inferring gene regulatory networks from asynchronous microarray data david oviatt, dr. mark clement,...

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Inferring Gene Regulatory Networks from Asynchronous Microarray Data David Oviatt, Dr. Mark Clement, Dr. Quinn Snell, Kenneth Sundberg Department of Computer Science, Brigham Young University, Provo, UT Jared Allen, Dr. Randall Roper Department of Biology, Indiana University-Purdue University Indianapolis, Indianapolis, IN

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Page 1: Inferring Gene Regulatory Networks from Asynchronous Microarray Data David Oviatt, Dr. Mark Clement, Dr. Quinn Snell, Kenneth Sundberg Department of Computer

Inferring Gene Regulatory Networks from Asynchronous

Microarray Data

David Oviatt, Dr. Mark Clement, Dr. Quinn Snell, Kenneth Sundberg

Department of Computer Science, Brigham Young University, Provo, UT

Jared Allen, Dr. Randall Roper

Department of Biology, Indiana University-Purdue University Indianapolis, Indianapolis, IN

Page 2: Inferring Gene Regulatory Networks from Asynchronous Microarray Data David Oviatt, Dr. Mark Clement, Dr. Quinn Snell, Kenneth Sundberg Department of Computer

Purpose:

Use microarray data to infer the gene

regulatory network of an organism

Page 3: Inferring Gene Regulatory Networks from Asynchronous Microarray Data David Oviatt, Dr. Mark Clement, Dr. Quinn Snell, Kenneth Sundberg Department of Computer

Other Methods' Unreasonable Requirements

• High number of samples

• Time series data

Page 4: Inferring Gene Regulatory Networks from Asynchronous Microarray Data David Oviatt, Dr. Mark Clement, Dr. Quinn Snell, Kenneth Sundberg Department of Computer

Problems:

• Scarcity of microarray data

• Large size of networks

• Noise

Page 5: Inferring Gene Regulatory Networks from Asynchronous Microarray Data David Oviatt, Dr. Mark Clement, Dr. Quinn Snell, Kenneth Sundberg Department of Computer

AIRnet: Asynchronous Inference of

Regulatory networks

Classify gene levels using k-means clustering

Compute influence vectors (i.v.)

Convert i.v.'s into a sorted list of edges

Use Kruskal's algorithm to find the minimum-cost

spanning tree

Page 6: Inferring Gene Regulatory Networks from Asynchronous Microarray Data David Oviatt, Dr. Mark Clement, Dr. Quinn Snell, Kenneth Sundberg Department of Computer

Influence Vectors

Perform pairwise-

comparisons of change in

gene levels between

samples, adding or

subtracting from i.v.

Divide i.v. by the total

number of comparisons

Page 7: Inferring Gene Regulatory Networks from Asynchronous Microarray Data David Oviatt, Dr. Mark Clement, Dr. Quinn Snell, Kenneth Sundberg Department of Computer

In-silico Data

• DREAM3 competition - http://wiki.c2b2.columbia.edu/dream/index.php/The_DREAM3_Challenges

• Laboratory of Intelligent Systems: Thomas Schaffter and Daniel Marbach - GeneNet Weaver - http://lis.epfl.ch/

Page 8: Inferring Gene Regulatory Networks from Asynchronous Microarray Data David Oviatt, Dr. Mark Clement, Dr. Quinn Snell, Kenneth Sundberg Department of Computer

Clockwise from top left: simulated E.coli 1 network; E.coli 1 inferred correlations above 50%; simulated E.coli 2 network; E.coli 2 inferred correlations above 50%;

inferred networks made using 2 bins for each gene.

Page 9: Inferring Gene Regulatory Networks from Asynchronous Microarray Data David Oviatt, Dr. Mark Clement, Dr. Quinn Snell, Kenneth Sundberg Department of Computer

Metrics

• Precision

• Recall

• F-score

• Accuracy

• Sensitivity

• Specificity

• MCC – Matthews Correlation Coefficient

Page 10: Inferring Gene Regulatory Networks from Asynchronous Microarray Data David Oviatt, Dr. Mark Clement, Dr. Quinn Snell, Kenneth Sundberg Department of Computer

AIRnet Compared to Random

• 1000 random predictions created for each test case

• Mean score of each metric reported for each network size

Page 11: Inferring Gene Regulatory Networks from Asynchronous Microarray Data David Oviatt, Dr. Mark Clement, Dr. Quinn Snell, Kenneth Sundberg Department of Computer

Factor by which AIRnet outperforms random networks

Size 10 Size 50 Size 100 Average

Precision: 1.335 7.198 9.327 5.953

Recall: 0.322 5.968 11.667 5.986

F-score: 0.848 7.292 12.303 6.814

Accuracy: 0.401 0.085 0.034 0.173

Sensitivity: 0.322 5.968 11.667 5.986

Specificity: 0.454 0.051 0.016 0.174

MCC: 0.490 0.531 0.433 0.485

Score Summaries:

Page 12: Inferring Gene Regulatory Networks from Asynchronous Microarray Data David Oviatt, Dr. Mark Clement, Dr. Quinn Snell, Kenneth Sundberg Department of Computer

The End