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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

RT-PCR results for 16 genes of the 4 ovarian cancer cell lines. F1, up- (↑) or down- (↓) regulated in CP70 + C30 + C200 compared to A2780. F2, up- (↑) or down- (↓) regulated in C30 + C200 compared to CP70.
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Figure 6: RT-PCR results for 16 genes of the 4 ovarian cancer cell lines. F1, up- (↑) or down- (↓) regulated in CP70 + C30 + C200 compared to A2780. F2, up- (↑) or down- (↓) regulated in C30 + C200 compared to CP70.

Mentions: Of the 199 genes selected from the first factor, the expression of 99 genes was up-regulated and the expression of 100 genes was down-regulated in CP70 + C30 + C200 compared to A2780. Of the 152 genes selected from the second factor, the expression of 24 genes was up-regulated and the expression of 128 genes was down-regulated in C30 + C200 compared to CP70. To validate the expression of genes selected from the biological factors, reverse transcription-polymerase chain reaction (RT-PCR) was performed for 16 genes with GAPDH as a control: COL3A1, ENO2, FGF18, JUN, LHX2, MEIS1, MEIS2, PBX3, PDGFRL, PRICKLE1, SAT, SHB, TIMP2, TLX1, TOP1 and UACA. Figure 6 demonstrates that the differential expression pattern of the 16 genes, as determined with RT-PCR, was comparable to the FA results of the microarray data, confirming the reliability of our analysis of the microarray data. Additionally, in Table 2 is shown that there is overlap between our gene lists and gene lists from other groups who have profiled A2780 and its cisplatin/oxaliplatin resistant subline(s), confirming our results [13-16]. Furthermore, FatiGO was used to annotate the genes with Gene Ontology (GO) terms (biological process and molecular function) and to compare the distribution of the main GO terms between the gene list selected from the first and second factor [17]. As shown in Table 3, the distributions of the main GO terms were not significantly different between the two groups of genes.


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)

RT-PCR results for 16 genes of the 4 ovarian cancer cell lines. F1, up- (↑) or down- (↓) regulated in CP70 + C30 + C200 compared to A2780. F2, up- (↑) or down- (↓) regulated in C30 + C200 compared to CP70.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: RT-PCR results for 16 genes of the 4 ovarian cancer cell lines. F1, up- (↑) or down- (↓) regulated in CP70 + C30 + C200 compared to A2780. F2, up- (↑) or down- (↓) regulated in C30 + C200 compared to CP70.
Mentions: Of the 199 genes selected from the first factor, the expression of 99 genes was up-regulated and the expression of 100 genes was down-regulated in CP70 + C30 + C200 compared to A2780. Of the 152 genes selected from the second factor, the expression of 24 genes was up-regulated and the expression of 128 genes was down-regulated in C30 + C200 compared to CP70. To validate the expression of genes selected from the biological factors, reverse transcription-polymerase chain reaction (RT-PCR) was performed for 16 genes with GAPDH as a control: COL3A1, ENO2, FGF18, JUN, LHX2, MEIS1, MEIS2, PBX3, PDGFRL, PRICKLE1, SAT, SHB, TIMP2, TLX1, TOP1 and UACA. Figure 6 demonstrates that the differential expression pattern of the 16 genes, as determined with RT-PCR, was comparable to the FA results of the microarray data, confirming the reliability of our analysis of the microarray data. Additionally, in Table 2 is shown that there is overlap between our gene lists and gene lists from other groups who have profiled A2780 and its cisplatin/oxaliplatin resistant subline(s), confirming our results [13-16]. Furthermore, FatiGO was used to annotate the genes with Gene Ontology (GO) terms (biological process and molecular function) and to compare the distribution of the main GO terms between the gene list selected from the first and second factor [17]. As shown in Table 3, the distributions of the main GO terms were not significantly different between the two groups of genes.

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