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

This is the analysis overview for Firehose run "25 July 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 265 tumor samples used in this analysis: 22 significant arm-level results, 24 significant focal amplifications, and 38 significant focal deletions were found.

- Clustering Analyses

- Clustering of Methylation: consensus NMF
  View Report | The 4380 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 220 samples and 4380 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 mRNA expression: consensus NMF
  View Report | The most robust consensus NMF clustering of 32 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 32 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 240 samples using the 1500 most variable genes 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 mRNAseq gene expression: consensus hierarchical
  View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 240 samples and 1500 genes 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 miRseq expression: consensus NMF
  View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 100 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 100 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.

- Correlation Analyses

- Correlation between molecular cancer subtypes and selected clinical features
  View Report | Testing the association between subtypes identified by 7 different clustering approaches and 10 clinical features across 283 patients, 10 significant findings detected with P value < 0.05.

- Correlation between mRNA expression and clinical features
  View Report | Testing the association between 17814 genes and 7 clinical features across 32 samples, statistically thresholded by Q value < 0.05, 2 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 -0.1254, -0.0197, 0.0617, 0.13288, 0.1995, 0.27192, 0.3438, 0.42186, 0.52398, 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 1060.1, 1736, 2320.3, 2917, 3506.5, 4142, 4797, 5440.8, 6184.9, respectively.

- Correlation between mRNA expression and DNA methylation
  View Report | The top 25 correlated methylation probe(s) per gene are displayed. Total number of matched samples = 31 Number of gene expression samples = 129 Number of methylation samples = 32

- Other Analyses

- Correlation between copy number variations of arm-level result and selected clinical features
  View Report | Testing the association between copy number variation 77 arm-level results and 10 clinical features across 265 patients, 3 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 62 peak regions and 10 clinical features across 265 patients, one significant finding detected with Q value < 0.25.

- PARADIGM pathway analysis of mRNA expression data
  View Report | There were 42 significant pathways identified in this analysis.

- PARADIGM pathway analysis of mRNA expression and copy number data
  View Report | There were 41 significant pathways identified in this analysis.

Methods & Data
Input

- Run Prefix = analyses__2012_07_25

- Summary Report Date = Fri Aug 17 15:14:24 2012

- Protection = FALSE

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