Analysis Overview for Kidney Renal Papillary 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 July 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 75 tumor samples used in this analysis: 15 significant arm-level results, 0 significant focal amplifications, and 6 significant focal deletions were found.

- Clustering Analyses

- Clustering of Methylation: consensus NMF
  View Report | The 7981 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 81 samples and 7981 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 63 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.

- Clustering of mRNAseq gene expression: consensus hierarchical
  View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 63 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 43 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 43 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 molecular cancer subtypes and selected clinical features
  View Report | Testing the association between subtypes identified by 7 different clustering approaches and 7 clinical features across 95 patients, 11 significant findings detected with P value < 0.05.

- Correlation between mRNA expression and clinical features
  View Report | Testing the association between 17814 genes and 3 clinical features across 16 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 -0.4716, -0.26136, -0.09026, 0.05506, 0.1966, 0.32504, 0.454, 0.58242, 0.72518, 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 913.1, 2044.2, 2696, 3257, 3807, 4349, 4905.7, 5516, 6256.9, respectively.

- Other Analyses

- Correlation between copy number variations of arm-level result and selected clinical features
  View Report | Testing the association between copy number variation 48 arm-level results and 7 clinical features across 75 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 6 peak regions and 7 clinical features across 75 patients, one significant finding detected with Q value < 0.25.

- 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 39 significant pathways identified in this analysis.

Methods & Data
Input

- Run Prefix = analyses__2012_07_25

- Summary Report Date = Fri Aug 17 15:10:07 2012

- Protection = FALSE

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