Algorithms {error model algorithm} can check feature-signal noise, measure background variance, find cross-hybridization, check for spatial crosstalk and spectral crosstalk, measure normalization variation, and study replicates. Error model weights optimize signal-to-noise ratio. It has confidence value. Error flags label too-high variance, hybridization-control variance, high background, rejected pixels, bright neighbors, too-low signal-to-noise ratio, and saturated pixels.
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Date Modified: 2022.0224