HotNet pathway analysis of mutation and copy number data
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
23 May 2013 | analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C1QC01JZ
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/C1QC01JZ
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 8 significant subnetworks identified in HotNet analysis.

Results
Significant subnetworks

HotNet identifies 8 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 8 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
--- | --- | --- | ---
BAIAP2(74),DIAPH1(9),SHANK1(10) | 3 | 0.0145 | 1
CLTB(1),CLTC(74),CLINT1(1) | 3 | 0.0145 | 1
PCDHA6(9),RELN(66),PCDHA4(10) | 3 | 0.0145 | 1
SNX9(14),CLINT1(1),GGA3(75),CLTC(74),CLTB(1),AP1G2(26) | 6 | 0.0100 | 1
BUB1(4),AP1G2(26),CLINT1(1),GGA3(75),CLTC(74),CLTB(1),SNX9(14) | 7 | 0.0000 | 1
MPO(74),THBS1(2),PLG(15),KLK3(10),HRG(33),CP(34),MEP1A(12) | 7 | 0.0000 | 1
AP2M1(34),MCF2L2(34),ATM(13),MEGF10(10),BAHD1(1),TGOLN2(3),TP53BP1(1),USP28(14),C17orf28(74),AARS2(12) | 10 | 0.0020 | 1
PLCB2(2),SLC9A3(7),SLC9A3R1(74),ARHGEF12(14),RDX(12),SLC34A1(1),SLC22A5(10),NF2(29),P2RY1(34),GNA13(74) | 10 | 0.0325 | 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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

- 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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