Predicting gene targets of perturbations via network-based filtering of mRNA expression compendia.
BACKGROUND
DNA microarrays are routinely applied to study diseased or drug-treated cell populations. A critical challenge is distinguishing the genes directly affected by these perturbations from the hundreds of genes that are indirectly affected. Here, we developed a sparse simultaneous equation model (SSEM) of mRNA expression data and applied Lasso regression to estimate the model parameters, thus constructing a network model of gene interaction effects. This inferred network model was then used to filter data from a given experimental condition of interest and predict the genes directly targeted by that perturbation.


RESULTS
Our proposed SSEM-Lasso method demonstrated substantial improvement in sensitivity compared with other tested methods for predicting the targets of perturbations in both simulated datasets and microarray compendia. In simulated data, for two different network types, and over a wide range of signal-to-noise ratios, our algorithm demonstrated a 167% increase in sensitivity on average for the top 100 ranked genes, compared with the next best method. Our method also performed well in identifying targets of genetic perturbations in microarray compendia, with up to a 24% improvement in sensitivity on average for the top 100 ranked genes. The overall performance of our network-filtering method shows promise for identifying the direct targets of genetic dysregulation in cancer and disease from expression profiles.


BACKGROUND
Microarray data are available at the Many Microbe Microarrays Database (M3D, http://m3d.bu.edu). Algorithm scripts are available at the Gardner Lab website (http://gardnerlab.bu.edu/SSEMLasso).
Cosgrove
Elissa J
EJ
Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.
Zhou
Yingchun
Y
Gardner
Timothy S
TS
Kolaczyk
Eric D
ED
D000465:Algorithms; D003198:Computer Simulation; D030562:Databases, Protein; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D020411:Oligonucleotide Array Sequence Analysis; D012333:RNA, Messenger / Q000378:metabolism*
D012333:RNA, Messenger
Nat Biotechnol. 2005 Mar;23(3):377-83
15765094
Mol Syst Biol. 2007;3:82
17299418
Nucleic Acids Res. 2008 Jan;36(Database issue):D866-70
17932051
Nucleic Acids Res. 2007 Jan;35(Database issue):D747-50
17132828
Nucleic Acids Res. 2003 Feb 15;31(4):e15
12582260
Nat Protoc. 2006;1(6):2551-4
17406508
Science. 2002 Aug 30;297(5586):1551-5
12202830
PLoS Biol. 2007 Jan;5(1):e8
17214507
Nucleic Acids Res. 2007 Jan;35(Database issue):D760-5
17099226
Cell. 2004 Jul 9;118(1):31-44
15242642
Biostatistics. 2007 Jan;8(1):2-8
16702229
Nat Biotechnol. 2004 May;22(5):615-21
15122300
Cell. 2007 Sep 7;130(5):797-810
17803904
Genetics. 2001 May;158(1):41-64
11333217
Nat Med. 1998 Nov;4(11):1293-301
9809554
Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50
16199517
Mol Syst Biol. 2007;3:91
17353933
BMC Bioinformatics. 2007 Feb 22;8:60
17316436
Nature. 2000 Oct 5;407(6804):651-4
11034217
Proc Natl Acad Sci U S A. 2003 Dec 23;100(26):15522-7
14673099
Nature. 2001 May 3;411(6833):41-2
11333967
Science. 1999 Oct 15;286(5439):531-7
10521349
Cell. 2000 Jul 7;102(1):109-26
10929718
Genome Biol. 2006;7(5):R36
16686963
Genome Res. 2005 May;15(5):724-36
15867433