stageR: a general stage-wise method for controlling the gene-level false discovery rate in differential expression and differential transcript usage

Berge, Koen Van den; Soneson, Charlotte; Robinson, Mark D.

doi:10.1186/s13059-017-1277-0

Cited by 104 publications

(160 citation statements)

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“…For the olfactory epithelium case study [Fletcher et al, 2017] detailed below, we apply stage-wise testing, as implemented in stageR [Heller et al, 2009, Van den Berge et al, 2017, to assess DE between lineages using multiple tests for each gene. Stage-wise testing aims to control the overall FDR (OFDR) [Heller et al, 2009], i.e., the expected proportion of genes with at least one falsely rejected null hypothesis among all genes declared DE.…”

Section: Stage-wise Testingmentioning

confidence: 99%

“…Stage-wise testing aims to control the overall FDR (OFDR) [Heller et al, 2009], i.e., the expected proportion of genes with at least one falsely rejected null hypothesis among all genes declared DE. In our case, the OFDR can be interpreted as a gene-level FDR [Van den Berge et al, 2017]. Stage-wise testing is performed in two stages, a screening and a confirmation stage.…”

Section: Stage-wise Testingmentioning

confidence: 99%

“…Moreover, comparing cell clusters within or between lineages can obscure interpretation: it is often unclear which clusters should be compared, how to properly combine the results of several pairwise cluster comparisons, or how to account for the fact that not all these comparisons are independent of each other. Inevitably, the number of cluster comparisons also increases rapidly with the number of lineages of interest, leading to multiple testing issues at the gene level [Van den Berge et al, 2017] and further decreasing the reproducibility of scRNA-seq DE results.…”

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confidence: 99%

“…Likewise, for between-lineage comparisons, tradeSeq provides both global tests to compare expression patterns between entire lineages (useful for initial screening of interesting genes), as well as specific tests that allow researchers to pinpoint relevant di↵erences in expression profiles between lineages. If multiple hypotheses are assessed for each gene, one can build upon our stageR package [Van den Berge et al, 2017] to conduct an omnibus test (e.g., there are no di↵erences in expression profiles across multiple lineages) prior to post hoc tests that identify the relevant specific di↵erences (e.g., all pairwise comparisons between lineages). We benchmark our method against current state-of-the-art methods using simulated datasets (with cyclic, bifurcating, and multifurcating trajectories) and demonstrate its functionality and versatility on two real datasets.…”

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confidence: 99%

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

Berge

Bézieux²,

Street

et al. 2019

Preprint

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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.

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Section: Stage-wise Testingmentioning

confidence: 99%

Section: Stage-wise Testingmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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

Berge

Bézieux²,

Street

et al. 2019

Preprint

Self Cite

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“…In addition to the lack of theoretical guarantees on the power to detect DE genes using time‐course RNA‐seq data, the FDR control of existing methods is not well studied. Simulation and empirical studies like Kvam et al (), Law et al (), Chu et al (), and Van den Berge et al () show that both DESeq and edgeR are conservative in some cases while liberal in others for traditional RNA‐seq analysis as well as for analyzing time‐course RNA‐seq data (Sun et al , ; Nguyen, ). Our comprehensive simulations support the same conclusions.…”

Section: Introductionmentioning

confidence: 99%

Large scale maximum average power multiple inference on time‐course count data with application to RNA‐seq analysis

et al. 2019

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Experiments that longitudinally collect RNA sequencing (RNA‐seq) data can provide transformative insights in biology research by revealing the dynamic patterns of genes. Such experiments create a great demand for new analytic approaches to identify differentially expressed (DE) genes based on large‐scale time‐course count data. Existing methods, however, are suboptimal with respect to power and may lack theoretical justification. Furthermore, most existing tests are designed to distinguish among conditions based on overall differential patterns across time, though in practice, a variety of composite hypotheses are of more scientific interest. Finally, some current methods may fail to control the false discovery rate. In this paper, we propose a new model and testing procedure to address the above issues simultaneously. Specifically, conditional on a latent Gaussian mixture with evolving means, we model the data by negative binomial distributions. Motivated by Storey (2007) and Hwang and Liu (2010), we introduce a general testing framework based on the proposed model and show that the proposed test enjoys the optimality property of maximum average power. The test allows not only identification of traditional DE genes but also testing of a variety of composite hypotheses of biological interest. We establish the identifiability of the proposed model, implement the proposed method via efficient algorithms, and demonstrate its good performance via simulation studies. The procedure reveals interesting biological insights, when applied to data from an experiment that examines the effect of varying light environments on the fundamental physiology of the marine diatom Phaeodactylum tricornutum.

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Statistical Modeling of High Dimensional Counts

Love

2021

Methods in Molecular Biology

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stageR: a general stage-wise method for controlling the gene-level false discovery rate in differential expression and differential transcript usage

Cited by 104 publications

References 49 publications

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

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

Large scale maximum average power multiple inference on time‐course count data with application to RNA‐seq analysis

Statistical Modeling of High Dimensional Counts

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