    • Correlation between mRNAseq expression and clinical features
      View Report | Testing the association between 18295 genes and 7 clinical features across 480 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

    • Correlation between RPPA expression and clinical features
      View Report | Testing the association between 166 genes and 7 clinical features across 454 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • Clustering Analyses

    • Clustering of copy number data by focal peak region with log2 ratio: consensus NMF
      View Report | The most robust consensus NMF clustering of 504 samples using the 32 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 2093 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 294 samples and 2093 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 mature expression: consensus hierarchical
      View Report | We filtered the data to 145 most variable miRs. Consensus average linkage hierarchical clustering of 217 samples and 145 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 mature expression: consensus NMF
      View Report | We filtered the data to 145 most variable miRs. Consensus NMF clustering of 217 samples and 145 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 precursor expression: consensus hierarchical
      View Report | We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 481 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 precursor expression: consensus NMF
      View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 481 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 mRNA expression: consensus hierarchical
      View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 72 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 mRNA expression: consensus NMF
      View Report | The most robust consensus NMF clustering of 72 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 480 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 480 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 RPPA data: consensus hierarchical
      View Report | 166 proteins were selected. Consensus average linkage hierarchical clustering of 454 samples and 166 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 RPPA data: consensus NMF
      View Report | The most robust consensus NMF clustering of 454 samples using 166 proteins was identified for k = 6 clusters. We computed the clustering for k = 2 to k = 8 and used the cophenetic correlation coefficient to determine the best solution.

  • Pathway Analyses

    • Association of mutation, copy number alteration, and subtype markers with pathways
      View Report | There are 9 genes with significant mutation (Q value <= 0.1) and 503 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 :

    • HotNet pathway analysis of mutation and copy number data
      View Report | There were 29 significant subnetworks identified in HotNet analysis.

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

    • PARADIGM pathway analysis of mRNA expression data
      View Report | There were 72 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression and copy number data
      View Report | There were 41 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression data
      View Report | There were 46 significant pathways identified in this analysis.

  • Other Correlation Analyses

    • Correlation between copy number variation genes and molecular subtypes
      View Report | Testing the association between copy number variation of 32 peak regions and 12 molecular subtypes across 504 patients, 133 significant findings detected with Q value < 0.25.

    • Correlation between copy number variations of arm-level result and molecular subtypes
      View Report | Testing the association between copy number variation 71 arm-level results and 12 molecular subtypes across 504 patients, 137 significant findings detected with Q value < 0.25.

    • Correlation between gene mutation status and molecular subtypes
      View Report | Testing the association between mutation status of 10 genes and 12 molecular subtypes across 293 patients, 9 significant findings detected with P value < 0.05 and Q value < 0.25.

    • Correlation between mRNA expression and DNA methylation
      View Report | The top 25 correlated methylation probes per gene are displayed. Total number of matched samples = 272. Number of gene expression samples = 480. Number of methylation samples = 294.

    • Correlations between copy number and mRNA expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are -0.1779, -0.06568, 0.01223, 0.08028, 0.143, 0.2099, 0.2794, 0.3657, 0.48009, 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 735, 1595, 2036.4, 2442, 2880, 3329, 3872, 4453.4, 5199, respectively.

Methods & Data
Input

  • Summary Report Date = Wed Jun 26 16:56:13 2013

  • Protection = FALSE

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