Analysis Overview
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
21 April 2013  |  analyses__2013_04_21
Maintainer Information
Citation Information
doi:10.7908/C1571904
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 Papillary Cell Carcinoma (Primary solid tumor cohort) - 21 April 2013. Broad Institute of MIT and Harvard. doi:10.7908/C1571904
Overview
Introduction

This is an overview of Kidney Renal Papillary Cell Carcinoma analysis pipelines from Firehose run "21 April 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 127 tumor samples used in this analysis: 16 significant arm-level results, 9 significant focal amplifications, and 25 significant focal deletions were found.

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

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

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

  • 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 127 samples using the 34 copy number focal regions was identified for k = 5 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 7742 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 111 samples and 7742 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 16 samples using the 1500 most variable genes was identified for k = 2 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 16 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 76 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 76 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 precursor expression: consensus NMF
      View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 117 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 precursor expression: consensus hierarchical
      View Report | We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 117 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.

  • Correlation Analyses

    • Correlation between copy number variations of arm-level result and selected clinical features
      View Report | Testing the association between copy number variation 58 arm-level results and 8 clinical features across 104 patients, 8 significant findings detected with Q value < 0.25.

    • Correlation between copy number variation genes (focal) and selected clinical features
      View Report | Testing the association between copy number variation 33 arm-level results and 8 clinical features across 104 patients, 4 significant findings detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 17247 genes and 8 clinical features across 59 samples, statistically thresholded by Q value < 0.05, 5 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 8 different clustering approaches and 8 clinical features across 104 patients, 9 significant findings detected with P value < 0.05 and Q value < 0.25.

    • Correlation between gene mutation status and selected clinical features
      View Report | Testing the association between mutation status of 7 genes and 8 clinical features across 101 patients, one significant finding detected with Q value < 0.25.

    • Correlation between mRNA expression and clinical features
      View Report | Testing the association between 17814 genes and 4 clinical features across 16 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

    • Correlation between mRNAseq expression and clinical features
      View Report | Testing the association between 18174 genes and 8 clinical features across 75 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 504 genes and 8 clinical features across 104 samples, statistically thresholded by Q value < 0.05, 4 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 -0.52769, -0.30528, -0.1208, 0.0345, 0.1843, 0.3315, 0.48097, 0.62018, 0.76409, 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 866.4, 2054, 2716, 3313, 3862, 4416, 4998, 5629, 6409.6, 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 = 60. Number of gene expression samples = 76. Number of methylation samples = 60.

    • Correlation between copy number variations of arm-level result and molecular subtypes
      View Report | Testing the association between copy number variation 59 arm-level results and 8 molecular subtypes across 127 patients, 40 significant findings detected with Q value < 0.25.

    • Correlation between copy number variation genes and molecular subtypes
      View Report | Testing the association between copy number variation of 33 peak regions and 8 molecular subtypes across 127 patients, 33 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 9 genes and 8 molecular subtypes across 111 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.

  • Pathway Analyses

    • Association of mutation, copy number alteration, and subtype markers with pathways
      View Report | There are 7 genes with significant mutation (Q value <= 0.1) and 351 genes with significant copy number alteration (Q value <= 0.25). The identified marker genes (Q value <= 0.01 or within top 2000) are 0 for subtype 1. 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 17 significant subnetworks identified in HotNet analysis.

    • PARADIGM pathway analysis of mRNASeq expression data
      View Report | There were 31 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression and copy number data
      View Report | There were 25 significant pathways identified in this analysis.

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

    • PARADIGM pathway analysis of mRNA expression and copy number data
      View Report | There were 42 significant pathways identified in this analysis.

Methods & Data
Input

  • Summary Report Date = Sat May 25 13:35:12 2013

  • Protection = FALSE

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