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The strength variation of the SAM is customarily quantified by the SAM Index, defined as the leading principal component of the 700 hPa atmospheric geopotential height anomalies in the Antarctic region (e.g., Mo 2000).

We call this approach to modeling Directed Component Analysis (DCA), differing from Principal Component Analysis in that the offset and directed vectors are chosen based on biological process intuition as opposed to the mean vector and leading principal component, respectively.

In this figure, GC* and GC*CpG are plotted as functions of the current GC content and the leading principal component, i.e., the direction of the maximum common variation, is fitted to the data (black solid line).

We used an additive logistic regression model, including as covariates sex and the five leading principal component axes (the appropriate number of axes to include was determined through heritability analysis, see below and Supplementary Fig. 1).

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We also used the leading principal components for multiband recognition.

Kirkpatrick and Meyer [ 15] suggested the direct estimation of the leading principal components (direct PC).

PCA is robust in capturing and presenting major variations of expression profiles on leading principal components.

These spectral analyses were applied to the leading principal components (PCs) of the data sets.

The classifier was optimized when eight leading principal components were used.

For the classification, the N leading principal components (which are linear combination of gene sets) were used.

An additional advantage of expressing the data in terms of the leading principal components is their robustness to noise.

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