The single-cell RNA sequencing (scRNA-seq) has become a revolutionary technology to detect and characterize distinct cell populations under different biological conditions. Unlike bulk RNA-seq, the expression of genes from scRNA-seq is highly sparse due to limited sequencing depth per cell. This is worsened by tossing away a significant portion of reads that cannot be mapped during gene quantification. To overcome data sparsity and fully utilize original sequences, we propose scSimClassify, a reference-free and alignment-free approach to classify cell types with k-mer level features derived from raw reads in a scRNA-seq experiment. The major contribution of scSimClassify is the simhash method compressing k-mers with similar abundance profiles into groups. The compressed k-mer groups (CKGs) serve as the aggregated k-mer level features for cell type classification. We evaluate the performance of CKG features for predicting cell types in four scRNA-seq datasets comparing four state-of-the-art classification methods as well as two scRNA-seq specific algorithms. Our experiments demonstrate that the CKG features lend themselves to better performance than traditional gene expression features in scRNA-seq classification accuracy in the majority of cases. Because CKG features can be efficiently derived from raw reads without a resource-intensive alignment process, scSimClassify offers an efficient alternative to help scientists rapidly classify cell types without relying on reference sequences. The current version of scSimClassify is implemented in python and can be found at https://github.com/digi2002/scSimClassify.
A Reference-free Approach for Cell Type Classification with scRNA-seq