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

ABSTRACT:

Background

Single-cell RNA-seq suffers from unwanted technical variation between cells, caused by its complex experiments and shallow sequencing depths. Many conventional normalization methods try to remove this variation by calculating the relative gene expression per cell. However, their choice of the maximum likelihood estimator is not ideal for this application.

Results

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 these data, we show that the Good-Turing estimator improves relative gen

SUBMITTER: Fahrenberger M 

PROVIDER: S-EPMC12569601 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature