inferring gene regulatory networks from asynchronous microarray data david oviatt, dr. mark clement,...
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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](https://reader035.vdocuments.site/reader035/viewer/2022062714/56649d455503460f94a21ecd/html5/thumbnails/1.jpg)
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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Purpose:
Use microarray data to infer the gene
regulatory network of an organism
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Other Methods' Unreasonable Requirements
• High number of samples
• Time series data
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Problems:
• Scarcity of microarray data
• Large size of networks
• Noise
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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
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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
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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/
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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.
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Metrics
• Precision
• Recall
• F-score
• Accuracy
• Sensitivity
• Specificity
• MCC – Matthews Correlation Coefficient
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AIRnet Compared to Random
• 1000 random predictions created for each test case
• Mean score of each metric reported for each network size
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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:
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The End