ArticleNucleic acids research2022
Enhancing biological signals and detection rates in single-cell RNA-seq experiments with cDNA library equalization.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- scDEcrypter: Uncertainty-aware differential expression analysis for viral infection in scRNA-seq.bioRxiv : the preprint server for biology · 2026Article
- Interpretable trajectory inference with single-cell linear adaptive negative-binomial expression (scLANE) testing.Nucleic acids research · 2026Article
- Interpretable Trajectory Inference with Single Cell Linear Adaptive Negative-binomial Expression (scLANE) Testing.bioRxiv : the preprint server for biology · 2025Article
- Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference.Briefings in bioinformatics · 2024Article
- Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference.bioRxiv : the preprint server for biology · 2023Article
- Review
- Studying stochastic systems biology of the cell with single-cell genomics data.bioRxiv : the preprint server for biology · 2023Article
- Analysis of Single-Cell RNA-seq Data.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Effect of CB2 Stimulation on Gene Expression in Pediatric B-Acute Lymphoblastic Leukemia: New Possible Targets.International journal of molecular sciences · 2022Article
- The use of base editing technology to characterize single nucleotide variants.Computational and structural biotechnology journal · 2022Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Considerable effort has been devoted to refining experimental protocols to reduce levels of technical variability and artifacts in single-cell RNA-sequencing data (scRNA-seq). We here present evidence that equalizing the concentration of cDNA libraries prior to pooling, a step not consistently performed in single-cell experiments, improves gene detection rates, enhances biological signals, and reduces technical artifacts in scRNA-seq data. To evaluate the effect of equalization on various protocols, we developed Scaffold, a simulation framework that models each step of an scRNA-seq experiment. Numerical experiments demonstrate that equalization reduces variation in sequencing depth and gene-specific expression variability. We then performed a set of experiments in vitro with and without the equalization step and found that equalization increases the number of genes that are detected in every cell by 17-31%, improves discovery of biologically relevant genes, and reduces nuisance signals associated with cell cycle. Further support is provided in an analysis of publicly available data.
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Registered trials
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