FN ISI Export Format
VR 1.0
FN ISI Export Format
VR 1.0
PT J
AU Conlon, EM
   Postier, BL
   Methe, BA
   Nevin, KP
   Lovley, DR
AF Conlon, E. M.
   Postier, B. L.
   Methe, B. A.
   Nevin, K. P.
   Lovley, D. R.
TI Hierarchical Bayesian meta-analysis models for cross-platform
   microarray studies
SO JOURNAL OF APPLIED STATISTICS
LA English
DT Article
DE Bayesian statistics; meta-analysis; microarray data; multiple platform;
   Markov chain Monte Carlo; deviance information criterion
ID GENE-EXPRESSION PROFILES; FALSE DISCOVERY RATE; MOLECULAR
   CLASSIFICATION; GEOBACTER-SULFURREDUCENS; PROSTATE-CANCER; MIXTURE
   MODEL; MARKER GENES; LUNG-CANCER; REPOSITORY; REDUCTION
AB The development of new technologies to measure gene expression has been
   calling for statistical methods to integrate findings across
   multiple-platform studies. A common goal of microarray analysis is to
   identify genes with differential expression between two conditions,
   such as treatment versus control. Here, we introduce a hierarchical
   Bayesian meta-analysis model to pool gene expression studies from
   different microarray platforms: spotted DNA arrays and short
   oligonucleotide arrays. The studies have different array design
   layouts, each with multiple sources of data replication, including
   repeated experiments, slides and probes. Our model produces the
   gene-specific posterior probability of differential expression, which
   is the basis for inference. In simulations combining two and five
   independent studies, our meta-analysis model outperformed separate
   analyses for three commonly used comparison measures; it also showed
   improved receiver operating characteristic curves. When combining
   spotted DNA and CombiMatrix short oligonucleotide array studies of
   Geobacter sulfurreducens, our meta-analysis model discovered more genes
   for fixed thresholds of posterior probability of differential
   expression and Bayesian false discovery than individual study analyses.
   We also examine an alternative model and compare models using the
   deviance information criterion.
C1 [Conlon, E. M.] Univ Massachusetts, Dept Math & Stat, Amherst, MA 01003 USA.
   [Postier, B. L.; Methe, B. A.; Nevin, K. P.; Lovley, D. R.] Univ Massachusetts, Dept Microbiol, Amherst, MA 01003 USA.
RP Conlon, EM, Univ Massachusetts, Dept Math & Stat, Amherst, MA 01003 USA.
EM econlon@mathstat.umass.edu
FU Office of Science (BER), US Department of Energy [DE-FC02-02ER63446]
FX This research was supported by the Office of Science (BER), US
   Department of Energy, Grant No. DE-FC02-02ER63446. We thank three
   anonymous referees for their thoughtful comments which enhanced the
   manuscript.
NR 57
TC 0
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXFORDSHIRE, ENGLAND
SN 0266-4763
J9 J APPL STAT
JI J. Appl. Stat.
PY 2009
VL 36
IS 10
BP 1067
EP 1085
DI 10.1080/02664760802562480
PG 19
SC Statistics & Probability
GA 498QC
UT ISI:000270156000002
ER

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