Analysis Overview - Esophageal Carcinoma (Primary solid tumor)

Analysis Overview

Esophageal Carcinoma (Primary solid tumor)

23 May 2013 | analyses__2013_05_23

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 Esophageal Carcinoma (Primary solid tumor cohort) - 23 May 2013. Broad Institute of MIT and Harvard. doi:10.7908/C1736NXH

Overview

Introduction

This is an overview of Esophageal Carcinoma analysis pipelines from Firehose run "23 May 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 50 tumor samples used in this analysis: 21 significant arm-level results, 21 significant focal amplifications, and 32 significant focal deletions were found.

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 50 samples using the 53 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 9365 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 50 samples and 9365 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.

Other Correlation Analyses

Correlation between copy number variation genes and molecular subtypes
View Report | Testing the association between copy number variation of 53 peak regions and 2 molecular subtypes across 50 patients, 3 significant findings detected with 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 65 arm-level results and 2 molecular subtypes across 50 patients, 2 significant findings detected with Q value < 0.25.

Methods & Data

Input

Summary Report Date = Wed Jun 26 16:25:00 2013
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