Compression of quantification uncertainty for scRNA-seq counts

Scott Van Buren, Hirak Sarkar, Avi Srivastava, Naim U. Rashid, Rob Patro, Michael I. Love

Abstract: Quantification estimates of gene expression from single-cell RNA-seq (scRNA-seq) data have inherent uncertainty due to reads that map to multiple genes. Many existing scRNA-seq quantification pipelines ignore multi-mapping reads and therefore underestimate expected read counts for many genes. alevin accounts for multi-mapping reads and allows for the generation of ‘inferential replicates’, which reflect quantification uncertainty. Previous methods have shown improved performance when incorporating these replicates into statistical analyses, but storage and use of these replicates increases computation time and memory requirements.