Analysis Overview - PANCANCER cohort with 12 disease types (Primary solid tumor)

Analysis Overview

PANCANCER cohort with 12 disease types (Primary solid tumor)

23 May 2013 | analyses__2013_05_23

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Analysis Overview for PANCANCER cohort with 12 disease types (Primary solid tumor cohort) - 23 May 2013. Broad Institute of MIT and Harvard. doi:10.7908/C1SX6B8H

Overview

Introduction

This is an overview of PANCANCER cohort with 12 disease types analysis pipelines from Firehose run "23 May 2013".

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

Correlations to Clinical Parameters

Correlation between miR expression and clinical features
View Report | Testing the association between 817 miRs and 7 clinical features across 560 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one miRs.
Correlation between mRNA expression and clinical features
View Report | Testing the association between 17814 genes and 15 clinical features across 1593 samples, statistically thresholded by Q value < 0.05, 12 clinical features related to at least one genes.

Clustering Analyses

Clustering of miR expression: consensus hierarchical
View Report | We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 568 samples and 150 miRs identified 6 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 miR expression: consensus NMF
View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 568 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 mature expression: consensus hierarchical
View Report | We filtered the data to 160 most variable miRs. Consensus average linkage hierarchical clustering of 3015 samples and 160 miRs identified 6 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 mRNA expression: consensus hierarchical
View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 1601 samples and 1500 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.

Other Analyses

Identification of putative miR direct targets
View Report | This pipeline use a relevance network approach to infer putative miR:mRNA regulatory connections. All miR:mRNA pairs that have correlations < -0.3 and have predicted interactions in three sequence prediction databases (Miranda, Pictar, Targetscan) define the final network.

Methods & Data

Input

Summary Report Date = Wed Jun 26 18:09:59 2013
Protection = FALSE

Made with Nozzle