  Signals analysis for cytometry remains a challenging task that has a
significant impact on uncertainty. Conventional cytometers assume that
individual measurements are well characterized by simple properties such as the
signal area, width, and height. However, these approaches have difficulty
distinguishing inherent biological variability from instrument artifacts and
operating conditions. As a result, it is challenging to quantify uncertainty in
the properties of individual cells and perform tasks such as doublet
deconvolution. We address these problems via signals analysis techniques that
use scale transformations to: (I) separate variation in biomarker expression
from effects due to flow conditions and particle size; (II) quantify
reproducibility associated with a given laser interrogation region; (III)
estimate uncertainty in measurement values on a per-event basis; and (IV)
extract the singlets that make up a multiplet. The key idea behind this
approach is to model how variable operating conditions deform the signal shape
and then use constrained optimization to "undo" these deformations for measured
signals; residuals to this process characterize reproducibility. Using a
recently developed microfluidic cytometer, we demonstrate that these techniques
can account for instrument and measurand induced variability with a residual
uncertainty of less than 2.5% in the signal shape and less than 1% in
integrated area.
