Building and Analyzing Genome-Wide Gene Disruption Networks

1. Building and Analyzing Genome-Wide Gene Disruption Networks J. Rung, T. Schlitt, et al. (2002) Presented by Sean Whalen, 2/26/03

2. Outline • What is a gene network • What is a disruption network • Building the network • Observations • Degree distribution • Connectivity • Review • Conclusions

3. What is a gene network? • Directed Acyclic Graph (DAG) • Nodes/Vertices=Objects, Edges/Arcs=Relationships • Arbitrary meaning is assigned, in order to visualize relationships in a system (and acquire knowledge) • Gene networks simply model genetic relationships

4. More on Gene Networks • How to represent the network? Arbitrary. • Example: Edge between nodes means parent codes for transcription factor • Example: Edge between nodes means change in expression level of parent affects level of child • Different modeling methods • Bayesian, Dynamic Bayesian • Problem: only deals with small data sets • This paper’s method: simple, genome-wide analysis, demonstrated biologically meaningful (yeast)

5. What is a disruption network? • Gene network built from expression data (mutant strain vs. control) • Nodes are genes, edges indicated a causal change in expression level • Represented as a matrix • A discretized matrix is built from this matrix, to infer connectivity properties • Disruption network=graph representation of discretized matrix

6. Building the Network • Expression data matrix • rij = log( lij / cij ) • rij = jth element of ith row • l = exp. level in mutant • c = exp. level in control • Discretized matrix • Expression level up, down, or unchanged • Normalize rij, adjust for gene-specific standard deviation • Select cutoff level γ [2..4] • Expression matrix → Normalize → Select Cutoff → Discrete Matrix

7. Building the Network (cont.) • Disruption network γ' is representation of discretized network as a graph • Edge between gi and gj if dij ≠ 0 • Label edge as down regulating if dij=-1, up regulating if dij=1. Nodes labeled w/gene names • Expression data from all genes in a yeast mutant (single gene deletion) taken over 300 experiments w/63 control experiments

8. up C A up down B Matrix → Graph Example

9. Observations • High out degree = influence many other genes • High in degree = complex regulation • Distribution of total degree follows power law (scale-free topology) • 50% of genes show change in expression with single deletion • Few genes with high in AND out degree • Strongy connected subnets (hubs) are evolutionally more conserved

10. Degree Distribution

11. Out Degree vs. In Degree The point? Rare for node to have high ranked in degree AND out degree. Only 1 node’s in degree is in the top 50% of in degrees, AND out degree is in top 50% of out degrees.

12. Connectivity • How connected is the graph with different γ values? • γ<3, one big component • Remove top 1%, 5%, and 10% of highest degree genes • For 3<γ<3.6, biggest component still order of magnitude higher

13. Sample hub (γ=4, r=down, g=up)

14. Review • A disruption networks is a graph representation of a discretized expression matrix, with a degree cutoff γ • Allows genome-wide analysis • Power-law distribution of edges • High out degree=gene encodes regulatory proteins • High in degree=gene involved in metabolism

15. Conclusions • Disruption networks suggest scale free topology in gene regulatory networks • Dominated by single large component (hub) • Looking for subnets containing genes involved in a process allowed prediction of genes with similar functions • DNs offer a different perspective of expression data than tradition methods such as heirarchical clustering