Analysis Overview
Esophageal Carcinoma (Primary solid tumor)
16 April 2014 | analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1930RS9
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 Esophageal Carcinoma (Primary solid tumor cohort) - 16 April 2014. Broad Institute of MIT and Harvard. doi:10.7908/C1930RS9
Overview
Introduction

This is an overview of Esophageal Carcinoma 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 32 tumor samples used in this analysis: 16 significant arm-level results, 1 significant focal amplifications, and 6 significant focal deletions were found.

- SNP6 Copy number analysis (GISTIC2)
  View Report | There were 103 tumor samples used in this analysis: 22 significant arm-level results, 24 significant focal amplifications, and 47 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 8 different clustering approaches and 8 clinical features across 57 patients, 7 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 71 focal events and 8 clinical features across 56 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 8 clinical features across 56 patients, no significant finding detected with Q value < 0.25.

- Correlation between gene methylation status and clinical features
  View Report | Testing the association between 19880 genes and 8 clinical features across 57 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

- Correlation between miRseq expression and clinical features
  View Report | Testing the association between 517 miRs and 8 clinical features across 52 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one miRs.

- Correlation between mRNAseq expression and clinical features
  View Report | Testing the association between 23891 genes and 8 clinical features across 51 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 103 samples using the 71 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 copy number data by peak region with threshold value: consensus NMF
  View Report | The most robust consensus NMF clustering of 103 samples using the 71 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 8554 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 126 samples and 8554 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 238 most variable miRs. Consensus average linkage hierarchical clustering of 72 samples and 238 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 238 most variable miRs. Consensus NMF clustering of 72 samples and 238 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.

- Clustering of miRseq precursor expression: consensus hierarchical
  View Report | We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 72 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 72 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 70 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 70 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.

- Pathway Analyses

- PARADIGM pathway analysis of mRNASeq expression and copy number data
  View Report | There were 39 significant pathways identified in this analysis.

- PARADIGM pathway analysis of mRNASeq expression data
  View Report | There were 37 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 71 focal events and 8 molecular subtypes across 103 patients, 20 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 80 arm-level events and 8 molecular subtypes across 103 patients, 5 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 = 70. Number of gene expression samples = 70. Number of methylation samples = 126.

- Correlations between copy number and mRNAseq expression
  View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 1102, 2226, 2944, 3573.4, 4213, 4848, 5497, 6184, 6931.9, respectively.

Methods & Data
Input

- Summary Report Date = Sat May 10 10:14:34 2014

- Protection = FALSE

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