ALLSTATE: Hierarchical Clustering for Single Cells based on Non-linear Transition Embedding

Single-cell RNA sequencing scRNA-seq provides critical insights into cellular diversity, essential for understanding complex biological dynamics. Traditional scRNA-seq analysis employs unsupervised clustering methods and supervised learning-based approaches to interpret cells or cell clusters. However, both of these approaches have limitations. Unsupervised learning-based methods struggle with selecting a single resolution, limiting their ability to reveal the multi-layered nature of cellular diversity. On the other hand, supervised learning-based methods lack the flexibility to adjust to the various levels of resolution needed to fully capture the complex spectrum of cell types and states.
In response to these challenges, hierarchical clustering has emerged as a
superior technique. It enables detailed exploration across various resolutions without predefined cluster counts, thus overcoming the limitations of both unsupervised and supervised methods. Nevertheless, the highdimensional nature of scRNA-seq poses significant analytical challenges.
We introduce ALLSTATE, a novel pipeline that utilizes non-linear transition embedding for dimension reduction, facilitating hierarchical clustering in a computationally efficient manner. Our experiments demonstrate that ALLSTATE achieves satisfactory clustering performance and allows to explore the connections between cellular hierarchies and cell types at multiple levels of resolution. Additionally, ALLSTATE effectively captures complex cellular differentiation paths, offering a nuanced view of cellular heterogeneity with performance comparable to mainstream methods.