Analysis Overview for Kidney Renal Clear Cell Carcinoma
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
Overview
Introduction

This is the analysis overview for Firehose run "25 August 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 490 tumor samples used in this analysis: 29 significant arm-level results, 11 significant focal amplifications, and 18 significant focal deletions were found.

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

  • Clustering Analyses

    • Clustering of copy number data: consensus NMF
      View Report | The most robust consensus NMF clustering of 490 samples using the 29 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 2383 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 283 samples and 2383 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 454 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 454 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 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 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 mRNAseq gene expression: consensus NMF
      View Report | The most robust consensus NMF clustering of 469 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 469 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 miRseq expression: consensus NMF
      View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 463 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 463 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 71 arm-level results and 9 clinical features across 490 patients, 3 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 29 peak regions and 9 clinical features across 490 patients, 25 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 7 different clustering approaches and 9 clinical features across 499 patients, 33 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 98 genes and 9 clinical features across 293 patients, 3 significant findings detected with Q value < 0.25.

    • Correlation between mRNA expression and clinical features
      View Report | Testing the association between 17814 genes and 7 clinical features across 72 samples, statistically thresholded by Q value < 0.05, 5 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 -0.21832, -0.0867, 0.0089, 0.0926, 0.1695, 0.2409, 0.3176, 0.40086, 0.5043, 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 786.7, 1601, 2032.1, 2428, 2845, 3305, 3811, 4367.6, 5100.3, 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 = 71 Number of gene expression samples = 221 Number of methylation samples = 72

  • Other Analyses

    • 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 55 genes with significant mutation (Q value <= 0.1) and 411 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 61 significant pathways identified in this analysis.

Methods & Data
Input

  • Run Prefix = awg_pancan8__2012_08_25

  • Summary Report Date = Fri Sep 14 18:07:17 2012

  • Protection = FALSE

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