Learning for single-cell assignment

Efficient single-cell assignment without prior marker gene annotations is essential for single-cell sequencing data analysis. Current methods, however, have limited effectiveness for distinct single-cell assignment. They failed to achieve a well-generalized performance in different tasks because of the inherent heterogeneity of different single-cell sequencing datasets and different single-cell types. Furthermore, current methods are inefficient to identify novel cell types that are absent in the reference datasets. To this end, we present scLearn, a learning-based framework that automatically infers quantitative measurement/similarity and threshold that can be used for different single-cell assignment tasks, achieving a well-generalized assignment performance on different single-cell types. We evaluated scLearn on a comprehensive set of publicly available benchmark datasets. We proved that scLearn outperformed the comparable existing methods for single-cell assignment from various aspects, demonstrating state-of-the-art effectiveness with a reliable and generalized single-cell type identification and categorizing ability.

FastPG: Fast clustering of millions of single cells

Current single-cell experiments can produce datasets with millions of cells. Unsupervised clustering can be used to identify cell populations in single-cell analysis but often leads to interminable computation time at this scale. This problem has previously been mitigated by subsampling cells, which greatly reduces accuracy. We built on the graph-based algorithm PhenoGraph and developed FastPG which has the same cell assignment accuracy but is on average 27x faster in our tests. FastPG also has higher cell assignment accuracy than two other fast clustering methods, FlowSOM and PARC.AvailabilityFastPG is available here: https://github.com/sararselitsky/FastPG