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The effects of temperature and GC content can be separated using multivariate statistical techniques, such as principal component analysis (Kreil and Ouzounis 2001; Saunders et al. 2003), correspondence analysis (Tekaia et al. 2002; Lobry and Chessel 2003; Tekaia and Yeramian 2006; Boussau et al. 2008; Puigbò et al. 2008), and other techniques (Naya et al. 2006; Zeldovich et al. 2007).
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We also demonstrated that two clinically very distinct subtypes of basal-like cancers, chemotherapy resistant and highly chemotherapy sensitive cancers, which cannot not be reliably separated using gene expression based multivariate prediction models [ 17, 18], show significant differences in transcriptional diversity (permutation test P < 10-5).
Using multivariate analysis, such factors could be separated from those which were attributable to inadequate treatment or chemotherapeutant failure.
Using HRMAS H NMR spectroscopy and multivariate statistics, invasive ductal cancer could be separated from lobular cancer [ 49].
In multivariate image analysis, a classifier is trained using features from cells known to be from the two classes to be separated.
Multivariate analysis using partial least squares-discriminant analysis (PLS-DA) was performed to examine whether PolyI C groups could be separated from control groups on the basis of the full proteome profile [65], [74].
The team faultline concept is operationalized using multivariate cluster analysis analysts use faultline measures to identify subgroups/clusters in a team and to quantify how subgroups/clusters are separated.
They can be reduced by using multivariate statistics.
The test data were analyzed using multivariate variance statistical analysis.
Independent relations were assessed using multivariate regression analysis.
Factors associated with mortality were assessed using multivariate logistic model.
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