Analysis Overview for Liver Hepatocellular Carcinoma
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Overview
Introduction

This is the analysis overview for Firehose run "21 December 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 97 tumor samples used in this analysis: 23 significant arm-level results, 26 significant focal amplifications, and 27 significant focal deletions were found.

- Clustering Analyses

- Clustering of copy number data: consensus NMF
  View Report | The most robust consensus NMF clustering of 97 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 11627 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 98 samples and 11627 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 96 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 96 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 61 arm-level results and 3 clinical features across 61 patients, 2 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 53 peak regions and 3 clinical features across 61 patients, no significant finding detected with Q value < 0.25.

- Correlation between molecular cancer subtypes and selected clinical features
  View Report | Testing the association between subtypes identified by 4 different clustering approaches and 3 clinical features across 62 patients, one significant finding detected with P value < 0.05.

- Correlation between miRseq expression and clinical features
  View Report | Testing the association between 570 genes and 3 clinical features across 61 samples, statistically thresholded by Q value < 0.05, no clinical feature related to at least one genes.

Methods & Data
Input

- Run Prefix = analyses__2012_12_21

- Summary Report Date = Fri Jan 25 11:49:49 2013

- Protection = FALSE

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