Inference of gene regulatory networks using regression based network method

1. Inference of gene regulatory networks using regression based network method 2005. 08. 11 Ha Seong, Kim Bioinformatics & Biostatistics Lab., SNU

2. Table of contents • Introduction • Gene regulatory networks • Gene regulatory network inference methods • Drawbacks of previous network methods • Linear modeling of genetic network • Method • Data pre-processing • Regression based network • Result • Caulobacter Crecentus • Mouse stem cell (hanyang univ.) • Discussion • Advantages • Weakness • Discussion

3. INTRODUCTION

4. Objective Introduce a new method to construct the gene regulatory networks using multiple regression method.

5. Gene regulatory networks Promoter sequence analysis Transcription factor analysis Gene expression level analysis

6. Gene regulatory network inference methods • Boolean networks • Kauffman • Somogyi • Akutsu • Shmulevich • Beysian networks • Friedman • Miyano • Imoto • Linear model • D’Haeseleer • Van Someren • Genetic network • Neural network

7. Drawbacks of previous network methods • Boolean networks • Data binarization cause loss of information • Beysian networks • Heavy computing time • Can not find self-regulated genes • Linear model • Dimensionality problem • Inherent linearity

8. Linear modeling of genetic networks • D’Haeseleer • Van Someren

9. METHOD

10. Data pre-processing • Discrete Cosine Transform algorithm • ex. Select 553 genes (cell-cycle) from total 1500 genes (T. Laub, 2000) • Matlab • Clustering gene expression profiles with self-organizing maps (SOMs) • ex. Identify groups of 553 genes with similar expression patterns. (T. Laub, 2000) • Known transcription factor (published papers)

11. Regression-based network I Regression models for G4 Gene expression data Time complexity Beta = 0 test Select significant beta. Cf.

12. Regression-based network II • Calculate the effect between genes using regression coefficient • Determine the directions and positive, negative effects G3 G5 G3 G5 Predict variables G4 G4 Response variable Do not significant beta3 Significant the beta3 • SEM (Structural Equation Model) • Path analysis

13. Regression-based network III Representation of the interaction term in gene regulatory network structure G5 G3 G3 G5 ? G4 G4 Do not significant interaction Significant the interaction

14. Interpretation of the interaction term HIS의 mRNA양이 Hybrid 1과 Hybrid 2에 의해서 조절. Hybrid 1의 수준에 따라서 Hybrid 2의 효과가 달라짐.

15. RESULT

16. Caulobacter CrecentusMouse stem cell (hanyang univ.)

17. DISCUSSION

18. Advantage • The method directly utilize the continuous gene expression data. • No loss of information • Interaction term could improve the network accuracy. • Obtain the effect between genes as a quantitative value • Apply various statistical approach to the method. • Low time complexity • We could be apply this method to large scale data set

19. Weakness • Treatment of models with high adjusted R-square • Clustering • If we use more higher indegree value, we have to consider the multicolinearity.

20. Future works • Select several models and apply probabilistic approach to this method. • Promoter sequence analysis • Mouse stem cell data