Analysis Overview for Lung Squamous Cell Carcinoma
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
Overview
Introduction

This is the analysis overview for Firehose run "24 October 2012".

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 343 tumor samples used in this analysis: 30 significant arm-level results, 30 significant focal amplifications, and 45 significant focal deletions were found.

    • Mutation Analysis (MutSig vS2N)
      View Report | 

  • Clustering Analyses

    • Clustering of copy number data: consensus NMF
      View Report | The most robust consensus NMF clustering of 343 samples using the 75 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 4855 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 226 samples and 4855 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 RPPA data: consensus NMF
      View Report | The most robust consensus NMF clustering of 195 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.

    • Clustering of RPPA data: consensus hierarchical
      View Report | The 150 most variable proteins were selected. Consensus average linkage hierarchical clustering of 195 samples and 150 proteins 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 mRNA expression: consensus NMF
      View Report | The most robust consensus NMF clustering of 154 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 mRNA expression: consensus hierarchical
      View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 154 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 220 samples using the 1500 most variable genes was identified for k = 8 clusters. We computed the clustering for k = 2 to k = 8 and used the cophenetic correlation coefficient to determine the best solution.

    • Clustering of mRNAseq gene expression: consensus hierarchical
      View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 220 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 miRseq expression: consensus NMF
      View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 332 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 hierarchical
      View Report | We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 332 samples and 150 miRs 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.

  • Correlation Analyses

    • 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 results and 11 clinical features across 307 patients, 13 significant findings detected with Q value < 0.25.

    • Correlation between copy number variation genes and selected clinical features
      View Report | Testing the association between copy number variation of 75 peak regions and 11 clinical features across 307 patients, one significant finding detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 17552 genes and 11 clinical features across 92 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

    • Correlation between 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 309 patients, 17 significant findings detected with P value < 0.05.

    • Correlation between RPPA expression and clinical features
      View Report | Testing the association between 174 genes and 11 clinical features across 190 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.

    • Correlation between mRNA expression and clinical features
      View Report | Testing the association between 17814 genes and 10 clinical features across 154 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 18545 genes and 11 clinical features across 220 samples, statistically thresholded by Q value < 0.05, 8 clinical features related to at least one genes.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 548 genes and 11 clinical features across 282 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

    • Correlations between copy number and mRNA expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are -0.02388, 0.0439, 0.10286, 0.1714, 0.2449, 0.3233, 0.4014, 0.4794, 0.5678, 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 1113, 1788.2, 2374.3, 2965, 3604, 4264.6, 4974, 5675, 6422.9, respectively.

    • Correlation between mRNA expression and DNA methylation
      View Report | The top 25 correlated methylation probes per gene are displayed. Total number of matched samples = 92. Number of gene expression samples = 220. Number of methylation samples = 92.

  • Other Analyses

    • PARADIGM pathway analysis of mRNA expression data
      View Report | There were 62 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNA expression and copy number data
      View Report | There were 43 significant pathways identified in this analysis.

Methods & Data
Input

  • Run Prefix = analyses__2012_10_24

  • Summary Report Date = Fri Nov 16 16:11:07 2012

  • Protection = FALSE

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