Normalizing single-cell RNA sequencing data: Challenges and opportunities

By Catalina A. Vallejos, Davide Risso, Antonio Scialdone, Sandrine Dudoit, John C. Marioni. Nature Methods, 2017

401 citations recorded by the source corpus.

Single-cell transcriptomics is becoming an important component of the molecular biologist's toolkit. A critical step when analyzing data generated using this technology is normalization. However, normalization is typically performed using methods developed for bulk RNA sequencing or even microarray data, and the suitability of these methods for single-cell transcriptomics has not been assessed. We here discuss commonly used normalization approaches and illustrate how these can produce misleading results. Finally, we present alternative approaches and provide recommendations for single-cell RNA sequencing users.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e4ae512d02b519ae13ff18fb37ecde3e3831f683

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Citations in the corpus

Single-Cell RNA-Seq Technologies and Related Computational Data Analysis (2019, background): “In general, normalization can be divided into two diﬀerent types: within-sample normalization and between-sample normalization (Vallejos et al., 2017).”
A deep generative model for gene expression profiles from single-cell RNA sequencing (2017, background): “Here the constant γn are optional covariates that can be passed to fw that account for batch effects, like normalization [12, 13], to remove unwanted variation from the latent representation.”
Interpretable dimensionality reduction of single cell transcriptome data with deep generative models (2017, neutral): “20, 21 and 22).”
A general and flexible method for signal extraction from single-cell RNA-seq data (2017, neutral): “(11) to get the desired ratio of within to between-cluster sums of squares.”
Droplet scRNA-seq is not zero-inflated (2019, neutral): “In this view the inclusion of UMI’s, which removes the effect of transcript lengths and PCR duplication, will not solve the problem of dropouts causing zero-inflation (Vallejos et al., 2017).”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, background): “Notably, size factors are treated as fixed and known, while they are actually random variables that have been estimated from the data [125] , and it is unclear how ignoring their associated uncertainty affects the downstream DE analysis.”
A deep generative model for single-cell RNA sequencing with application to detecting differentially expressed genes (2017, background): “Here the constant γn are optional covariates that can be passed to fw that account for batch effects, like normalization [12, 13], to remove unwanted variation from the latent representation.”
Comparison and evaluation of statistical error models for scRNA-seq (2021, background): “However, separating biological heterogeneity across cells that corresponds to differences in cell type and state from alternative sources of variation represents a key analytical challenge in the normalization and preprocessing of single-cell RNA-seq data [3, 4].”
A test metric for assessing single-cell RNA-seq batch correction (2018, methodology): “More specifically, we consider the fact that HVGs depend on the type of normalization 45 .”
Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, methodology): “The method was also shown to give robust size factor estimates in a small-scale comparison from the original authors (Vallejos et al , 2017).”