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A biological question and a balanced (orthogonal) design: the ingredients to efficiently analyze two-color microarrays with Confirmatory Factor Analysis.

Crijns AP, Gerbens F, Plantinga AE, Meersma GJ, de Jong S, Hofstra RM, de Vries EG, van der Zee AG, de Bock GH, te Meerman GJ - BMC Genomics (2006)

Bottom Line: 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.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.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.

View Article: PubMed Central - HTML - PubMed

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

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.

Conclusion: 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.

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Related in: MedlinePlus

Correlations between the Cy5 and Cy3 data from each array, respectively, and the first factor retained with FA.
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Figure 2: Correlations between the Cy5 and Cy3 data from each array, respectively, and the first factor retained with FA.

Mentions: The first factors from the Cy5 and Cy3 data explained 85% of the total variation and represented variation common to all arrays. Figure 2 shows that the correlations between the Cy5 and Cy3 data of each array and the first factor were highly similar. This figure also indicates that the quality of the arrays was very comparable. By subtracting this common variation from the Cy5 and Cy3 data all gene specific variation that does not contribute to differences between arrays was eliminated (i.e. the Cy5 and Cy3 data were standardized by subtracting the first factor).


A biological question and a balanced (orthogonal) design: the ingredients to efficiently analyze two-color microarrays with Confirmatory Factor Analysis.

Crijns AP, Gerbens F, Plantinga AE, Meersma GJ, de Jong S, Hofstra RM, de Vries EG, van der Zee AG, de Bock GH, te Meerman GJ - BMC Genomics (2006)

Correlations between the Cy5 and Cy3 data from each array, respectively, and the first factor retained with FA.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1590029&req=5

Figure 2: Correlations between the Cy5 and Cy3 data from each array, respectively, and the first factor retained with FA.
Mentions: The first factors from the Cy5 and Cy3 data explained 85% of the total variation and represented variation common to all arrays. Figure 2 shows that the correlations between the Cy5 and Cy3 data of each array and the first factor were highly similar. This figure also indicates that the quality of the arrays was very comparable. By subtracting this common variation from the Cy5 and Cy3 data all gene specific variation that does not contribute to differences between arrays was eliminated (i.e. the Cy5 and Cy3 data were standardized by subtracting the first factor).

Bottom Line: 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.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.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.

View Article: PubMed Central - HTML - PubMed

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

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.

Conclusion: 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.

Show MeSH
Related in: MedlinePlus