2006
DOI: 10.1099/mic.0.28278-0
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Multivariate analysis of microarray data by principal component discriminant analysis: prioritizing relevant transcripts linked to the degradation of different carbohydrates in Pseudomonas putida S12

Abstract: The value of the multivariate data analysis tools principal component analysis (PCA) and principal component discriminant analysis (PCDA) for prioritizing leads generated by microarrays was evaluated. To this end, Pseudomonas putida S12 was grown in independent triplicate fermentations on four different carbon sources, i.e. fructose, glucose, gluconate and succinate. RNA isolated from these samples was analysed in duplicate on an anonymous clone-based array to avoid bias during data analysis. The relevant tran… Show more

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Cited by 31 publications
(24 citation statements)
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References 41 publications
(44 reference statements)
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“…Because the edd and eda gene products are the sine qua non for 6-phosphogluconate metabolism, the physical and genetic organization of the edd/glk and eda/zwf-1 genes explains why this pathway is operational in P. putida KT2440 (46).…”
Section: Discussionmentioning
confidence: 99%
“…Because the edd and eda gene products are the sine qua non for 6-phosphogluconate metabolism, the physical and genetic organization of the edd/glk and eda/zwf-1 genes explains why this pathway is operational in P. putida KT2440 (46).…”
Section: Discussionmentioning
confidence: 99%
“…Genes with a signal log 2 ratio of C1 or B-1 at least at one time point were classified as differentially expressed, and clustered using the self-organizing tree algorithm (SOTA) (Herrero et al 2001) and complete time-course profiles (0, 3, 6, 9, and 12 h). The number of groups was decided based on principal-component analysis (PCA; van der Werf et al 2006). Since the cumulative contribution ratio of the first three principal components was 97.5%, we subdivided the genes into three groups with different expression profiles.…”
Section: Cluster Analysismentioning
confidence: 99%
“…Differentially expressed genes representing the complete time course profile (S0, S60, S120, S150, and ZSP) were clustered using the K-means algorithm. To discover the number of groups to be considered, we applied principal-component analysis (PCA) (38). Since the differences between successive PCA components (eigenvalues) go rapidly to near zero after the eighth component, we subdivided the genes into eight groups with different expression patterns.…”
Section: Methodsmentioning
confidence: 99%