Analysis Overview for Cervical Squamous Cell Carcinoma
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Overview
Introduction

This is the analysis overview for Firehose run "21 December 2012".

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

- Copy number analysis (GISTIC2)
  View Report | There were 102 tumor samples used in this analysis: 23 significant arm-level results, 24 significant focal amplifications, and 27 significant focal deletions were found.

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

- Mutation Analysis (MutSig vS2N)
  View Report |

- Clustering Analyses

- Clustering of copy number data: consensus NMF
  View Report | The most robust consensus NMF clustering of 102 samples using the 51 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 Methylation: consensus NMF
  View Report | The 9125 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 122 samples and 9125 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 mRNAseq gene expression: consensus NMF
  View Report | The most robust consensus NMF clustering of 97 samples using the 1500 most variable genes 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 mRNAseq gene expression: consensus hierarchical
  View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 97 samples and 1500 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 expression: consensus NMF
  View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 122 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 expression: consensus hierarchical
  View Report | We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 122 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.

- Correlation Analyses

- Correlation between copy number variations of arm-level result and selected clinical features
  View Report | Testing the association between copy number variation 35 arm-level results and 6 clinical features across 26 patients, one significant finding detected with Q value < 0.25.

- Correlation between copy number variation genes and selected clinical features
  View Report | Testing the association between copy number variation of 46 peak regions and 6 clinical features across 26 patients, 2 significant findings detected with Q value < 0.25.

- Correlation between gene methylation status and clinical features
  View Report | Testing the association between 17313 genes and 4 clinical features across 25 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.

- Correlation between molecular cancer subtypes and selected clinical features
  View Report | Testing the association between subtypes identified by 6 different clustering approaches and 6 clinical features across 32 patients, one significant finding detected with P value < 0.05.

- Correlation between gene mutation status and selected clinical features
  View Report | Testing the association between 'NFE2L2 MUTATION ANALYSIS' and 4 clinical features across 12 patients, no significant finding detected with Q value < 0.25.

- Correlation between mRNAseq expression and clinical features
  View Report | Testing the association between 18242 genes and 5 clinical features across 28 samples, statistically thresholded by Q value < 0.05, no clinical feature related to at least one genes.

- Correlation between miRseq expression and clinical features
  View Report | Testing the association between 564 genes and 6 clinical features across 32 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.

- Correlations between copy number and mRNAseq expression
  View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 1138, 1906, 2528, 3102, 3729, 4369, 5063, 5807, 6633, respectively.

- Correlation between mRNA expression and DNA methylation
  View Report | The top 25 correlated methylation probes per gene are displayed. Total number of matched samples = 97. Number of gene expression samples = 97. Number of methylation samples = 97.

- Other Analyses

- PARADIGM pathway analysis of mRNASeq expression data
  View Report | There were 51 significant pathways identified in this analysis.

- PARADIGM pathway analysis of mRNASeq expression and copy number data
  View Report | There were 39 significant pathways identified in this analysis.

Methods & Data
Input

- Run Prefix = analyses__2012_12_21

- Summary Report Date = Fri Jan 25 11:19:44 2013

- Protection = FALSE

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