Analysis Overview
PANCANCER cohort with 12 disease types (Primary solid tumor)
22 February 2013  |  analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C1VT1Q96
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 PANCANCER cohort with 12 disease types (Primary solid tumor cohort) - 22 February 2013. Broad Institute of MIT and Harvard. doi:10.7908/C1VT1Q96
Overview
Introduction

This is an overview of PANCANCER cohort with 12 disease types analysis pipelines from Firehose run "22 February 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 4806 tumor samples used in this analysis: 33 significant arm-level results, 40 significant focal amplifications, and 51 significant focal deletions were found.

  • Correlations to Clinical Parameters

    • Correlation between copy number variation genes (focal) and selected clinical features
      View Report | Testing the association between subtypes identified by 91 different clustering approaches and 21 clinical features across 4563 patients, 527 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 subtypes identified by 80 different clustering approaches and 21 clinical features across 4563 patients, 329 significant findings detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 15521 genes and 17 clinical features across 238 samples, statistically thresholded by Q value < 0.05, 9 clinical features related to at least one genes.

    • Correlation between miR expression and clinical features
      View Report | Testing the association between 817 miRs and 7 clinical features across 560 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one miRs.

    • Correlation between mRNA expression and clinical features
      View Report | Testing the association between 17814 genes and 20 clinical features across 1596 samples, statistically thresholded by Q value < 0.05, 17 clinical features related to at least one genes.

  • Clustering Analyses

    • Clustering of miR expression: consensus hierarchical
      View Report | We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 568 samples and 150 miRs identified 6 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 miR expression: consensus NMF
      View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 568 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 mRNA expression: consensus hierarchical
      View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 1604 samples and 1500 genes identified 5 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 1604 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.

  • Other Analyses

    • Identification of putative miR direct targets
      View Report | This pipeline use a relevance network approach to infer putative miR:mRNA regulatory connections. All miR:mRNA pairs that have correlations < -0.3 and have predicted interactions in three sequence prediction databases (Miranda, Pictar, Targetscan) define the final network.

  • Other Correlation Analyses

    • Correlation between copy number and miR expression
      View Report

    • Correlation between mRNA expression and DNA methylation
      View Report | The top 25 correlated methylation probes per gene are displayed. Total number of matched samples = 238. Number of gene expression samples = 1604. Number of methylation samples = 238.

Methods & Data
Input

  • Summary Report Date = Mon Aug 5 20:31:14 2013

  • Protection = FALSE

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