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Accordingly, samples with intensity values higher than the cutoff intensity were considered to be positively stained.
To do this, we first determine a cutoff intensity value as the largest intensity value in all the background boundary segments.
Samples went to the left daughter node if their peak intensities were equal to or less than the cutoff intensity value; otherwise, the samples would go to the right daughter node.
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For both comparisons, the IgMFA sensitivity gradually decreased as increasing cutoff intensities were used.
The IgMFA sensitivity ranged with the different cutoff intensities from ≥ 1+ (90%; 95% credible interval = 79 94%) to 4+ (29.6%; 95% credible interval = 22 35%).
Using this cutoff, AURKA intensity scores in the validation set were dichotomised, and the crude hazard rate ratio (HRR) for AURKA expression was calculated in a Cox regression analysis with OS as outcome.
We illustrate the application of this method for an optimal cutoff estimation, intensity-based filtering and for optimal fold differences selection that provides the highest ratio of true to false signals.
The training set was used to determine the optimal cutoff for dichotomising intensity scores into 'low andKA'high 'high AURKA' staining intensities.
An intensity cutoff did not filter out samples in which at least one average reading had intensity above the cutoff for the peak tested.
A reference temperature is 296 K and an intensity cutoff is 10−27 cm−1/molecule cm−2 at 4000 K.
The light source was a 300-W xenon lamp with a 420-nm cutoff and an intensity of ~100 mW/cm2.
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