Sphetcher: Spherical Thresholding Improves Sketching of Single-Cell Transcriptomic Heterogeneity

Abstract

The massive size of single-cell RNA sequencing datasets often exceeds the capability of current computational analysis methods to solve routine tasks such as detection of cell types. Recently, geometric sketching was introduced as an alternative to uniform subsampling. It selects a subset of cells (the sketch) that evenly cover the transcriptomic space occupied by the original dataset, to accelerate downstream analyses and highlight rare cell types. Here, we propose algorithm Sphetcher that makes use of the thresholding technique to efficiently pick representative cells within spheres (as opposed to the typically used equal-sized boxes) that cover the entire transcriptomic space. We show that the spherical sketch computed by Sphetcher constitutes a more accurate representation of the original transcriptomic landscape. Our optimization scheme allows to include fairness aspects that can encode prior biological or experimental knowledge. We show how a fair sampling can inform the inference of the trajectory of human skeletal muscle myoblast differentiation.

SEMITONES: Single-cEll Marker IdentificaTiON by Enrichment Scoring

“…Its possible limitations include the need for an adequate cell similarity metric and a set of reference cells that is representative of the cell population. Therefore, in future research, we will explore deterministic approaches and the use of geometric sketching [44] to select an optimal set of reference cells. Additionally, we aim to improve the run time for many features and the memory demand for large numbers of reference cells.…”