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BMC Genomics

, 7:232

First Online: 12 September 2006Received: 26 April 2006Accepted: 12 September 2006


BackgroundFactor 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.

ResultsTwo 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.

ConclusionOur 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.

AbbreviationsFAFactor Analysis

EFAExploratory Factor Analysis

CFAConfirmatory Factor Analysis

FDRFalse Discovery Rate

SVDSingular Value Decomposition

ULSUnweighted linear Least Squares

RT-PCRreverse transcription-polymerase chain reaction

GOGene Ontology

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2164-7-232 contains supplementary material, which is available to authorized users.

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Fuente: https://link.springer.com/article/10.1186/1471-2164-7-232

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