Analysis Overview - Pheochromocytoma and Paraganglioma (Primary solid tumor)

Analysis Overview

Pheochromocytoma and Paraganglioma (Primary solid tumor)

16 April 2014 | analyses__2014_04_16

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 (2014): Analysis Overview for Pheochromocytoma and Paraganglioma (Primary solid tumor cohort) - 16 April 2014. Broad Institute of MIT and Harvard. doi:10.7908/C19K48XN

Overview

Introduction

This is an overview of Pheochromocytoma and Paraganglioma 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

SNP6 Copy number analysis (GISTIC2)
View Report | There were 159 tumor samples used in this analysis: 18 significant arm-level results, 5 significant focal amplifications, and 22 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 4 different clustering approaches and 2 clinical features across 10 patients, no significant finding 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 10 focal events and 2 clinical features across 9 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 10 arm-level events and 2 clinical features across 9 patients, no significant finding detected with Q value < 0.25.
Correlation between miRseq expression and clinical features
View Report | Testing the association between 528 miRs and 2 clinical features across 10 samples, statistically thresholded by Q value < 0.05, no clinical feature related to at least one miRs.

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 159 samples using the 27 copy number focal regions was identified for k = 6 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 159 samples using the 27 copy number focal regions was identified for k = 5 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 11233 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 179 samples and 11233 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 241 most variable miRs. Consensus average linkage hierarchical clustering of 149 samples and 241 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 241 most variable miRs. Consensus NMF clustering of 149 samples and 241 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 hierarchical
View Report | We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 149 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.
Clustering of miRseq precursor expression: consensus NMF
View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 149 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.

Other Correlation Analyses

Correlation between copy number variation genes (focal events) and molecular subtypes
View Report | Testing the association between copy number variation 27 focal events and 6 molecular subtypes across 159 patients, 29 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 67 arm-level events and 6 molecular subtypes across 159 patients, 21 significant findings detected with P value < 0.05 and Q value < 0.25.

Methods & Data

Input

Summary Report Date = Sat May 10 11:33:26 2014
Protection = FALSE

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