Analysis Overview
Thyroid Adenocarcinoma (Primary solid tumor)
16 April 2014 | analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1G73CDW
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 Thyroid Adenocarcinoma (Primary solid tumor cohort) - 16 April 2014. Broad Institute of MIT and Harvard. doi:10.7908/C1G73CDW
Overview
Introduction

This is an overview of Thyroid Adenocarcinoma 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

- CHASM 1.0.5 (Cancer-Specific High-throughput Annotation of Somatic Mutations)
  View Report | There are 4376 mutations identified by MuTect and 801 mutations with significant functional impact at BHFDR <= 0.25.

- LowPass Copy number analysis (GISTIC2)
  View Report | There were 97 tumor samples used in this analysis: 9 significant arm-level results, 0 significant focal amplifications, and 0 significant focal deletions were found.

- Mutation Analysis (MutSig v1.5)
  View Report |

- Mutation Analysis (MutSig v2.0 and MutSigCV v0.9 merged result)
  View Report |

- Mutation Analysis (MutSig v2.0)
  View Report |

- Mutation Analysis (MutSigCV v0.9)
  View Report |

- Mutation Assessor
  View Report |

- SNP6 Copy number analysis (GISTIC2)
  View Report | There were 494 tumor samples used in this analysis: 18 significant arm-level results, 1 significant focal amplifications, and 32 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 10 different clustering approaches and 15 clinical features across 486 patients, 56 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 30 focal events and 15 clinical features across 484 patients, 12 significant findings 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 57 arm-level events and 15 clinical features across 484 patients, 53 significant findings detected with Q value < 0.25.

- Correlation between gene methylation status and clinical features
  View Report | Testing the association between 19707 genes and 15 clinical features across 486 samples, statistically thresholded by Q value < 0.05, 12 clinical features related to at least one genes.

- Correlation between gene mutation status and selected clinical features
  View Report | Testing the association between mutation status of 6 genes and 15 clinical features across 394 patients, 8 significant findings detected with Q value < 0.25.

- Correlation between miRseq expression and clinical features
  View Report | Testing the association between 514 miRs and 15 clinical features across 485 samples, statistically thresholded by Q value < 0.05, 14 clinical features related to at least one miRs.

- Correlation between mRNAseq expression and clinical features
  View Report | Testing the association between 17990 genes and 15 clinical features across 484 samples, statistically thresholded by Q value < 0.05, 13 clinical features related to at least one genes.

- Correlation between RPPA expression and clinical features
  View Report | Testing the association between 175 genes and 15 clinical features across 219 samples, statistically thresholded by Q value < 0.05, 10 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 494 samples using the 35 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 494 samples using the 35 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 Methylation: consensus NMF
  View Report | The 5272 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 496 samples and 5272 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 255 most variable miRs. Consensus average linkage hierarchical clustering of 495 samples and 255 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 255 most variable miRs. Consensus NMF clustering of 495 samples and 255 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 495 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 495 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 494 samples and 1500 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 mRNAseq gene expression: consensus NMF
  View Report | The most robust consensus NMF clustering of 494 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 RPPA data: consensus hierarchical
  View Report | 175 proteins were selected. Consensus average linkage hierarchical clustering of 222 samples and 175 proteins 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 RPPA data: consensus NMF
  View Report | The most robust consensus NMF clustering of 222 samples using 175 proteins 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.

- Pathway Analyses

- HotNet pathway analysis of mutation and copy number data
  View Report

- 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 52 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 30 focal events and 10 molecular subtypes across 494 patients, 87 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 57 arm-level events and 10 molecular subtypes across 494 patients, 166 significant findings detected with P value < 0.05 and Q value < 0.25.

- Correlation between gene mutation status and molecular subtypes
  View Report | Testing the association between mutation status of 6 genes and 10 molecular subtypes across 401 patients, 28 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 = 494. Number of gene expression samples = 494. Number of methylation samples = 496.

- Correlations between copy number and mRNAseq expression
  View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 414, 1182, 1747, 2057, 2363, 2693, 3052, 3515.4, 4170.7, respectively.

Methods & Data
Input

- Summary Report Date = Sat May 10 12:05:15 2014

- Protection = FALSE

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