Inferring Gene Regulatory Networks from Asynchronous Microarray Data David Oviatt, Dr. Mark Clement,...

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

Purpose:

Use microarray data to infer the gene

regulatory network of an organism

Other Methods' Unreasonable Requirements

• High number of samples

• Time series data

Problems:

• Scarcity of microarray data

• Large size of networks

• Noise

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

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

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/

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.

Metrics

• Precision

• Recall

• F-score

• Accuracy

• Sensitivity

• Specificity

• MCC – Matthews Correlation Coefficient

AIRnet Compared to Random

• 1000 random predictions created for each test case

• Mean score of each metric reported for each network size

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:

The End