Analysis Overview
Thymoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C15T3JSZ
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Analysis Overview for Thymoma (Primary solid tumor cohort) - 21 August 2015. Broad Institute of MIT and Harvard. doi:10.7908/C15T3JSZ
Overview
Introduction

This is an overview of Thymoma analysis pipelines from Firehose run "21 August 2015".

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 123 tumor samples used in this analysis: 19 significant arm-level results, 0 significant focal amplifications, and 12 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 8 clinical features across 124 patients, 29 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 12 focal events and 8 clinical features across 123 patients, 27 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 52 arm-level events and 8 clinical features across 123 patients, 61 significant findings detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 20084 genes and 8 clinical features across 124 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 5 clinical features related to at least one genes.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 625 miRs and 8 clinical features across 124 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 5 clinical features related to at least one miRs.

    • Correlation between mRNAseq expression and clinical features
      View Report | Testing the association between 18301 genes and 8 clinical features across 120 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one genes.

    • Correlation between RPPA expression and clinical features
      View Report | Testing the association between 192 genes and 8 clinical features across 90 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 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 123 samples using the 12 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 123 samples using the 12 copy number focal regions was identified for k = 8 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 7113 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 124 samples and 7113 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 miRseq mature expression: consensus hierarchical
      View Report | Median absolute deviation (MAD) was used to select 647 most variable miRs. Consensus ward linkage hierarchical clustering of 124 samples and 647 miRs identified 4 subtypes with the stability of the clustering increasing for k = 2 to k = 10.

    • Clustering of miRseq mature expression: consensus NMF
      View Report | We filtered the data to 647 most variable miRs. Consensus NMF clustering of 124 samples and 647 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 | Median absolute deviation (MAD) was used to select 156 most variable miRs. Consensus ward linkage hierarchical clustering of 124 samples and 156 miRs identified 6 subtypes with the stability of the clustering increasing for k = 2 to k = 10.

    • Clustering of miRseq precursor expression: consensus NMF
      View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 124 samples and 150 miRs identified 5 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 | Median absolute deviation (MAD) was used to select 1500 most variable genes. Consensus ward linkage hierarchical clustering of 120 samples and 1500 genes identified 4 subtypes with the stability of the clustering increasing for k = 2 to k = 10.

    • Clustering of mRNAseq gene expression: consensus NMF
      View Report | The most robust consensus NMF clustering of 120 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 | Median absolute deviation (MAD) was used to select 192 most variable proteins. Consensus ward linkage hierarchical clustering of 90 samples and 192 proteins identified 4 subtypes with the stability of the clustering increasing for k = 2 to k = 10.

    • Clustering of RPPA data: consensus NMF
      View Report | The most robust consensus NMF clustering of 90 samples using 192 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.

  • Other Analyses

    • Identification of putative miR direct targets by sequencing data
      View Report | The CLR algorithm was applied on 850 miRs and 18301 mRNAs across 120 samples. After 2 filtering steps, the number of 53 miR:genes pairs were detected.

  • Pathway Analyses

    • GSEA Class2: Canonical Pathways enriched in each subtypes of mRNAseq_cNMF in THYM-TP
      View Report | basic data info

    • PARADIGM pathway analysis of mRNASeq expression and copy number data
      View Report | There were 46 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression data
      View Report | There were 47 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 12 focal events and 10 molecular subtypes across 123 patients, 65 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 52 arm-level events and 10 molecular subtypes across 123 patients, 308 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 = 120. Number of gene expression samples = 120. Number of methylation samples = 124.

    • Correlations between copy number and mRNAseq expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 620, 1478.6, 2306, 2789.2, 3238, 3687, 4186, 4766.4, 5513, respectively.

Methods & Data
Input

  • Summary Report Date = Mon Nov 9 00:38:21 2015

  • Protection = FALSE

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