This is an overview of Uterine Corpus Endometrioid Carcinoma 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.
-
Sequence and Copy Number Analyses
-
SNP6 Copy number analysis (GISTIC2)
View Report | There were 540 tumor samples used in this analysis: 41 significant arm-level results, 47 significant focal amplifications, and 44 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 7 clinical features across 547 patients, 39 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 91 focal events and 7 clinical features across 539 patients, 275 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 82 arm-level events and 7 clinical features across 539 patients, 156 significant findings detected with Q value < 0.25. -
Correlation between mRNAseq expression and clinical features
View Report | Testing the association between 18919 genes and 7 clinical features across 543 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 6 clinical features related to at least one genes. -
Clustering Analyses
-
Clustering of copy number data by focal peak region with absolute value: consensus NMF
View Report | The most robust consensus NMF clustering of 539 samples using the 91 most variable genes was identified for k = 5 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 539 samples using the 91 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 lincRNA expression: consensus hierarchical
View Report | Median absolute deviation (MAD) was used to select 2500 most variable lincRNAs. Consensus ward linkage hierarchical clustering of 542 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 543 samples using the 2500 most variable lincRNAs 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. -
Clustering of miR mature expression: consensus hierarchical
View Report | Median absolute deviation (MAD) was used to select 303 most variable miRs. Consensus ward linkage hierarchical clustering of 537 samples and 303 miRs identified 3 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 538 samples using the 303 most variable miRs was identified for k = 5 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 543 samples using the 2500 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. -
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 542 samples and 2500 genes identified 5 subtypes with the stability of the clustering increasing for k = 2 to k = 10. -
Other Analyses
-
Identification of putative miR direct targets by sequencing data
View Report | The CLR algorithm was applied on 808 miRs and 18919 mRNAs across 534 samples. After 2 filtering steps, the number of 77 miR:gene pairs were detected. -
Methylation__HM27_Clustering_CNMF
View Report | The most robust consensus NMF clustering of 117 samples using the 2327 most variable genes was identified for k = 5 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_CNMF
View Report | The most robust consensus NMF clustering of 430 samples using the 12369 most variable genes was identified for k = 5 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 429 samples and 2500 genes identified 3 subtypes with the stability of the clustering increasing for k = 2 to k = 10. -
Other Correlation Analyses
-
Correlation between copy number variation genes (focal events) and molecular subtypes
View Report | Testing the association between copy number variation 91 focal events and 10 molecular subtypes across 539 patients, 900 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 82 arm-level events and 10 molecular subtypes across 539 patients, 699 significant findings detected with P value < 0.05 and Q value < 0.25.
-
Summary Report Date = Thu Dec 14 14:17:06 2017
-
Protection = FALSE