title: Transcriptome data analysis for cell culture processes.
abstract: In the past decade, DNA microarrays have fundamentally changed the way we study complex biological systems. By measuring the expression levels of thousands of transcripts, the paradigm of studying organisms has shifted from focusing on the local phenomena of a few genes to surveying the whole genome. DNA microarrays are used in a variety of ways, from simple comparisons between two samples to more intricate time-series studies. With the large number of genes being studied, the dimensionality of the problem is inevitably high. The analysis of microarray data thus requires specific approaches. In the case of time-series microarray studies, data analysis is further complicated by the correlation between successive time points in a series.In this review, we survey the methodologies used in the analysis of static and time-series microarray data, covering data pre-processing, identification of differentially expressed genes, profile pattern recognition, pathway analysis, and network reconstruction. When available, examples of their use in mammalian cell cultures are presented.
authors.forename: Marlene
authors.initials: M
authors.lastname: Castro-Melchor
authors.forename: Huong
authors.initials: H
authors.lastname: Le
authors.forename: Wei-Shou
authors.initials: WS
authors.lastname: Hu
mesh_terms: D000818:Animals; D018929:Cell Culture Techniques / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D013223:Statistics as Topic / Q000379:methods*; D059467:Transcriptome / Q000235:genetics*