Meta-analytic framework for liquid association.
BACKGROUND
Although coexpression analysis via pair-wise expression correlation is popularly used to elucidate gene-gene interactions at the whole-genome scale, many complicated multi-gene regulations require more advanced detection methods. Liquid association (LA) is a powerful tool to detect the dynamic correlation of two gene variables depending on the expression level of a third variable (LA scouting gene). LA detection from single transcriptomic study, however, is often unstable and not generalizable due to cohort bias, biological variation and limited sample size. With the rapid development of microarray and NGS technology, LA analysis combining multiple gene expression studies can provide more accurate and stable results.


RESULTS
In this article, we proposed two meta-analytic approaches for LA analysis (MetaLA and MetaMLA) to combine multiple transcriptomic studies. To compensate demanding computing, we also proposed a two-step fast screening algorithm for more efficient genome-wide screening: bootstrap filtering and sign filtering. We applied the methods to five Saccharomyces cerevisiae datasets related to environmental changes. The fast screening algorithm reduced 98% of running time. When compared with single study analysis, MetaLA and MetaMLA provided stronger detection signal and more consistent and stable results. The top triplets are highly enriched in fundamental biological processes related to environmental changes. Our method can help biologists understand underlying regulatory mechanisms under different environmental exposure or disease states.


METHODS
A MetaLA R package, data and code for this article are available at http://tsenglab.biostat.pitt.edu/software.htm.


BACKGROUND
ctseng@pitt.edu.


BACKGROUND
Supplementary data are available at Bioinformatics online.
School of Statistics, Capital University of Economics and Business, Fengtai, Beijing, China.
Lin
L
Wang
Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Silvia
S
Liu
Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Ying
Y
Ding
Institute of Statistical Science, Academia Sinica, Nankang, Taipei, Taiwan.
Shin-Sheng
SS
Yuan
Department of Statistics, College of Arts and Sciences, University of South Carolina, Columbia, SC, USA.
Yen-Yi
YY
Ho
Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
George C
GC
Tseng
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D004843:Epistasis, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D012984:Software; D059467:Transcriptome