Analysis Overview
Pancreatic Adenocarcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C1TT4P11
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 Pancreatic Adenocarcinoma (Primary solid tumor cohort) - 23 May 2013. Broad Institute of MIT and Harvard. doi:10.7908/C1TT4P11
Overview
Introduction

This is an overview of Pancreatic Adenocarcinoma analysis pipelines from Firehose run "23 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 50 tumor samples used in this analysis: 15 significant arm-level results, 11 significant focal amplifications, and 24 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 10 clinical features across 26 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.

    • Correlation between copy number variation genes (focal) and selected clinical features
      View Report | Testing the association between copy number variation 28 arm-level results and 10 clinical features across 20 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 '17p loss' and 10 clinical features across 20 patients, no significant finding detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 20596 genes and 8 clinical features across 23 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 515 genes and 8 clinical features across 20 samples, statistically thresholded by Q value < 0.05, no clinical feature related to at least one genes.

    • Correlation between mRNAseq expression and clinical features
      View Report | Testing the association between 18603 genes and 7 clinical features across 18 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

  • 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 50 samples using the 35 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 Methylation: consensus NMF
      View Report | The 803 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 65 samples and 803 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 | We filtered the data to 159 most variable miRs. Consensus average linkage hierarchical clustering of 43 samples and 159 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 mature expression: consensus NMF
      View Report | We filtered the data to 159 most variable miRs. Consensus NMF clustering of 43 samples and 159 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 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 precursor expression: consensus NMF
      View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 43 samples and 150 miRs 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 mRNAseq gene expression: consensus hierarchical
      View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 40 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 40 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.

  • Pathway Analyses

    • PARADIGM pathway analysis of mRNASeq expression and copy number data
      View Report | There were 24 significant pathways identified in this analysis.

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

  • Other Correlation Analyses

    • Correlation between copy number variation genes and molecular subtypes
      View Report | Testing the association between copy number variation of 35 peak regions and 8 molecular subtypes across 50 patients, 42 significant findings detected with 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 19 arm-level results and 8 molecular subtypes across 50 patients, 5 significant findings detected with 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 = 40. Number of gene expression samples = 40. Number of methylation samples = 65.

    • Correlations between copy number and mRNAseq expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 897.7, 2190, 2913, 3547, 4157, 4755, 5397, 6124.6, 6975.3, respectively.

Methods & Data
Input

  • Summary Report Date = Wed Jun 26 18:08:01 2013

  • Protection = FALSE

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