HotNet pathway analysis of mutation and copy number data
Thyroid Adenocarcinoma (Primary solid tumor)
15 January 2014 | analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1P55KZG
Maintained by Hailei Zhang (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): HotNet pathway analysis of mutation and copy number data. Broad Institute of MIT and Harvard. doi:10.7908/C1P55KZG
Overview
Introduction

HotNet is an algrithom for finding altered subnetworks in a large protein-protein interaction network conatinning a significant number of mutations and copy number alterations (CNAs).

Summary

There were 7 significant subnetworks identified in HotNet analysis.

Results
Significant subnetworks

HotNet identifies 7 altered subnetworks based on the matched somatic mutation and copy number alterations data. Table 1 showes the top 10 significant subnetworks.

Table 1. Get Full Table Top 10 out of 7 subnetworks in ranked by p value. The last column of RepeatTimes shows how many times the subnetwork was picked up in differenct delta values.

Network | No.ofgenes | p-value | RepeatTimes
--- | --- | --- | ---
C19orf57(7),RUNX1T1(5),VCL(10) | 3 | 0.028 | 1
EPHA4(10),NGEF(10),ARHGEF15(5) | 3 | 0.028 | 1
PTEN(10),CTNND2(5),MAGI2(1) | 3 | 0.028 | 1
CUX1(2),SERPINB3(5),ARSA(68),TG(7) | 4 | 0.018 | 1
PHF23(5),RPL18A(6),MAPK8IP2(69),LRSAM1(1) | 4 | 0.018 | 1
PIAS2(5),CHAF1A(6),MBD1(4),GTF2IRD1(2) | 4 | 0.018 | 1
PLXNB2(68),SLC1A6(6),DLG5(12),ABCA1(1) | 4 | 0.018 | 1

Methods & Data
Input

Somatic mutation data from Mutsig pipeline, copy number alterations derived from GISTIC pipeline and influence matrix derived from Human Protein Reference Database (HPRD) provided by HotNet website .

HotNet Method

HotNet is an algorithm for de novo identification of significantly altered subnetworks.First, it formulates an influence measure between pairs of genes in the network using a diffusion process defined on the graph. Second, it identifies subnetworks using either a combinatorial model or an enhanced influence model. Finally, it derives a two-stage multiple hypothesis test that mitigates the testing of a large number of hypotheses in subnetwork discovery .

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] HotNet
[2] F. Vandin, E. Upfal, and B.J. Raphael., Algorithms for Detecting Significantly Mutated Pathways in Cancer, Journal of Computational Biology 18(3):507-22 (2011)
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