PMID- 30184058
OWN - NLM
STAT- MEDLINE
DCOM- 20191016
LR  - 20220818
IS  - 1367-4811 (Electronic)
IS  - 1367-4803 (Print)
IS  - 1367-4803 (Linking)
VI  - 34
IP  - 22
DP  - 2018 Nov 15
TI  - Bayesian integrative model for multi-omics data with missingness.
PG  - 3801-3808
LID - 10.1093/bioinformatics/bty775 [doi]
AB  - MOTIVATION: Integrative analysis of multi-omics data from different 
      high-throughput experimental platforms provides valuable insight into regulatory 
      mechanisms associated with complex diseases, and gains statistical power to 
      detect markers that are otherwise overlooked by single-platform omics analysis. 
      In practice, a significant portion of samples may not be measured completely due 
      to insufficient tissues or restricted budget (e.g. gene expression profile are 
      measured but not methylation). Current multi-omics integrative methods require 
      complete data. A common practice is to ignore samples with any missing platform 
      and perform complete case analysis, which leads to substantial loss of 
      statistical power. METHODS: In this article, inspired by the popular Integrative 
      Bayesian Analysis of Genomics data (iBAG), we propose a full Bayesian model that 
      allows incorporation of samples with missing omics data. RESULTS: Simulation 
      results show improvement of the new full Bayesian approach in terms of outcome 
      prediction accuracy and feature selection performance when sample size is limited 
      and proportion of missingness is large. When sample size is large or the 
      proportion of missingness is low, incorporating samples with missingness may 
      introduce extra inference uncertainty and generate worse prediction and feature 
      selection performance. To determine whether and how to incorporate samples with 
      missingness, we propose a self-learning cross-validation (CV) decision scheme. 
      Simulations and a real application on child asthma dataset demonstrate superior 
      performance of the CV decision scheme when various types of missing mechanisms 
      are evaluated. AVAILABILITY AND IMPLEMENTATION: Freely available on the GitHub at 
      https://github.com/CHPGenetics/FBM. SUPPLEMENTARY INFORMATION: Supplementary data 
      are available at Bioinformatics online.
FAU - Fang, Zhou
AU  - Fang Z
AD  - Department of Biostatistics, University of Pittsburgh, Pittsburgh, USA.
FAU - Ma, Tianzhou
AU  - Ma T
AD  - Department of Epidemiology and Biostatistics, University of Maryland, College 
      Park, MD, USA.
FAU - Tang, Gong
AU  - Tang G
AD  - Department of Biostatistics, University of Pittsburgh, Pittsburgh, USA.
FAU - Zhu, Li
AU  - Zhu L
AD  - Department of Biostatistics, University of Pittsburgh, Pittsburgh, USA.
FAU - Yan, Qi
AU  - Yan Q
AD  - Division of Pediatric Pulmonology, Allergy and Immunology, Children's Hospital of 
      Pittsburgh of UPMC, Pittsburgh, USA.
FAU - Wang, Ting
AU  - Wang T
AD  - Division of Pediatric Pulmonology, Allergy and Immunology, Children's Hospital of 
      Pittsburgh of UPMC, Pittsburgh, USA.
FAU - Celedon, Juan C
AU  - Celedon JC
AD  - Division of Pediatric Pulmonology, Allergy and Immunology, Children's Hospital of 
      Pittsburgh of UPMC, Pittsburgh, USA.
FAU - Chen, Wei
AU  - Chen W
AD  - Department of Biostatistics, University of Pittsburgh, Pittsburgh, USA.
AD  - Division of Pediatric Pulmonology, Allergy and Immunology, Children's Hospital of 
      Pittsburgh of UPMC, Pittsburgh, USA.
FAU - Tseng, George C
AU  - Tseng GC
AD  - Department of Biostatistics, University of Pittsburgh, Pittsburgh, USA.
LA  - eng
GR  - R01 HL117191/HL/NHLBI NIH HHS/United States
GR  - R01 MD011764/MD/NIMHD NIH HHS/United States
GR  - R01 CA190766/CA/NCI NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PL  - England
TA  - Bioinformatics
JT  - Bioinformatics (Oxford, England)
JID - 9808944
SB  - IM
MH  - *Bayes Theorem
MH  - *Genomics
MH  - Humans
MH  - Research Design
MH  - Sample Size
MH  - *Transcriptome
PMC - PMC6223369
EDAT- 2018/09/06 06:00
MHDA- 2019/10/17 06:00
PMCR- 2019/11/15
CRDT- 2018/09/06 06:00
PHST- 2018/01/15 00:00 [received]
PHST- 2018/08/31 00:00 [accepted]
PHST- 2018/09/06 06:00 [pubmed]
PHST- 2019/10/17 06:00 [medline]
PHST- 2018/09/06 06:00 [entrez]
PHST- 2019/11/15 00:00 [pmc-release]
AID - 5089231 [pii]
AID - bty775 [pii]
AID - 10.1093/bioinformatics/bty775 [doi]
PST - ppublish
SO  - Bioinformatics. 2018 Nov 15;34(22):3801-3808. doi: 10.1093/bioinformatics/bty775.