Analysis Overview for Acute Myeloid Leukemia
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
Overview
Introduction

This is the analysis overview for Firehose run "25 July 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

- Mutation Analysis (MutSig)
  View Report | Significantly mutated genes (q ≤ 0.1): 68

- Clustering Analyses

- Clustering of Methylation: consensus NMF
  View Report | The 10461 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 192 samples and 10461 genes identified 7 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 179 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 179 samples and 1500 genes identified 2 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 187 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 187 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 molecular cancer subtypes and selected clinical features
  View Report | Testing the association between subtypes identified by 5 different clustering approaches and 3 clinical features across 197 patients, 5 significant findings detected with P value < 0.05.

- Correlation between gene mutation status and selected clinical features
  View Report | Testing the association between mutation status of 47 genes and 3 clinical features across 199 patients, 4 significant findings detected with Q value < 0.25.

Methods & Data
Input

- Run Prefix = analyses__2012_07_25

- Summary Report Date = Fri Aug 17 15:12:05 2012

- Protection = FALSE

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