An efficient concordant integrative analysis of multiple large-scale two-sample expression data sets.
BACKGROUND
We have proposed a mixture model based approach to the concordant integrative analysis of multiple large-scale two-sample expression datasets. Since the mixture model is based on the transformed differential expression test P-values (z-scores), it is generally applicable to the expression data generated by either microarray or RNA-seq platforms. The mixture model is simple with three normal distribution components for each dataset to represent down-regulation, up-regulation and no differential expression. However, when the number of datasets increases, the model parameter space increases exponentially due to the component combination from different datasets.


RESULTS
In this study, motivated by the well-known generalized estimating equations (GEEs) for longitudinal data analysis, we focus on the concordant components and assume that the proportions of non-concordant components follow a special structure. We discuss the exchangeable, multiset coefficient and autoregressive structures for model reduction, and their related expectation-maximization (EM) algorithms. Then, the parameter space is linear with the number of datasets. In our previous study, we have applied the general mixture model to three microarray datasets for lung cancer studies. We show that more gene sets (or pathways) can be detected by the reduced mixture model with the exchangeable structure. Furthermore, we show that more genes can also be detected by the reduced model. The Cancer Genome Atlas (TCGA) data have been increasingly collected. The advantage of incorporating the concordance feature has also been clearly demonstrated based on TCGA RNA sequencing data for studying two closely related types of cancer.


METHODS
Additional results are included in a supplemental file. Computer program R-functions are freely available at http://home.gwu.edu/∼ylai/research/Concordance.


BACKGROUND
ylai@gwu.edu.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Department of Statistics, The George Washington University, Washington, DC 20052, USA.
Yinglei
Y
Lai
Department of Statistics, The George Washington University, Washington, DC 20052, USA.
Fanni
F
Zhang
Department of Statistics, The George Washington University, Washington, DC 20052, USA.
Tapan K
TK
Nayak
Department of Statistics, The George Washington University, Washington, DC 20052, USA.
Reza
R
Modarres
Department of Pharmacology and Physiology.
Norman H
NH
Lee
Division of Genomic Medicine, Department of Medicine, The George Washington University Medical Center, Washington, DC 20037, USA.
Timothy A
TA
McCaffrey
D000465:Algorithms; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D056726:Genetic Association Studies; D015894:Genome, Human; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D017423:Sequence Analysis, RNA / Q000379:methods*