Analysis Overview
PANCANCER cohort with 12 disease types (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1XG9PM1
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 PANCANCER cohort with 12 disease types (Primary solid tumor cohort) - 15 January 2014. Broad Institute of MIT and Harvard. doi:10.7908/C1XG9PM1
Overview
Introduction

This is an overview of PANCANCER cohort with 12 disease types analysis pipelines from Firehose run "15 January 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

    • LowPass Copy number analysis (GISTIC2)
      View Report | There were 568 tumor samples used in this analysis: 24 significant arm-level results, 48 significant focal amplifications, and 15 significant focal deletions were found.

  • Correlations to Clinical Parameters

    • Correlation between miR expression and clinical features
      View Report | Testing the association between 817 miRs and 6 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 15 clinical features across 1593 samples, statistically thresholded by Q value < 0.05, 12 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 1601 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 1601 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.

Methods & Data
Input

  • Summary Report Date = Fri Feb 28 12:46:51 2014

  • Protection = FALSE

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