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The data were then transferred to SPSS and analyzed by means of descriptive statistics, statistical comparisons, correlation analyses and multivariate analyses.
There are two important criteria for the comparisons: correlation and Bland-Altman plot.
For these comparisons, correlation p-values were calculated using Pearson correlation between gene expression and quantitative TG values.
To limit spurious findings due to multiple comparisons, correlation analyses of PSG variables were limited to those measures significantly different between groups.
The correlation between the patient rating of SMF severity (PR-SMFRS) and the CR-SMFRS was slightly lower than for the aforementioned comparisons (correlation coefficient 0.28).
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In the Libby samples, comparisons (correlations) between ANA levels and disease and exposure rankings were made using the nonparametric Spearman rank correlation.
For within-quadrant, within-hemisphere and mirror symmetric (across vertical meridian) within-quadrant, between-hemisphere comparisons, correlations steadily decreased at larger angular distances.
Positive slopes and correlation coefficients are obtained in all comparisons; correlations are higher when fitting only to genes with real-time RT-PCR log2 fold-changes bigger than 0.5, becoming 0.6 when comparing only to Sato's study.
For within-quadrant + within-hemisphere and mirror-symmetric, within-quadrant + between-hemisphere comparisons, correlations steadily decreased at larger angular distances suggesting an effect of polar angle distance which likely reflects overlapping response field and homotopic connections, respectively.
Such a representation facilitates automated software recognition of findings and observations, intrapatient comparison, correlation to norms and outcomes research.
For all species, the comparison correlation coefficient was of r ≥ 0.99 for the training subset and of r ≥ 0.96 for the test subset.
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