PMID- 23142963
OWN - NLM
STAT- MEDLINE
DCOM- 20130805
LR  - 20211021
IS  - 1367-4811 (Electronic)
IS  - 1367-4803 (Print)
IS  - 1367-4803 (Linking)
VI  - 29
IP  - 2
DP  - 2013 Jan 15
TI  - iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics 
      data.
PG  - 149-59
LID - 10.1093/bioinformatics/bts655 [doi]
AB  - MOTIVATION: Analyzing data from multi-platform genomics experiments combined with 
      patients' clinical outcomes helps us understand the complex biological processes 
      that characterize a disease, as well as how these processes relate to the 
      development of the disease. Current data integration approaches are limited in 
      that they do not consider the fundamental biological relationships that exist 
      among the data obtained from different platforms. Statistical Model: We propose 
      an integrative Bayesian analysis of genomics data (iBAG) framework for 
      identifying important genes/biomarkers that are associated with clinical outcome. 
      This framework uses hierarchical modeling to combine the data obtained from 
      multiple platforms into one model. RESULTS: We assess the performance of our 
      methods using several synthetic and real examples. Simulations show our 
      integrative methods to have higher power to detect disease-related genes than 
      non-integrative methods. Using the Cancer Genome Atlas glioblastoma dataset, we 
      apply the iBAG model to integrate gene expression and methylation data to study 
      their associations with patient survival. Our proposed method discovers multiple 
      methylation-regulated genes that are related to patient survival, most of which 
      have important biological functions in other diseases but have not been 
      previously studied in glioblastoma. AVAILABILITY: 
      http://odin.mdacc.tmc.edu/ approximately vbaladan/. CONTACT: veera@mdanderson.org SUPPLEMENTARY 
      INFORMATION: Supplementary data are available at Bioinformatics online.
FAU - Wang, Wenting
AU  - Wang W
AD  - Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, 
      Houston, TX 77030, USA.
FAU - Baladandayuthapani, Veerabhadran
AU  - Baladandayuthapani V
FAU - Morris, Jeffrey S
AU  - Morris JS
FAU - Broom, Bradley M
AU  - Broom BM
FAU - Manyam, Ganiraju
AU  - Manyam G
FAU - Do, Kim-Anh
AU  - Do KA
LA  - eng
GR  - P30 CA016672/CA/NCI NIH HHS/United States
GR  - P50 CA140388/CA/NCI NIH HHS/United States
GR  - R01 CA160736/CA/NCI NIH HHS/United States
GR  - P50 CA14038802/CA/NCI NIH HHS/United States
GR  - P50 CA116199/CA/NCI NIH HHS/United States
GR  - R01 CA107304/CA/NCI NIH HHS/United States
GR  - P50 CA127001/CA/NCI NIH HHS/United States
GR  - P50 CA127001 03/CA/NCI NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20121109
PL  - England
TA  - Bioinformatics
JT  - Bioinformatics (Oxford, England)
JID - 9808944
SB  - IM
MH  - Bayes Theorem
MH  - Brain Neoplasms/*genetics/metabolism/*mortality
MH  - DNA Methylation
MH  - Gene Expression Profiling
MH  - Genomics/methods
MH  - Glioblastoma/*genetics/metabolism/*mortality
MH  - Humans
MH  - *Models, Statistical
PMC - PMC3546799
EDAT- 2012/11/13 06:00
MHDA- 2013/08/06 06:00
PMCR- 2012/11/09
CRDT- 2012/11/13 06:00
PHST- 2012/11/13 06:00 [entrez]
PHST- 2012/11/13 06:00 [pubmed]
PHST- 2013/08/06 06:00 [medline]
PHST- 2012/11/09 00:00 [pmc-release]
AID - bts655 [pii]
AID - 10.1093/bioinformatics/bts655 [doi]
PST - ppublish
SO  - Bioinformatics. 2013 Jan 15;29(2):149-59. doi: 10.1093/bioinformatics/bts655. 
      Epub 2012 Nov 9.