title: A biological question and a balanced (orthogonal) design: the ingredients to efficiently analyze two-color microarrays with Confirmatory Factor Analysis.
abstract: BACKGROUND
  Factor analysis (FA) has been widely applied in microarray studies as a data-reduction-tool without any a-priori assumption regarding associations between observed data and latent structure (Exploratory Factor Analysis).A disadvantage is that the representation of data in a reduced set of dimensions can be difficult to interpret, as biological contrasts do not necessarily coincide with single dimensions. However, FA can also be applied as an instrument to confirm what is expected on the basis of pre-established hypotheses (Confirmatory Factor Analysis, CFA). We show that with a hypothesis incorporated in a balanced (orthogonal) design, including 'SelfSelf' hybridizations, dye swaps and independent replications, FA can be used to identify the latent factors underlying the correlation structure among the observed two-color microarray data. An orthogonal design will reflect the principal components associated with each experimental factor. We applied CFA to a microarray study performed to investigate cisplatin resistance in four ovarian cancer cell lines, which only differ in their degree of cisplatin resistance.
  
  
  RESULTS
  Two latent factors, coinciding with principal components, representing the differences in cisplatin resistance between the four ovarian cancer cell lines were easily identified. From these two factors 315 genes associated with cisplatin resistance were selected, 199 genes from the first factor (False Discovery Rate (FDR): 19%) and 152 (FDR: 24%) from the second factor, while both gene sets shared 36. The differential expression of 16 genes was validated with reverse transcription-polymerase chain reaction.
  
  
  CONCLUSIONS
  Our results show that FA is an efficient method to analyze two-color microarray data provided that there is a pre-defined hypothesis reflected in an orthogonal design.
authors.affiliation: Department of Gynecologic Oncology, University Medical Center Groningen and University of Groningen, PO-box 30.001, 9700 RB, Groningen, The Netherlands. a.p.g.crijns@med.umcg.nl
authors.forename: Anne P G
authors.initials: AP
authors.lastname: Crijns
authors.forename: Frans
authors.initials: F
authors.lastname: Gerbens
authors.forename: A Edo D
authors.initials: AE
authors.lastname: Plantinga
authors.forename: Gert Jan
authors.initials: GJ
authors.lastname: Meersma
authors.forename: Steven
authors.initials: S
authors.lastname: de Jong
authors.forename: Robert M W
authors.initials: RM
authors.lastname: Hofstra
authors.forename: Elisabeth G E
authors.initials: EG
authors.lastname: de Vries
authors.forename: Ate G J
authors.initials: AG
authors.lastname: van der Zee
authors.forename: Geertruida H
authors.initials: GH
authors.lastname: de Bock
authors.forename: Gerard J
authors.initials: GJ
authors.lastname: te Meerman
mesh_terms: D018906:Antineoplastic Agents, Alkylating / Q000494:pharmacology; D045744:Cell Line, Tumor / Q000187:drug effects; D002945:Cisplatin / Q000494:pharmacology; D004273:DNA, Neoplasm / Q000235:genetics; D019008:Drug Resistance, Neoplasm / Q000235:genetics; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D010051:Ovarian Neoplasms / Q000473:pathology; D025341:Principal Component Analysis; D012334:RNA, Neoplasm / Q000235:genetics; D012107:Research Design; D020133:Reverse Transcriptase Polymerase Chain Reaction
chemical_list: D018906:Antineoplastic Agents, Alkylating; D004273:DNA, Neoplasm; D012334:RNA, Neoplasm; D002945:Cisplatin