Analysis Overview
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C15719SF
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Analysis Overview for Kidney Renal Papillary Cell Carcinoma (Primary solid tumor cohort) - 15 July 2014. Broad Institute of MIT and Harvard. doi:10.7908/C15719SF
Overview
Introduction

This is an overview of Kidney Renal Papillary Cell Carcinoma analysis pipelines from Firehose run "15 July 2014".

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

    • Mutation Analysis (MutSig 2CV v3.1)
      View Report | 

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

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

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

    • Mutation Assessor
      View Report | 

    • SNP6 Copy number analysis (GISTIC2)
      View Report | There were 224 tumor samples used in this analysis: 17 significant arm-level results, 7 significant focal amplifications, and 22 significant focal deletions were found.

  • Correlations to Clinical Parameters

    • Correlation between aggregated molecular cancer subtypes and selected clinical features
      View Report | Testing the association between subtypes identified by 8 different clustering approaches and 11 clinical features across 202 patients, 18 significant findings detected with P value < 0.05 and Q value < 0.25.

    • Correlation between copy number variation genes (focal events) and selected clinical features
      View Report | Testing the association between copy number variation 29 focal events and 11 clinical features across 198 patients, 37 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 69 arm-level events and 11 clinical features across 198 patients, 47 significant findings detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 19873 genes and 11 clinical features across 186 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 8 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 28 genes and 11 clinical features across 165 patients, one significant finding detected with Q value < 0.25.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 482 miRs and 11 clinical features across 202 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 8 clinical features related to at least one miRs.

    • Correlation between mRNAseq expression and clinical features
      View Report | Testing the association between 18037 genes and 11 clinical features across 197 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 9 clinical features related to at least one genes.

    • Correlation between mutation rate and clinical features
      View Report | Testing the association between 2 variables and 12 clinical features across 165 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one variables.

  • 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 224 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 copy number data by peak region with threshold value: consensus NMF
      View Report | The most robust consensus NMF clustering of 224 samples using the 29 copy number focal regions was identified for k = 4 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 7210 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 226 samples and 7210 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 | Median absolute deviation (MAD) was used to select 292 most variable miRs. Consensus ward linkage hierarchical clustering of 242 samples and 292 miRs identified 7 subtypes with the stability of the clustering increasing for k = 2 to k = 10.

    • Clustering of miRseq mature expression: consensus NMF
      View Report | We filtered the data to 292 most variable miRs. Consensus NMF clustering of 242 samples and 292 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 | Median absolute deviation (MAD) was used to select 120 most variable miRs. Consensus ward linkage hierarchical clustering of 242 samples and 120 miRs identified 3 subtypes with the stability of the clustering increasing for k = 2 to k = 10.

    • Clustering of miRseq precursor expression: consensus NMF
      View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 242 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 mRNAseq gene expression: consensus hierarchical
      View Report | Median absolute deviation (MAD) was used to select 1500 most variable genes. Consensus ward linkage hierarchical clustering of 213 samples and 1500 genes identified 3 subtypes with the stability of the clustering increasing for k = 2 to k = 10.

    • Clustering of mRNAseq gene expression: consensus NMF
      View Report | The most robust consensus NMF clustering of 213 samples using the 1500 most variable genes was identified for k = 4 clusters. We computed the clustering for k = 2 to k = 8 and used the cophenetic correlation coefficient to determine the best solution.

  • Pathway Analyses

    • PARADIGM pathway analysis of mRNA expression and copy number data
      View Report | There were 47 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 mRNASeq expression and copy number data
      View Report | There were 25 significant pathways identified in this analysis.

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

  • Other Correlation Analyses

    • Correlation between copy number variation genes (focal events) and molecular subtypes
      View Report | Testing the association between copy number variation 29 focal events and 8 molecular subtypes across 224 patients, 69 significant findings detected with P value < 0.05 and 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 events and 8 molecular subtypes across 224 patients, 111 significant findings detected with P value < 0.05 and Q value < 0.25.

    • Correlation between gene mutation status and molecular subtypes
      View Report | Testing the association between mutation status of 28 genes and 8 molecular subtypes across 168 patients, 2 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 = 197. Number of gene expression samples = 213. Number of methylation samples = 226.

    • Correlations between copy number and mRNAseq expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 872.8, 1936, 2528, 3075, 3597, 4136.8, 4732, 5358.4, 6116, respectively.

Methods & Data
Input

  • Summary Report Date = Tue Sep 16 18:04:12 2014

  • Protection = FALSE

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