HotNet pathway analysis of mutation and copy number data
Brain Lower Grade Glioma (Primary solid tumor)
23 September 2013 | analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C1HT2MNQ
Maintained by Hailei Zhang (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): HotNet pathway analysis of mutation and copy number data. Broad Institute of MIT and Harvard. doi:10.7908/C1HT2MNQ
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 117 significant subnetworks identified in HotNet analysis.

Results
Significant subnetworks

HotNet identifies 117 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 117 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
--- | --- | --- | ---
CDC23(27),CDC16(69),ANAPC7(26) | 3 | 0 | 4
EAF1(13),TFPT(116),TPM3(23) | 3 | 0 | 4
MYL2(26),CDC42BPA(14),PPP1R12C(116) | 3 | 0 | 4
NAV1(16),C12orf35(23),TBC1D4(67) | 3 | 0 | 4
TNNT1(116),TNNC1(13),TNNI1(14) | 3 | 0 | 4
EIF3A(71),EIF4G1(14),EIF4B(26) | 3 | 0 | 3
TUBA3C(68),GRM7(14),PFDN5(26) | 3 | 0 | 3
MED13L(27),MED23(36),MED13(14),ELF3(14) | 4 | 0 | 3
AKAP7(35),PRKAR1A(13),AKAP11(66) | 3 | 0 | 2
AP2M1(12),MED4(66),LAMP1(67) | 3 | 0 | 2

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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