This is an overview of Adrenocortical Carcinoma analysis pipelines from Firehose run "15 July 2014".
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.
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Sequence and Copy Number Analyses
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Mutation Analysis (MutSig 2CV v3.1)
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Mutation Analysis (MutSig v1.5)
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Mutation Analysis (MutSig v2.0)
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Mutation Analysis (MutSigCV v0.9)
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Mutation Assessor
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SNP6 Copy number analysis (GISTIC2)
View Report | There were 90 tumor samples used in this analysis: 22 significant arm-level results, 19 significant focal amplifications, and 26 significant focal deletions were found. -
Correlations to Clinical Parameters
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Correlation between aggregated molecular cancer subtypes and selected clinical features
View Report | Testing the association between subtypes identified by 8 different clustering approaches and 7 clinical features across 82 patients, 12 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 45 focal events and 7 clinical features across 80 patients, one 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 75 arm-level events and 7 clinical features across 80 patients, one significant finding detected with Q value < 0.25. -
Correlation between gene methylation status and clinical features
View Report | Testing the association between 19519 genes and 7 clinical features across 78 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 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 125 genes and 7 clinical features across 56 patients, 2 significant findings detected with Q value < 0.25. -
Correlation between miRseq expression and clinical features
View Report | Testing the association between 518 miRs and 7 clinical features across 78 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 17733 genes and 7 clinical features across 77 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one genes. -
Correlation between mutation rate and clinical features
View Report | Testing the association between 2 variables and 8 clinical features across 81 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one variables. -
Clustering Analyses
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Clustering of copy number data by focal peak region with log2 ratio: consensus NMF
View Report | The most robust consensus NMF clustering of 90 samples using the 45 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 copy number data by peak region with threshold value: consensus NMF
View Report | The most robust consensus NMF clustering of 90 samples using the 45 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 15062 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 80 samples and 15062 genes 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 miRseq mature expression: consensus hierarchical
View Report | Median absolute deviation (MAD) was used to select 238 most variable miRs. Consensus ward linkage hierarchical clustering of 80 samples and 238 miRs identified 6 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 238 most variable miRs. Consensus NMF clustering of 80 samples and 238 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 129 most variable miRs. Consensus ward linkage hierarchical clustering of 80 samples and 129 miRs identified 5 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 80 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 | Median absolute deviation (MAD) was used to select 1500 most variable genes. Consensus ward linkage hierarchical clustering of 79 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 79 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. -
Pathway Analyses
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PARADIGM pathway analysis of mRNASeq expression and copy number data
View Report | There were 67 significant pathways identified in this analysis. -
PARADIGM pathway analysis of mRNASeq expression data
View Report | There were 50 significant pathways identified in this analysis. -
Other Correlation Analyses
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Correlation between copy number variation genes (focal events) and molecular subtypes
View Report | Testing the association between copy number variation 45 focal events and 8 molecular subtypes across 90 patients, 31 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 80 arm-level events and 8 molecular subtypes across 90 patients, 54 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 142 genes and 8 molecular subtypes across 64 patients, no significant finding 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 = 79. Number of gene expression samples = 79. Number of methylation samples = 80. -
Correlations between copy number and mRNAseq expression
View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 1036, 2087.8, 2812, 3439, 4042, 4653, 5277.8, 5922.2, 6687, respectively.
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Summary Report Date = Tue Sep 16 17:16:45 2014
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Protection = FALSE