Evaluating discrepancies in dimensionality reduction for time-series single-cell RNA-sequencing data.

Area of Science:

• Computational Biology
• Genomics
• Data Science

Background:

• Time-series single-cell RNA sequencing (scRNA-seq) enables studying cellular dynamics.
• Dimensionality reduction techniques are used to visualize these dynamics in low-dimensional manifolds.
• Challenges exist in uniquely identifying temporal structures due to cell correspondence issues across time points.

Purpose of the Study:

• To investigate and quantify discrepancies in dynamical pattern representation across different dimensionality reduction techniques for scRNA-seq data.
• To propose a novel approach for reasoning about these discrepancies using synthetic data.
• To guide the development of improved methods for analyzing temporal scRNA-seq data.

Main Methods:

• Generation of synthetic time-series scRNA-seq data using variational autoencoders to create biologically plausible dynamical patterns.
• Application and comparison of various dimensionality reduction techniques: Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), and single-cell Variational Inference (scVI).
• Development of an approach to reason about the extent to which different low-dimensional manifolds capture distinct dynamical patterns.

Main Results:

• Synthetic data demonstrated that different dimensionality reduction techniques capture varying aspects of temporal dynamics.
• No single technique consistently outperformed others in representing dynamical patterns.
• Results suggest that individual techniques may not reliably represent dynamics when used in isolation.

Conclusions:

• The proposed synthetic dynamical pattern approach provides a framework for evaluating dimensionality reduction methods for time-series scRNA-seq data.
• Comparing multiple dimensionality reduction techniques is essential for a comprehensive understanding of cellular dynamics.
• This work highlights the need for robust methods development to accurately detect complex temporal patterns in scRNA-seq data.