Download presentation

Presentation is loading. Please wait.

Published byDiane Borders Modified over 3 years ago

1
DREAM4 Puzzle – inferring network structure from microarray data Qiong Cheng

2
Outline Gene Network Gene Regulatory Systems and Related Work FunGen: Reconstructing Biological Networks Using Conditional Correlation Analysis ARACNE: Algorithm for Reconstructing Accurate Cellular Network

3
Gene Network Directed network –nodes : genes –edges : regulation –including loops –Scale-free: Degree distribution: –power law P(k) ~ k -λ

4
Genetic Network Generation Schematic Jong Modeling and simulation of genetic regulatory systems: a literature review. J. Comput Biol 2002;9(1):67-103

5
Random Network Model ER model –each pair of nodes connected by an edge with probability p –Independence of the edges –poisson degree distribution (e.g. P(k) ~ e -k for k) BA model –Scale-free distribution ( P(k) ~ k -x ) –Process: new nodes prefer attached to already high degree nodes http://arxiv.org/pdf/cond-mat/0010278

6
Random Network Model Module extraction from source random scale- free network (used by DREAM3) –Hierarchical scale-free network –Extraction: Random seed node + iteratively adding neighbor nodes with highest modularity Q Marbach D, Schaffter T, Mattiussi C, and Floreano D (2009) Generating Realistic in silico Gene Networks for Performance Assessment of Reverse Engineering Methods. J Comput Biol, 16(2):229–239

7
Microarray Data Distributions Benford’s law ( in base 10): P(D)=log 10 (1+D -1 ) Zipf’s law: microarray data log-normal distribution as a potential distribution for normalization of the bulk of the corrected spot intensities Noise Source: “Make Sense Of Microarray Data Distributions”

8
Reverse Engineering Clustering + … Correlation measures + … Optimization method –Bayesian network (conditional independence via DAG) –Markov chains –Dynamic Bayesian network –Expectation maximization (max likelihood) –GA –Neuron network Simulation –Piecewise-linear differential equations –Stochastic equations –Stochastic/hybrid petri-net –Boolean network Regression techniques

9
FunGen : Reconstructing Biological Networks Using Conditional Correlation Analysis Synthetic network Network dynamics Simulation protocol - perturbation Conditional correlation –Correlation is symetric –Matrix is non-symetric –May lead to indirect connection False positive ( indirect connection ) + false negative ( noise ) – error = FP/(FP+TN) + FN/(FN+TP) Reduce false positive –Choose optimal ρ_opt –Triangle reduction construction

10
ARACNE: Algorithm for Reconstructing Accurate Cellular Network Assume two-way interaction: pairwise potential determines all statistical dependencies + uniform marginal distributions Mutual information (MI) = measure of relatedness Independency Data processing inequality: if genes g 1 and g 3 interact through g 2 then ARACNE starts with network so for every edge look at gene triplets and remove edge with smallest MI Ignore the direction of the edges Reconstruct tree-network topologies exactly –higher-order potential interactions will not be accounted for (ARACNE’s algorithm will open 3-gene loops). –A two-gene interaction will be detected iff there are no alternate paths.

11
ARACNE – Example & Evaluation Synthetic networks: ER, BA Performance to be assessed via Precision-Recall curves (PRCs) Example:

12
(Demo) Sample input data file Input_file_name.exp N = 3 # genes M = 2 # microarrays Input file has N+1=4 lines each lines has M+2 (2M+2) fields AffyIDHG_U95Av2SudHL6.CHPST486.CHP G1G116.4773670.6993936320.150969 0.5297595 G2G27.69892740.5593536526.04019 0.5445875 G3G38.80989550.544587521.554955 0.31372303 header line annotation name Microarray chip names (value,p-value)-chip1 Source from ARACNE slides

13
(Demo, cont’d) Sample output data file input_data_file_name[non-default_param_vals].adj # lines = N = # genes G1:080.064729 G2:120.029864370.0521425 G3:210.0298643 G4:380.0427217 G5:450.403516 G6:540.40351660.582265 G7:650.58226590.38039 G8:710.052142580.743262 G9:800.06472930.0427217 70.74326290.333104 G10:960.38039 80.333104 AffyIDID# Associated gene ID# MI value 9 14 810 7 23 6 5 Source from ARACNE slides

Similar presentations

Presentation is loading. Please wait....

OK

Bayesian Belief Propagation

Bayesian Belief Propagation

© 2018 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google

Ppt on automatic car parking system Ppt on network theory Download ppt on indus valley civilization images Ppt on data handling for class 8 cbse Ppt on vertical axis wind turbine Ppt on panel discussion invitation Ppt on frames in html Ppt on solar energy class 9 Ppt on diode circuits tutorial Ppt on business process management