HotNet pathway analysis of mutation and copy number data
Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (Primary solid tumor)
16 April 2014 | analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1CN72H7
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/C1CN72H7
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 2 significant subnetworks identified in HotNet analysis.

Results
Significant subnetworks

HotNet identifies 2 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 2 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
--- | --- | --- | ---
CKM(7),MYOM2(18),CAPN3(8),TTN(22),TRIM54(9) | 5 | 0.001 | 1
LILRB2(7),HLA-B(14),HLA-F(13),B2M(8),KIR3DL1(7),MAGEA1(8) | 6 | 0.024 | 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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