PMID- 29949981
OWN - NLM
STAT- MEDLINE
DCOM- 20190827
LR  - 20240327
IS  - 1367-4811 (Electronic)
IS  - 1367-4803 (Print)
IS  - 1367-4803 (Linking)
VI  - 34
IP  - 13
DP  - 2018 Jul 1
TI  - Covariate-dependent negative binomial factor analysis of RNA sequencing data.
PG  - i61-i69
LID - 10.1093/bioinformatics/bty237 [doi]
AB  - MOTIVATION: High-throughput sequencing technologies, in particular RNA sequencing 
      (RNA-seq), have become the basic practice for genomic studies in biomedical 
      research. In addition to studying genes individually, for example, through 
      differential expression analysis, investigating co-ordinated expression 
      variations of genes may help reveal the underlying cellular mechanisms to derive 
      better understanding and more effective prognosis and intervention strategies. 
      Although there exists a variety of co-expression network based methods to analyze 
      microarray data for this purpose, instead of blindly extending these methods for 
      microarray data that may introduce unnecessary bias, it is crucial to develop 
      methods well adapted to RNA-seq data to identify the functional modules of genes 
      with similar expression patterns. RESULTS: We have developed a fully Bayesian 
      covariate-dependent negative binomial factor analysis (dNBFA) method-dNBFA-for 
      RNA-seq count data, to capture coordinated gene expression changes, while 
      considering effects from covariates reflecting different influencing factors. 
      Unlike existing co-expression network based methods, our proposed model does not 
      require multiple ad-hoc choices on data processing, transformation, as well as 
      co-expression measures and can be directly applied to RNA-seq data. Furthermore, 
      being capable of incorporating covariate information, the proposed method can 
      tackle setups with complex confounding factors in different experiment designs. 
      Finally, the natural model parameterization removes the need for a normalization 
      preprocessing step, as commonly adopted to compensate for the effect of 
      sequencing-depth variations. Efficient Bayesian inference of model parameters is 
      derived by exploiting conditional conjugacy via novel data augmentation 
      techniques. Experimental results on several real-world RNA-seq datasets on 
      complex diseases suggest dNBFA as a powerful tool for discovering the gene 
      modules with significant differential expression and meaningful biological 
      insight. AVAILABILITY AND IMPLEMENTATION: dNBFA is implemented in R language and 
      is available at https://github.com/siamakz/dNBFA.
FAU - Zamani Dadaneh, Siamak
AU  - Zamani Dadaneh S
AD  - Department of Electrical and Computer Engineering, TEES-AgriLife Center for 
      Bioinformatics and Genomic Systems Engineering, Texas A&M University, College 
      Station, TX, USA.
FAU - Zhou, Mingyuan
AU  - Zhou M
AD  - Department of Information, Risk, and Operations Management, The University of 
      Texas at Austin, Austin, TX, USA.
FAU - Qian, Xiaoning
AU  - Qian X
AD  - Department of Electrical and Computer Engineering, TEES-AgriLife Center for 
      Bioinformatics and Genomic Systems Engineering, Texas A&M University, College 
      Station, TX, USA.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PT  - Research Support, U.S. Gov't, Non-P.H.S.
PL  - England
TA  - Bioinformatics
JT  - Bioinformatics (Oxford, England)
JID - 9808944
SB  - IM
MH  - Autistic Disorder/genetics
MH  - Bayes Theorem
MH  - Factor Analysis, Statistical
MH  - Gene Expression Profiling/*methods
MH  - *Gene Regulatory Networks
MH  - High-Throughput Nucleotide Sequencing/*methods
MH  - Humans
MH  - Neoplasms/genetics
MH  - Sequence Analysis, RNA/*methods
MH  - *Software
PMC - PMC6022606
EDAT- 2018/06/29 06:00
MHDA- 2019/08/28 06:00
PMCR- 2018/06/27
CRDT- 2018/06/29 06:00
PHST- 2018/06/29 06:00 [entrez]
PHST- 2018/06/29 06:00 [pubmed]
PHST- 2019/08/28 06:00 [medline]
PHST- 2018/06/27 00:00 [pmc-release]
AID - 5045747 [pii]
AID - bty237 [pii]
AID - 10.1093/bioinformatics/bty237 [doi]
PST - ppublish
SO  - Bioinformatics. 2018 Jul 1;34(13):i61-i69. doi: 10.1093/bioinformatics/bty237.