Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications

Berge, Koen Van den; Perraudeau, Fanny; Soneson, Charlotte; Love, Michael I.; Risso, Davide; Vert, Jean-Philippe; Robinson, Mark D.; Dudoit, Sandrine

doi:10.1186/s13059-018-1406-4

Cited by 198 publications

(204 citation statements)

References 66 publications

(4 reference statements)

Supporting

Mentioning

188

Contrasting

Order By: Relevance

“…Yet, a recent, large-scale comparison study of DE analysis has suggested that bulk DE testing packages perform comparably to the best-performing single-cell tools . Furthermore, when bulk tools are adapted to model single-cell data via introducing gene weights into the tests, these tools have been suggested to outperform their single-cell counterparts (Van den Berge et al, 2018). 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 .…”

Section: Differential Expression Testingmentioning

confidence: 99%

Current best practices in single‐cell RNA‐seq analysis: a tutorial

Luecken

Theis

2019

Molecular Systems Biology

1,525

1,386

View full text Add to dashboard Cite

Single‐cell RNA ‐seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single‐cell analysis methods. As more analysis tools are becoming available, it is becoming increasingly difficult to navigate this landscape and produce an up‐to‐date workflow to analyse one's data. Here, we detail the steps of a typical single‐cell RNA ‐seq analysis, including pre‐processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell‐ and gene‐level downstream analysis. We formulate current best‐practice recommendations for these steps based on independent comparison studies. We have integrated these best‐practice recommendations into a workflow, which we apply to a public dataset to further illustrate how these steps work in practice. Our documented case study can be found at https://www.github.com/theislab/single-cell-tutorial . This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.

show abstract

Section: Differential Expression Testingmentioning

confidence: 99%

Current best practices in single‐cell RNA‐seq analysis: a tutorial

Luecken

Theis

2019

Molecular Systems Biology

1,525

1,386

View full text Add to dashboard Cite

show abstract

“…Note that we enforce identical basis functions between lineages, i.e., b k does not depend on l, as well as identical smoothing parameter g , in order to ensure that the smoothers are comparable across lineages. Importantly, the model of Equation (1) can accommodate zero-inflated counts typical for full-length scRNA-seq protocols by using observation-level (i.e., cell-level) weights obtained from the zero-inflated negative binomial (ZINB) approach of Van den Berge et al [2018] and Risso et al [2018a].…”

Section: Negative Binomial Generalized Additive Modelsmentioning

confidence: 99%

“…Zero inflation weights are estimated with the ZINB-WaVE method [Risso et al, 2018a], using the cluster labels and batch as covariates. Note that no other trajectory-based DE method can account for zero inflation or provide the range of tests available in tradeSeq; hence, we forgo a comparison with other methods aside from a ZINB-edgeR analysis [Van den Berge et al, 2018].…”

Section: Case Studiesmentioning

confidence: 99%

“…In practice, tradeSeq infers smooth functions for the gene expression measures along pseudotime for each lineage using generalized additive models and tests biologically meaningful hypotheses based on parameters of these smoothers. By allowing cell-level weights for each individual count in the gene-by-cell expression matrix, tradeSeq can handle zero inflation, which is essential for dealing with dropouts in full-length scRNA-seq protocols [Van den Berge et al, 2018]. As it is agnostic to the dimensionality reduction and TI methods, the approach scales from simple to complex trajectories with multiple bifurcations: tradeSeq only requires the original expression count matrix of the individual cells, estimated pseudotimes, and a hard or soft assignment (weights) of the cells to the lineages to infer the lineage-specific smoothers.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Trajectory-based differential expression analysis for single-cell sequencing data

Berge

Bézieux²,

Street

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression levels during biological processes such as the cell cycle, cell type di↵erentiation, and cellular activation. Downstream of trajectory inference, it is vital to discover genes that are associated with the lineages in the trajectory to illuminate the underlying biological processes. Furthermore, genes that are di↵erentially expressed between developmental/activational lineages might be highly relevant to further unravel the system under study. Current data analysis procedures, however, typically cluster cells and assess di↵erential expression between the clusters, which fails to exploit the continuous resolution provided by trajectory inference to its full potential. The few available non-cluster-based methods only assess broad di↵erences in gene expression between lineages, hence failing to pinpoint the exact types of divergence. We introduce a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of (i) within-lineage di↵erential expression by detecting associations between gene expression and pseudotime over an entire lineage or by comparing gene expression between points/regions within the lineage and (ii) between-lineage di↵erential expression by comparing gene expression between lineages over the entire lineages or at specific points/regions. By incorporating observation-level weights, the model additionally allows to account for zero inflation, commonly observed in single-cell RNA-seq data from full-length protocols. We evaluate the method on simulated and real datasets from dropletbased and full-length protocols, and show that the flexible inference framework is capable of yielding biological insights through a clear interpretation of the data.

show abstract

“…One of the more common applications of RNA-seq data is estimating and testing for differences in gene expression between two groups. Many packages and techniques exist to perform this task [Robinson and Smyth, 2007b, Hardcastle and Kelly, 2010, Van De Wiel et al, 2012, Kharchenko et al, 2014, Law et al, 2014, Love et al, 2014, Finak et al, 2015, Guo et al, 2015, Nabavi et al, 2015, Delmans and Hemberg, 2016, Korthauer et al, 2016, Costa-Silva et al, 2017, Qiu et al, 2017, Miao et al, 2018, Van den Berge et al, 2018, Wang and Nabavi, 2018, Wang et al, 2019, and so developing approaches and software to compare these different software packages would be of great utility to the scientific community. Generating data from the two-group model is a special case of (1) and (2), where…”

Section: Application: Evaluating Differential Expression Analysismentioning

confidence: 99%

Data-based RNA-seq Simulations by Binomial Thinning

Gerard

2019

Preprint

View full text Add to dashboard Cite

With the explosion in the number of methods designed to analyze bulk and single-cell RNAseq data, there is a growing need for approaches that assess and compare these methods. The usual technique is to compare methods on data simulated according to some theoretical model. However, as real data often exhibit violations from theoretical models, this can result in unsubstantiated claims of a method's performance. Rather than generate data from a theoretical model, in this paper we develop methods to add signal to real RNA-seq datasets. Since the resulting simulated data are not generated from an unrealistic theoretical model, they exhibit realistic (annoying) attributes of real data. This lets RNA-seq methods developers assess their procedures in non-ideal (model-violating) scenarios. Our procedures may be applied to both single-cell and bulk RNA-seq. We show that our simulation method results in more realistic datasets and can alter the conclusions of a differential expression analysis study. We also demonstrate our approach by comparing various factor analysis techniques on RNA-seq datasets. Our tools are available in the seqgendiff R package on the Comprehensive R Archive Network: https://cran.r-project.org/package=seqgendiff.

show abstract

Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications

Cited by 198 publications

References 66 publications

Current best practices in single‐cell RNA‐seq analysis: a tutorial

Current best practices in single‐cell RNA‐seq analysis: a tutorial

Trajectory-based differential expression analysis for single-cell sequencing data

Data-based RNA-seq Simulations by Binomial Thinning

Contact Info

Product

Resources

About