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GTestimate: improving relative gene expression estimation in scRNA-seq using the Good-Turing estimator
Martin Fahrenberger1,2, Christopher Esk3,4, Jürgen A Knoblich4,5
1Center for Integrative Bioinformatics Vienna (CIBIV), Max Perutz Labs, University of Vienna and Medical University of Vienna, Vienna BioCenter (VBC), 1030 Vienna, Austria.
GTestimate improves single-cell RNA sequencing normalization using the Good-Turing estimator to better account for unobserved genes. This novel method enhances gene expression and cell distance estimations, leading to improved downstream analysis results.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Single-cell RNA sequencing (scRNA-seq) is susceptible to technical variations due to complex experiments and shallow sequencing.
- Conventional normalization methods often use suboptimal maximum likelihood estimators for relative gene expression per cell.
- These limitations can impact the accuracy of downstream scRNA-seq data analysis.
Purpose of the Study:
- To introduce GTestimate, a novel normalization method for scRNA-seq data.
- To improve the estimation of relative gene expression by accounting for unobserved genes.
- To enhance the accuracy of cell-cell distance estimations in scRNA-seq data.
Main Methods:
- Developed GTestimate, a normalization method utilizing the Good-Turing estimator.
- Introduced cell-targeted PCR amplification sequencing (cta-seq) for ultra-deep single-cell sequencing.
- Validated GTestimate using cta-seq data and explored its compatibility with Seurat workflows on four datasets.
Main Results:
- GTestimate demonstrates improved relative gene expression estimation compared to conventional methods.
- The Good-Turing estimator enhances cell-cell distance estimation accuracy.
- GTestimate integration with Seurat workflows improved downstream analysis results across example datasets.
Conclusions:
- Employing a more suitable estimator, like Good-Turing, significantly enhances scRNA-seq normalization.
- GTestimate offers a user-friendly R-package compatible with various workflows, facilitating widespread adoption.
- Improved normalization has substantial implications for the reliability and interpretation of scRNA-seq downstream results.
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