  In single-cell RNA sequencing (scRNA-seq) data analysis, a critical challenge
is to infer hidden dynamic cellular processes from measured static cell
snapshots. To tackle this challenge, many computational methods have been
developed from distinct perspectives. Besides the common perspectives of
inferring trajectories (or pseudotime) and RNA velocity, another important
perspective is to estimate the differentiation potential of cells, which is
commonly referred to as "cell potency." In this review, we provide a
comprehensive summary of 11 computational methods that estimate cell potency
from scRNA-seq data under different assumptions, some of which are even
conceptually contradictory. We divide these methods into three categories:
mean-based, entropy-based, and correlation-based methods, depending on how a
method summarizes gene expression levels of a cell or cell type into a potency
measure. Our review focuses on the key similarities and differences of the
methods within each category and between the categories, providing a high-level
intuition of each method. Moreover, we use a unified set of mathematical
notations to detail the 11 methods' methodologies and summarize their usage
complexities, including the number of ad-hoc parameters, the number of required
inputs, and the existence of discrepancies between the method description in
publications and the method implementation in software packages. Realizing the
conceptual contradictions of existing methods and the difficulty of fair
benchmarking without single-cell-level ground truths, we conclude that accurate
estimation of cell potency from scRNA-seq data remains an open challenge.
