Analysis Overview
Lung Adenocarcinoma (MOLECULAR_NONSMOKER)
07 February 2013  |  awg_luad__2013_02_07
Maintainer Information
Citation Information
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 Lung Adenocarcinoma (MOLECULAR_NONSMOKER cohort) - 07 February 2013. Broad Institute of MIT and Harvard. doi:10.7908/C13N21G0
Overview
Introduction

This is an overview of Lung Adenocarcinoma analysis pipelines from Firehose run "07 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 81 tumor samples used in this analysis: 17 significant arm-level results, 16 significant focal amplifications, and 26 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 and selected clinical features
      View Report | Testing the association between copy number variation of 42 peak regions and 14 clinical features across 80 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 68 arm-level results and 14 clinical features across 80 patients, 2 significant findings detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 17281 genes and 14 clinical features across 62 samples, statistically thresholded by Q value < 0.05, 6 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 15 genes and 14 clinical features across 50 patients, no significant finding detected with Q value < 0.25.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 538 genes and 14 clinical features across 80 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

    • Correlation between mRNA expression and clinical features
      View Report | Testing the association between 17814 genes and 8 clinical features across 9 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 18227 genes and 14 clinical features across 80 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.

    • Correlation between RPPA expression and clinical features
      View Report | Testing the association between 174 genes and 14 clinical features across 66 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 81 samples using the 42 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 3482 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 63 samples and 3482 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 expression: consensus hierarchical
      View Report | We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 81 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 81 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 81 samples and 1500 genes 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 mRNAseq gene expression: consensus NMF
      View Report | The most robust consensus NMF clustering of 81 samples using the 1500 most variable genes was identified for k = 4 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 | The 150 most variable proteins were selected. Consensus average linkage hierarchical clustering of 66 samples and 150 proteins 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 RPPA data: consensus NMF
      View Report | The most robust consensus NMF clustering of 66 samples using the 150 most variable 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.

  • Other Analyses

    • Correlate_Clinical_vs_Molecular_Signatures
      View Report | Testing the association between subtypes identified by 8 different clustering approaches and 14 clinical features across 80 patients, 5 significant findings detected with P value < 0.05 and Q value < 0.25.

    • Hotnet_nozzleReport
      View Report | There were 25 significant subnetworks identified in HotNet analysis.

    • ParadigmReport
      View Report | There were 17 significant pathways identified in this analysis.

    • ParadigmReportWithCopyNumber
      View Report | There were 12 significant pathways identified in this analysis.

    • ParadigmReportWithRNASeq
      View Report | There were 34 significant pathways identified in this analysis.

    • ParadigmReportWithRNASeqAndCopyNumber
      View Report | There were 16 significant pathways identified in this analysis.

  • Other Correlation Analyses

    • Correlation between gene mutation status and selected clinical features
      View Report | Testing the association between mutation status of 15 genes and 8 clinical features across 50 patients, 98 significant findings detected with P value < 1.

    • Correlation between mRNA expression and DNA methylation
      View Report | The top 25 correlated methylation probes per gene are displayed. Total number of matched samples = 63. Number of gene expression samples = 81. Number of methylation samples = 63.

    • Correlations between copy number and mRNA expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are -0.31188, -0.1302, 0.0108, 0.13158, 0.2402, 0.34752, 0.4539, 0.5627, 0.68836, respectively.

    • Correlations between copy number and mRNAseq expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 985, 2044, 2723, 3380, 4019, 4659, 5330, 6022.8, 6806, respectively.

    • Preprocessing of clinical data
      View Report | Clinical data for tier 1 clinical variables are generated.

Methods & Data
Input
  • Summary Report Date = Thu Jul 11 10:30:43 2013

  • Protection = FALSE