scRL: Utilizing Reinforcement Learning to Evaluate Fate Decisions in Single-Cell Data.

Area of Science:

• Computational Biology
• Developmental Biology
• Genomics

Background:

• Single-cell RNA sequencing (scRNA-seq) generates vast transcriptomic data.
• Current trajectory inference tools struggle to precisely identify cell fate decision points.
• Understanding developmental logic requires pinpointing critical differentiation moments.

Purpose of the Study:

• To introduce single-cell reinforcement learning (scRL), a novel framework for analyzing cell differentiation.
• To accurately identify where and when cell fate decisions occur.
• To provide a unified approach for decoding developmental trajectories.

Main Methods:

• Developed an actor-critic reinforcement learning framework (scRL).
• Utilized Latent Dirichlet Allocation to derive an interpretable latent manifold.
• Implemented critic for state-value functions (fate intensity) and actor for optimal route tracing.
• Applied scRL to diverse datasets including hematopoiesis and gene-knockout experiments.

Main Results:

• scRL outperforms fifteen state-of-the-art methods across five evaluation metrics.
• Identified early cell fate decision states preceding lineage commitment.
• Revealed novel regulatory genes, such as Dapp1.
• Provided accurate lineage contribution intensity without ground-truth probabilities.

Conclusions:

• scRL offers a powerful and unified approach for decoding developmental logic from scRNA-seq data.
• The framework accurately pinpoints critical cell fate decisions and developmental routes.
• scRL advances the analysis of complex biological systems and gene regulation.