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

This study introduces a novel scRNA-seq normalization method, which takes unobserved genes into account. To validate GTestimate we designed a cell targeted amplification strategy, enabling us to sequence a small set of cells twice, once at a "typical" sequencing depth and once at a "ultra-deep" sequencing depth.

Type: Transcriptome Sequencing
Archiver: European Genome-Phenome Archive (EGA)

1 Dataset 1 Publication

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID	Description	Technology	Samples
EGAD50000001338	This data-set contains two sequencing runs of the same scRNA-seq library. Once with and once without cell targeted amplification of transcripts.	unspecified	2

Publications	Citations
GTestimate: improving relative gene expression estimation in scRNA-seq using the Good-Turing estimator. Fahrenberger M, Esk C, Knoblich JA, von Haeseler A. Gigascience 14: 2025 giaf084	1