Use of Bayesian networks to probabilistically model and improve the likelihood of validation of microarray findings by RT-PCR.
Though genome-wide technologies, such as microarrays, are widely used, data from these methods are considered noisy; there is still varied success in downstream biological validation. We report a method that increases the likelihood of successfully validating microarray findings using real time RT-PCR, including genes at low expression levels and with small differences. We use a Bayesian network to identify the most relevant sources of noise based on the successes and failures in validation for an initial set of selected genes, and then improve our subsequent selection of genes for validation based on eliminating these sources of noise. The network displays the significant sources of noise in an experiment, and scores the likelihood of validation for every gene. We show how the method can significantly increase validation success rates. In conclusion, in this study, we have successfully added a new automated step to determine the contributory sources of noise that determine successful or unsuccessful downstream biological validation.
English
Sangeeta B
SB
Stanford Center for Biomedical Informatics Research (BMIR), Stanford University School of Medicine, 251 Campus Drive, Stanford, CA 94305, USA. english1@stanford.edu
Shih
Shou-Ching
SC
Ramoni
Marco F
MF
Smith
Lois E
LE
Butte
Atul J
AJ
D000704:Analysis of Variance; D000818:Animals; D001499:Bayes Theorem; D003930:Diabetic Retinopathy / Q000235:genetics / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods*; D023281:Genomics; D018496:Hyperoxia / Q000235:genetics / Q000378:metabolism; D051379:Mice; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D012333:RNA, Messenger / Q000032:analysis; D015203:Reproducibility of Results; D020133:Reverse Transcriptase Polymerase Chain Reaction
D012333:RNA, Messenger
Nat Genet. 2004 Oct;36(10):1090-8
15448693
Bioinformatics. 2003 Nov 1;19(16):2031-8
14594707
BMC Bioinformatics. 2003 Jun 25;4:27
12823866
Nat Genet. 2003 Jun;34(2):166-76
12740579
Nucleic Acids Res. 2002 Feb 15;30(4):e15
11842121
BMC Neurosci. 2002 Oct 25;3:16
12401135
BMC Genomics. 2004 Sep 02;5:61
15345031
Physiol Genomics. 2004 Aug 11;18(3):308-15
15161964
J Biol Chem. 2001 Jun 8;276(23):19937-44
11259426
Nucleic Acids Res. 2003 Aug 15;31(16):e96
12907748
Nucleic Acids Res. 2003 Oct 1;31(19):5676-84
14500831
Bioinformatics. 2003 Nov 22;19(17):2271-82
14630656
J Comput Biol. 2000;7(3-4):601-20
11108481
Nature. 2002 May 2;417(6884):78-83
11967526
BMC Genomics. 2003 Jul 29;4(1):31
12885301
Invest Ophthalmol Vis Sci. 1994 Jan;35(1):101-11
7507904
Am J Pathol. 2002 Jul;161(1):35-41
12107087
Biosystems. 2004 Jul;75(1-3):57-65
15245804
Proc Natl Acad Sci U S A. 2001 Apr 10;98(8):4746-51
11296301
Bioinformatics. 2002 Mar;18(3):405-12
11934739
J Comput Biol. 2002;9(4):671-83
12323100
Proc Natl Acad Sci U S A. 2000 Jan 4;97(1):262-7
10618406
Bioinformatics. 2001 Jun;17(6):509-19
11395427
Cell. 2002 May 3;109(3):307-20
12015981
Proc Natl Acad Sci U S A. 2001 Apr 24;98(9):5116-21
11309499