GTestimate: Improving relative gene expression estimation in scRNA-seq using the Good-Turing estimator

Single-cell RNA-seq suffers from unwanted technical variation between cells, caused by its complex experiments and shallow sequencing depths. We present GTestimate, a new normalization method based on the Good-Turing estimator, which improves upon conventional normalization methods by accounting for unobserved genes. To validate GTestimate we developed a novel cell targeted PCR-amplification approach (cta-seq), which enables ultra-deep sequencing of single cells. Based on this data we show that the Good-Turing estimator improves relative gene expression estimation and cell-cell distance estimation. Finally, we use GTestimate's compatibility with Seurat workflows to explore three common example data-sets and show how it can improve downstream results. ### Competing Interest Statement The authors have declared no competing interest.