  Quantifying the size of cell populations is crucial for understanding
biological processes such as growth, injury repair, and disease progression.
Often, experimental data offer information in the form of relative frequencies
of distinct cell types, rather than absolute cell counts. This emphasizes the
need to devise effective strategies for estimating absolute cell quantities
from fraction data. In response to this challenge, we present two computational
approaches grounded in stochastic cell population models: the first-order
moment method (FOM) and the second-order moment method (SOM). These methods
explicitly establish mathematical mappings from cell fraction to cell
population size using moment equations of the stochastic models. Notably, our
investigation demonstrates that the SOM method obviates the requirement for a
priori knowledge of the initial population size, highlighting the utility of
incorporating variance details from cell proportions. The robustness of both
the FOM and SOM methods was analyzed from different perspectives. Additionally,
we extended the application of the FOM and SOM methods to various biological
mechanisms within the context of cell plasticity models. Our methodologies not
only assist in mitigating the inherent limitations of experimental techniques
when only fraction data is available for detecting cell population size, but
they also offer new insights into utilizing the stochastic characteristics of
cell population dynamics to quantify interactions between different biomasses
within the system.
