Quantitative assessment of cell fate commitment in single-cell transcriptomics using scCS

Cell fate trajectory inference is one of the key downstream methods in single-cell RNA-sequencing data analysis. Multiple existing tools allow studying such and help identify continuous cell fate dynamics, important genes along the trajectory, and reconstruct the transcriptional states a cell passes to its estimated final state. These methods have been essential to study various biological systems, yet cell fate by itself currently presents mostly qualitative analysis, and existing tools do not allow to directly quantify the fate-related parameters, including commitment, transition speed, fate affinity and entropy. Such quantifications may be performed through multiple parameters and result in better understanding and description of cell fates. We present scCS (single-cell Commitment Scoring), a scverse-friendly Python framework for this problem. scCS introduces Discounted Future-Fate Propagation (DFFP), which models the source transition graph as a geometrically stopped random walk that can reach endpoint anchors or stop unresolved, with a user-defined finite expected graph horizon. For each cell, the resulting probabilities are separated into total fate reach, relative affinity among reached fates, entropy-based fate specificity and reach-supported resolved commitment, while Signed Ordering Flux independently quantifies local progression. scCS also provides endpoint-anchor, graph-coverage and horizon-sensitivity diagnostics, an instantaneous local-direction mode, standardized visualizations, gene-level analyses and replicate-aware comparisons across experimental conditions. Applications to pancreatic endocrinogenesis and neural crest-Schwann-cell differentiation illustrate the general framework. scCS converts an explicit biological fate hypothesis into auditable cell-, population- and replicate-level quantities without redefining the source dynamics.