Analysis Overview for Skin Cutaneous Melanoma
(WT cohort)
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Overview
Introduction

This is the analysis overview for Firehose run "02 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 35 tumor samples used in this analysis: 16 significant arm-level results, 12 significant focal amplifications, and 8 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 35 samples using the 20 copy number focal regions 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 Methylation: consensus NMF
      View Report | The 11637 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 35 samples and 11637 genes identified 2 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 20 samples using the 175 most variable proteins 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 RPPA data: consensus hierarchical
      View Report | The 175 most variable proteins were selected. Consensus average linkage hierarchical clustering of 20 samples and 175 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 mRNAseq gene expression: consensus NMF
      View Report | The most robust consensus NMF clustering of 33 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 mRNAseq gene expression: consensus hierarchical
      View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 33 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 34 samples and 150 miRs identified 2 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 34 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

    • Preprocessing of clinical data
      View Report | Clinical data for tier 1 clinical variables are generated.

    • Correlation between copy number variations of arm-level result and selected clinical features
      View Report | Testing the association between copy number variation 40 arm-level results and 7 clinical features across 23 patients, no significant finding 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 40 arm-level results and 7 clinical features across 23 patients, no significant finding 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 20 arm-level results and 7 clinical features across 23 patients, no significant finding 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 20 arm-level results and 7 clinical features across 23 patients, no significant finding detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 17162 genes and 6 clinical features across 18 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 17162 genes and 6 clinical features across 18 samples, statistically thresholded by Q value < 0.05, 3 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 7 clinical features across 23 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.

    • Correlation between molecular cancer subtypes and selected clinical features
      View Report | Testing the association between subtypes identified by 8 different clustering approaches and 7 clinical features across 23 patients, no significant finding 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 5 genes and 7 clinical features across 22 patients, no significant finding detected with Q value < 0.25.

    • Correlation between gene mutation status and selected clinical features
      View Report | Testing the association between mutation status of 5 genes and 7 clinical features across 22 patients, no significant finding detected with Q value < 0.25.

    • Correlation between RPPA expression and clinical features
      View Report | Testing the association between 175 genes and 6 clinical features across 17 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

    • Correlation between RPPA expression and clinical features
      View Report | Testing the association between 175 genes and 6 clinical features across 17 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 18110 genes and 6 clinical features across 21 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 18110 genes and 6 clinical features across 21 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 593 genes and 6 clinical features across 23 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 593 genes and 6 clinical features across 23 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

    • Correlations between copy number and mRNAseq expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 986, 2281, 3075, 3791, 4477, 5135, 5856.5, 6644, 7502.5, 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 = 33. Number of gene expression samples = 33. Number of methylation samples = 33.

    • Correlation between copy number variations of arm-level result and molecular subtypes
      View Report | Testing the association between copy number variation 55 arm-level results and 8 molecular subtypes across 35 patients, 2 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 20 peak regions and 8 molecular subtypes across 35 patients, 2 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 8 genes and 8 molecular subtypes across 34 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.

  • Pathway Analyses

    • HotNet pathway analysis of mutation and copy number data
      View Report | There were 3 significant subnetworks identified in HotNet analysis.

    • PARADIGM pathway analysis of mRNASeq expression data
      View Report | There were 55 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression and copy number data
      View Report | There were 64 significant pathways identified in this analysis.

Methods & Data
Input

  • Run Prefix = awg_skcm__2013_05_02

  • Summary Report Date = Mon May 13 16:35:38 2013

  • Protection = FALSE

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