A general and flexible method for signal extraction from single-cell RNA-seq data

By Davide Risso, Fanny Perraudeau, S. Gribkova, Sandrine Dudoit, Jean-Philippe Vert. Nature Communications, 2017

624 citations recorded by the source corpus.

Single-cell RNA-sequencing (scRNA-seq) is a powerful high-throughput technique that enables researchers to measure genome-wide transcription levels at the resolution of single cells. Because of the low amount of RNA present in a single cell, some genes may fail to be detected even though they are expressed; these genes are usually referred to as dropouts. Here, we present a general and flexible zero-inflated negative binomial model (ZINB-WaVE), which leads to low-dimensional representations of the data that account for zero inflation (dropouts), over-dispersion, and the count nature of the data. We demonstrate, with simulated and real data, that the model and its associated estimation procedure are able to give a more stable and accurate low-dimensional representation of the data than principal component analysis (PCA) and zero-inflated factor analysis (ZIFA), without the need for a preliminary normalization step. Single-cell RNA sequencing (scRNA-seq) data provides information on transcriptomic heterogeneity within cell populations. Here, Risso et al develop ZINB-WaVE for low-dimensional representations of scRNA-seq data that account for zero inflation, over-dispersion, and the count nature of the data.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b08438c2970a5cf8300055186e13687ac92e891b

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Citations in the corpus

Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model (2019, methodology): “Normalization, transformation, and dimension reduction: PCA on log-CPM z -scores, ZINB-WAVE [28], PCA on deviance residuals , PCA on Pearson residuals , and GLM-PCA .”
Comparison and evaluation of statistical error models for scRNA-seq (2021, background): “For example, a recent study [22] argued that fixing the NB inverse overdispersion parameter θ to a single value is an appropriate estimate of technical overdispersion for all genes in all scRNA-seq datasets, while others [23] propose learning unique parameter values for each gene in each dataset.”
Trajectory-based differential expression analysis for single-cell sequencing data (2019, neutral): “, Risso et al. [2018b]).”
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics (2017, methodology): “Our recommended single-cell RNASeq data analysis workflow, implemented in Bioconductor R packages, is described in [19]: the pipeline includes the data-adaptive selection of a normalization procedure (scone package; [20]), dimensionality reduction using a zero-inflated negative binomial model (zinbw”
Droplet scRNA-seq is not zero-inflated (2019, methodology): “On a statistics oriented branch of the scRNA-seq methods field, the zero-inflated negative binomial distribution have become popular as a way to directly model scRNA-seq data (Eraslan et al., 2018; Lopez et al., 2018; Risso et al., 2018).”
Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models (2019, neutral): “[17] D.”
A benchmark of batch-effect correction methods for single-cell RNA sequencing data (2020, methodology): “This has prompted efforts to develop workflows to handle data with such characteristics [4 – 6].”
Spatiotemporal analysis of human intestinal development at single-cell resolution (2021, positive): “For each comparison (cluster-level or compartment level), we used the ‘zinbwave’ function from R package ‘‘zinbwave’’ (Risso et al., 2018) to fit single cell count data for each gene and compute their observational weights.”
Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA Sequencing (2018, methodology): “This results points to the importance of at least modeling the sparsity of the data and is consistent with previous contributions [11, 9].”
Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, methodology): “According to this comparison, the top-performing DE analysis tools are DESeq2 (Love et al , 2014) and EdgeR (Robinson et al , 2010) in combination with weights estimated by ZINB-wave (Risso et al , 2018).”
Information Constraints on Auto-Encoding Variational Bayes (2018, methodology): “Most research efforts in scRNA-seq methodology research focus on using generalized linear models and two-way ANOVA [40, 35] to regress out the effects of quality control metrics.”