Analysis Overview
Stomach Adenocarcinoma (Primary solid tumor)
22 February 2013  |  analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C10000BJ
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 Stomach Adenocarcinoma (Primary solid tumor cohort) - 22 February 2013. Broad Institute of MIT and Harvard. doi:10.7908/C10000BJ
Overview
Introduction

This is an overview of Stomach Adenocarcinoma 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 237 tumor samples used in this analysis: 25 significant arm-level results, 26 significant focal amplifications, and 46 significant focal deletions were found.

    • Mutation Analysis (MutSig v2.0)
      View Report | 

    • Mutation Analysis (MutSig vS2N)
      View Report | 

    • Mutation Analysis (MutSigCV v0.6)
      View Report | 

  • Correlations to Clinical Parameters

    • Correlation between copy number variation genes (focal) and selected clinical features
      View Report | Testing the association between subtypes identified by 72 different clustering approaches and 9 clinical features across 161 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 subtypes identified by 74 different clustering approaches and 9 clinical features across 161 patients, 6 significant findings detected with Q value < 0.25.

    • Correlation between gene mutation status and selected clinical features
      View Report | Testing the association between mutation status of 47 genes and 8 clinical features across 116 patients, 2 significant findings detected with Q value < 0.25.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 533 genes and 9 clinical features across 161 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 19433 genes and 8 clinical features across 43 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

  • Clustering Analyses

    • Clustering of copy number data: consensus NMF
      View Report | The most robust consensus NMF clustering of 237 samples using the 72 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 5998 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 209 samples and 5998 genes identified 4 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 237 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 NMF
      View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 237 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 mRNAseq gene expression: consensus hierarchical
      View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 43 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 43 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.

  • Other Analyses

    • Correlate_Clinical_vs_Molecular_Signatures
      View Report | Testing the association between subtypes identified by 6 different clustering approaches and 9 clinical features across 162 patients, 4 significant findings 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 76 significant subnetworks identified in HotNet analysis.

    • PARADIGM pathway analysis of mRNASeq expression and copy number data
      View Report | There were 12 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression data
      View Report | There were 18 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 72 peak regions and 6 molecular subtypes across 237 patients, 108 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 74 arm-level results and 6 molecular subtypes across 237 patients, 46 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 47 genes and 6 molecular subtypes across 116 patients, 14 significant findings detected with P value < 0.05 and Q value < 0.25.

    • Correlations between copy number and mRNAseq expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 1118.2, 2319.4, 3065.6, 3788, 4531, 5253.2, 6000.4, 6791, 7664.8, respectively.

Methods & Data
Input

  • Summary Report Date = Mon Aug 5 20:52:39 2013

  • Protection = FALSE

Copyright © 2013 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle