This is an overview of Uterine Carcinosarcoma analysis pipelines from FireCloud run "17 October 2017".
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 FireCloud 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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Analysis of mutagenesis by APOBEC cytidine deaminases (P-MACD).
View Report | There are 57 tumor samples in this analysis. The Benjamini-Hochberg-corrected p-value for enrichment of the APOBEC mutation signature in 2 samples is <=0.05. Out of these, 2 have enrichment values >2, which implies that in such samples at least 50% of APOBEC signature mutations have been in fact made by APOBEC enzyme(s). -
Mutation Assessor
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Mutation Signature Analysis
View Report | Our analysis idenfied 1 solution(s) of mutational signatures across 57 samples by BayesNMF method. -
SNP6 Copy number analysis (GISTIC2)
View Report | There were 56 tumor samples used in this analysis: 30 significant arm-level results, 27 significant focal amplifications, and 30 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 10 different clustering approaches and 5 clinical features across 57 patients, no significant finding detected with P value < 0.05 and Q value < 0.25. -
Correlation between APOBEC signature variables and clinical features
View Report | Testing the association between 3 variables and 5 clinical features across 57 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one variables. -
Correlation between copy number variation genes (focal events) and selected clinical features
View Report | Testing the association between copy number variation 58 focal events and 5 clinical features across 56 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 79 arm-level events and 5 clinical features across 56 patients, no significant finding detected with Q value < 0.25. -
Correlation between gene mutation status and selected clinical features
View Report | Testing the association between mutation status of 12 genes and 5 clinical features across 57 patients, no significant finding detected with Q value < 0.25. -
Correlation between mRNAseq expression and clinical features
View Report | Testing the association between 18939 genes and 5 clinical features across 56 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 1 clinical feature related to at least one genes. -
Correlation between mutation rate and clinical features
View Report | Testing the association between 2 variables and 6 clinical features across 57 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one variables. -
Clustering Analyses
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Clustering of copy number data by focal peak region with absolute value: consensus NMF
View Report | The most robust consensus NMF clustering of 56 samples using the 58 most variable genes was identified for k = 3 clusters. We computed the clustering for k = 2 to k = 10 and used the cophenetic correlation coefficient and the average silhouette width calculation to determine the robust clusters. -
Clustering of copy number data by peak region with threshold value: consensus NMF
View Report | The most robust consensus NMF clustering of 56 samples using the 58 most variable genes was identified for k = 6 clusters. We computed the clustering for k = 2 to k = 10 and used the cophenetic correlation coefficient and the average silhouette width calculation to determine the robust clusters. -
Clustering of lincRNA expression: consensus hierarchical
View Report | Median absolute deviation (MAD) was used to select 2500 most variable lincRNAs. Consensus ward linkage hierarchical clustering of 55 samples and 2500 lincRNAs identified 3 subtypes with the stability of the clustering increasing for k = 2 to k = 10. -
Clustering of lincRNA expression: consensus NMF
View Report | The most robust consensus NMF clustering of 56 samples using the 2500 most variable lincRNAs was identified for k = 3 clusters. We computed the clustering for k = 2 to k = 10 and used the cophenetic correlation coefficient and the average silhouette width calculation to determine the robust clusters. -
Clustering of miR mature expression: consensus hierarchical
View Report | Median absolute deviation (MAD) was used to select 331 most variable miRs. Consensus ward linkage hierarchical clustering of 56 samples and 331 miRs identified 4 subtypes with the stability of the clustering increasing for k = 2 to k = 10. -
Clustering of miR mature expression: consensus NMF
View Report | The most robust consensus NMF clustering of 57 samples using the 331 most variable miRs was identified for k = 6 clusters. We computed the clustering for k = 2 to k = 10 and used the cophenetic correlation coefficient and the average silhouette width calculation to determine the robust clusters. -
Clustering of protein coding gene expression: consensus NMF
View Report | The most robust consensus NMF clustering of 56 samples using the 2500 most variable genes was identified for k = 3 clusters. We computed the clustering for k = 2 to k = 10 and used the cophenetic correlation coefficient and the average silhouette width calculation to determine the robust clusters. -
Clustering of Protein-coding gene expression: consensus hierarchical
View Report | Median absolute deviation (MAD) was used to select 2500 most variable genes. Consensus ward linkage hierarchical clustering of 55 samples and 2500 genes identified 5 subtypes with the stability of the clustering increasing for k = 2 to k = 10. -
Other Analyses
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Identification of putative miR direct targets by sequencing data
View Report | The CLR algorithm was applied on 845 miRs and 18939 mRNAs across 56 samples. After 2 filtering steps, the number of 18 miR:gene pairs were detected. -
Methylation__HM450_Clustering_CNMF
View Report | The most robust consensus NMF clustering of 57 samples using the 12415 most variable genes was identified for k = 4 clusters. We computed the clustering for k = 2 to k = 10 and used the cophenetic correlation coefficient and the average silhouette width calculation to determine the robust clusters. -
Methylation__HM450_Clustering_Consensus_Plus
View Report | Median absolute deviation (MAD) was used to select 2500 most variable genes. Consensus ward linkage hierarchical clustering of 56 samples and 2500 genes identified 4 subtypes with the stability of the clustering increasing for k = 2 to k = 10. -
Mutation_MutSig2CV
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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 58 focal events and 10 molecular subtypes across 56 patients, 71 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 79 arm-level events and 10 molecular subtypes across 56 patients, 9 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 12 genes and 10 molecular subtypes across 57 patients, 12 significant findings detected with P value < 0.05 and Q value < 0.25.
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Summary Report Date = Thu Dec 14 14:17:44 2017
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Protection = FALSE