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

There are various dimensionality reduction techniques for visually inspecting dynamical patterns in time-series single-cell RNA-sequencing (scRNA-seq) data. However, the lack of one-to-one correspondence between cells across time points makes it difficult to uniquely uncover temporal structure in a low-dimensional manifold. The use of different techniques may thus lead to discrepancies in the representation of dynamical patterns. However, The extent of these discrepancies remains unclear. To investigate this, we propose an approach for reasoning about such discrepancies based on synthetic time-series scRNA-seq data generated by variational autoencoders. The synthetic dynamical patterns induced in a low-dimensional manifold reflect biologically plausible temporal patterns, such as dividing cell clusters during a differentiation process. We consider manifolds from different dimensionality reduction techniques, such as principal component analysis, t-distributed stochastic neighbor embedding, uniform manifold approximation, and projection and single-cell variational inference. We illustrate how the proposed approach allows for reasoning about to what extent low-dimensional manifolds, obtained from different techniques, can capture different dynamical patterns. None of these techniques was found to be consistently superior and the results indicate that they may not reliably represent dynamics when used in isolation, underscoring the need to compare multiple perspectives. Thus, the proposed synthetic dynamical pattern approach provides a foundation for guiding future methods development to detect complex patterns in time-series scRNA-seq data.