Introduction to Wireless Auditing - Welcome to Computer Science

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



                                     Outline
 •    What is a gene network
 •    What is a disruption network
 •    Building the network
 •    Observations
        Degree distribution
        Connectivity
 • Review
 • Conclusions
[ chocobospore.org ][ 10/14/2012 ]
                                        [ Building Gene Disruption Networks ]



             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



[ chocobospore.org ][ 10/14/2012 ]
                                                 [ Building Gene Disruption Networks ]



             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)


[ chocobospore.org ][ 10/14/2012 ]
                                     [ Building Gene Disruption Networks ]



     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

[ chocobospore.org ][ 10/14/2012 ]
                                                          [ Building Gene Disruption Networks ]



                  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


[ chocobospore.org ][ 10/14/2012 ]
                                     [ Building Gene Disruption Networks ]



       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

[ chocobospore.org ][ 10/14/2012 ]
                                                [ Building Gene Disruption Networks ]



            Matrix → Graph Example

                                                       up
                                                                        C
    Gene           A           B     C   A


       A           0           0     1
                                         down                         up
        B          -1          0     1
        C          0           0     0                      B




[ chocobospore.org ][ 10/14/2012 ]
                                         [ Building Gene Disruption Networks ]



                              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
[ chocobospore.org ][ 10/14/2012 ]
                                     [ Building Gene Disruption Networks ]



                    Degree Distribution




[ chocobospore.org ][ 10/14/2012 ]
                                        [ Building Gene Disruption Networks ]



             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.




 [ chocobospore.org ][ 10/14/2012 ]
                                          [ Building Gene Disruption Networks ]



                               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


[ chocobospore.org ][ 10/14/2012 ]
                                     [ Building Gene Disruption Networks ]



     Sample hub (γ=4, r=down, g=up)




[ chocobospore.org ][ 10/14/2012 ]
                                              [ Building Gene Disruption Networks ]



                                     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

[ chocobospore.org ][ 10/14/2012 ]
                                          [ Building Gene Disruption Networks ]



                                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

[ chocobospore.org ][ 10/14/2012 ]

				
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