title: Exploring the feasibility of next-generation sequencing and microarray data meta-analysis.
abstract: Emerging next-generation sequencing (NGS) technology potentially resolves many issues that prevent widespread clinical use of gene expression microarrays. However, the number of publicly available NGS datasets is still smaller than that of microarrays. This paper explores the possibilities for combining information from both microarray and NGS gene expression datasets for the discovery of differentially expressed genes (DEGs). We evaluate several existing methods in detecting DEGs using individual datasets as well as combined NGS and microarray datasets. Results indicate that analysis of combined NGS and microarray data is feasible, but successful detection of DEGs may depend on careful selection of algorithms as well as on data normalization and pre-processing.
authors.affiliation: Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. pwu33@ gatech.edu
authors.forename: Po-Yen
authors.initials: PY
authors.lastname: Wu
authors.forename: John H
authors.initials: JH
authors.lastname: Phan
authors.forename: May D
authors.initials: MD
authors.lastname: Wang
mesh_terms: D005189:False Positive Reactions; D005240:Feasibility Studies; D020869:Gene Expression Profiling; D015201:Meta-Analysis as Topic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D013223:Statistics as Topic