HotNet pathway analysis of mutation and copy number data
Brain Lower Grade Glioma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
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/C1XW4H95
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
BCAN(28),ADAMTS4(27),SERPINA3(70),MST1(3) 4 0.041 1
KCNJ2(21),ADRB2(40),HTR4(40),AKAP5(70) 4 0.041 1
MET(99),ARHGEF12(49),PLXNB1(1),MLL(52) 4 0.041 1
RERE(114),TRIP6(98),FBLN1(48),ECM1(28) 4 0.041 1
SGCD(40),ADRA1B(40),KCND2(97),FLNC(99) 4 0.041 1
CDH9(32),CDH18(32),CDH12(34),CDH10(32),CDH6(31) 5 0.000 1
HDAC4(43),CYFIP2(40),FXR1(15),REV3L(51),ATRX(140),LUC7L2(97) 6 0.000 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.

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)