SCKAR is a reference-free method for scRNA-seq data analysis

Analysis of single-cell RNA-seq data is typically performed on a gene expression matrix estimated by aligning reads to a reference transcriptome. However, this approach is difficult to apply to organisms with no or incomplete references. Additionally, events deviating from the reference remain undetected. Here we present a k-mer based reference-free method SCKAR for analysis of single-cell RNA-seq data. We assess its performance on a metastatic renal cell carcinoma dataset, and find that it largely captures differentially expressed genes. We then analyze a recent dataset on neurogenesis in axolotl and observe increased transcription of ribosomal and mitochondrial RNAs during neurogenesis, as well as a microRNA previously linked to neuronal development. Finally, in an analysis of a congenital heart disease dataset, we detect long non-coding RNAs and intron retention in heart disease-related genes in diseased cardiomyocytes. SCKAR thus provides a reference-free approach for uncovering novel transcriptional dynamics across diverse biological systems. Reference-free scRNA-seq analysis using sckAR reveals elevated rRNA and mtRNA transcription during axolotl neurogenesis, and increased intronic and lncRNA transcription in congenital heart disease.