Diffusion maps for high-dimensional single-cell analysis of differentiation data.

Area of Science:

• Computational Biology
• Genomics
• Systems Biology

Background:

• Single-cell technologies reveal cellular heterogeneity but present analytical challenges.
• Traditional clustering methods are ill-suited for continuous cellular differentiation processes.
• Identifying developmental trajectories requires methods that capture continuous cell state transitions.

Purpose of the Study:

• To propose and validate diffusion maps for inferring cellular differentiation trajectories from high-dimensional single-cell data.
• To adapt diffusion maps for handling noise and missing data in single-cell experiments.
• To establish a robust pseudotemporal ordering of cells reflecting differentiation pathways.

Main Methods:

• Adaptation of diffusion maps for single-cell gene expression data.
• Inclusion of kernel width optimization and uncertainty handling.
• Application to simulated data and experimental datasets (qPCR, RNA-Seq).

Main Results:

• Diffusion maps successfully establish pseudotemporal ordering of cells, reflecting differentiation trajectories.
• The method demonstrates robustness against noise and sampling density variations.
• Diffusion maps outperform Principal Component Analysis and t-distributed Stochastic Neighbour Embedding in preserving global structure and pseudotemporal order.

Conclusions:

• Diffusion maps provide a powerful framework for analyzing continuous cellular differentiation.
• The adapted method accurately captures cell state transitions and developmental dynamics.
• This approach offers significant advantages over existing dimension reduction techniques for single-cell trajectory inference.