Analysis Overview
Stomach Adenocarcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1445K5V
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 Stomach Adenocarcinoma (Primary solid tumor cohort) - 16 April 2014. Broad Institute of MIT and Harvard. doi:10.7908/C1445K5V
Overview
Introduction

This is an overview of Stomach Adenocarcinoma analysis pipelines from Firehose run "16 April 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 107 tumor samples used in this analysis: 21 significant arm-level results, 17 significant focal amplifications, and 5 significant focal deletions were found.

    • Mutation Analysis (MutSig v1.5)
      View Report | 

    • Mutation Analysis (MutSig v2.0 and MutSigCV v0.9 merged result)
      View Report | 

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

    • Mutation Analysis (MutSigCV v0.9)
      View Report | 

    • Mutation Assessor
      View Report | 

    • SNP6 Copy number analysis (GISTIC2)
      View Report | There were 351 tumor samples used in this analysis: 26 significant arm-level results, 33 significant focal amplifications, and 45 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 10 different clustering approaches and 11 clinical features across 311 patients, 12 significant findings detected with P value < 0.05 and Q value < 0.25.

    • Correlation between copy number variation genes (focal events) and selected clinical features
      View Report | Testing the association between copy number variation 78 focal events and 11 clinical features across 309 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 79 arm-level events and 11 clinical features across 309 patients, 3 significant findings detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 19730 genes and 11 clinical features across 263 samples, statistically thresholded by Q value < 0.05, 8 clinical features related to at least one genes.

    • Correlation between gene mutation status and selected clinical features
      View Report | Testing the association between mutation status of 20 genes and 11 clinical features across 220 patients, 5 significant findings detected with Q value < 0.25.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 507 miRs and 11 clinical features across 310 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one miRs.

    • Correlation between mRNAseq expression and clinical features
      View Report | Testing the association between 23365 genes and 11 clinical features across 274 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.

    • Correlation between RPPA expression and clinical features
      View Report | Testing the association between 189 genes and 11 clinical features across 264 samples, statistically thresholded by Q value < 0.05, 5 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 351 samples using the 78 copy number focal regions was identified for k = 5 clusters. We computed the clustering for k = 2 to k = 8 and used the cophenetic correlation coefficient to determine the best solution.

    • Clustering of copy number data by peak region with threshold value: consensus NMF
      View Report | The most robust consensus NMF clustering of 351 samples using the 78 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 6095 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 325 samples and 6095 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 251 most variable miRs. Consensus average linkage hierarchical clustering of 276 samples and 251 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 251 most variable miRs. Consensus NMF clustering of 276 samples and 251 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 hierarchical
      View Report | We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 323 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 323 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 274 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 274 samples using the 1500 most variable genes was identified for k = 5 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 | 189 proteins were selected. Consensus average linkage hierarchical clustering of 264 samples and 189 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 RPPA data: consensus NMF
      View Report | The most robust consensus NMF clustering of 264 samples using 189 proteins 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.

  • Pathway Analyses

    • HotNet pathway analysis of mutation and copy number data
      View Report | There were 72 significant subnetworks identified in HotNet analysis.

    • PARADIGM pathway analysis of mRNASeq expression and copy number data
      View Report | There were 40 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression data
      View Report | There were 43 significant pathways identified in this analysis.

  • Other Correlation Analyses

    • Correlation between copy number variation genes (focal events) and molecular subtypes
      View Report | Testing the association between copy number variation 78 focal events and 10 molecular subtypes across 351 patients, 315 significant findings detected with P value < 0.05 and 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 79 arm-level events and 10 molecular subtypes across 351 patients, 173 significant findings detected with P value < 0.05 and Q value < 0.25.

    • Correlation between gene mutation status and molecular subtypes
      View Report | Testing the association between mutation status of 20 genes and 10 molecular subtypes across 220 patients, 22 significant findings detected with P value < 0.05 and 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 = 231. Number of gene expression samples = 274. Number of methylation samples = 325.

    • Correlations between copy number and mRNAseq expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 956.2, 1690, 2182, 2650, 3092, 3560, 4010, 4529.6, 5208, respectively.

Methods & Data
Input

  • Summary Report Date = Sat May 10 11:59:53 2014

  • Protection = FALSE

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