1 / 13

DREAM4 Puzzle – inferring network structure from microarray data

DREAM4 Puzzle – inferring network structure from microarray data. Qiong Cheng. Outline. Gene Network Gene Regulatory Systems and Related Work FunGen: Reconstructing Biological Networks Using Conditional Correlation Analysis ARACNE: Algorithm for Reconstructing Accurate Cellular Network.

lel
Download Presentation

DREAM4 Puzzle – inferring network structure from microarray data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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)=log10(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 g1 and g3 interact through g2 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 • Example: • Synthetic networks: ER , BA • Performance to be assessed via Precision-Recall curves (PRCs)

  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 AffyID HG_U95Av2 SudHL6.CHP ST486.CHP G1 G1 16.477367 0.69939363 20.150969 0.5297595 G2 G2 7.6989274 0.55935365 26.04019 0.5445875 G3 G3 8.8098955 0.5445875 21.554955 0.31372303 Microarray chip names annotation name header line (value,p-value)-chip1 Source from ARACNE slides

  13. (Demo, cont’d) Sample output data file 5 AffyID ID# MI value Associated gene ID# input_data_file_name[non-default_param_vals].adj # lines = N = # genes G1:0 8 0.064729 G2:1 2 0.0298643 7 0.0521425 G3:2 1 0.0298643 G4:3 8 0.0427217 G5:4 5 0.403516 G6:5 4 0.403516 6 0.582265 G7:6 5 0.582265 9 0.38039 G8:7 1 0.0521425 8 0.743262 G9:8 0 0.064729 3 0.0427217 7 0.743262 9 0.333104 G10:9 6 0.38039 8 0.333104 4 1 6 9 7 8 10 2 3 Source from ARACNE slides

More Related