BGP: identifying gene-specific branching dynamics from single-cell data with a branching Gaussian process

Boukouvalas, Alexis; Hensman, James; Rattray, Magnus

doi:10.1186/s13059-018-1440-2

Cited by 25 publications

(16 citation statements)

References 26 publications

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“…Instead, all of these methods treat the pseudotimes as fixed and known. The BranchedGP method allows for uncertainty in the assignment of cells to lineages and relies on branching Gaussian processes to identify gene-specific branching dynamics 20 . However, it is computationally very intensive, with reported computation time of 2 min per gene on a data set that has been subsampled to 467 cells 20 ; we therefore did not consider this method in our evaluation.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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“…A related model is the PseudoGP framework (Campbell and Yau 2016 ) that uses the posterior distributions from probabilistic pseudotime to quantify the uncertainty in downstream analyses such as differential expression. Branching differentiation processes can also be modelled using Gaussian processes (Boukouvalas et al 2018 ; Penfold et al 2018 ).…”

Section: Bayesian Applications In Single-cell Biologymentioning

confidence: 99%

Bayesian statistical learning for big data biology

Yau

Campbell

2019

Biophys Rev

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Bayesian statistical learning provides a coherent probabilistic framework for modelling uncertainty in systems. This review describes the theoretical foundations underlying Bayesian statistics and outlines the computational frameworks for implementing Bayesian inference in practice. We then describe the use of Bayesian learning in single-cell biology for the analysis of high-dimensional, large data sets.

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“…“Omic” technologies have been successful in cataloguing changes in gene expression during cell fate transitions. Many computational tools have been developed for the ordering of gene expression changes in pseudotime, delineating cell fate bifurcation points and linking genes into networks 3 – 5 . However, while we have a good understanding of the fates/states that cells transition through and their order in time/space, the mechanisms that allow cells to move through the fate/state landscape are not well understood.…”

Section: Introductionmentioning

confidence: 99%

Dynamical gene regulatory networks are tuned by transcriptional autoregulation with microRNA feedback

Minchington

Griffiths‐Jones

Papalopulu

2020

Sci Rep

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Concepts from dynamical systems theory, including multi-stability, oscillations, robustness and stochasticity, are critical for understanding gene regulation during cell fate decisions, inflammation and stem cell heterogeneity. However, the prevalence of the structures within gene networks that drive these dynamical behaviours, such as autoregulation or feedback by microRnAs, is unknown. We integrate transcription factor binding site (tfBS) and microRnA target data to generate a gene interaction network across 28 human tissues. This network was analysed for motifs capable of driving dynamical gene expression, including oscillations. Identified autoregulatory motifs involve 56% of transcription factors (tfs) studied. tfs that autoregulate have more interactions with microRnAs than non-autoregulatory genes and 89% of autoregulatory TFs were found in dual feedback motifs with a microRnA. Both autoregulatory and dual feedback motifs were enriched in the network. tfs that autoregulate were highly conserved between tissues. Dual feedback motifs with microRnAs were also conserved between tissues, but less so, and TFs regulate different combinations of microRNAs in a tissue-dependent manner. The study of these motifs highlights ever more genes that have complex regulatory dynamics. These data provide a resource for the identification of TFs which regulate the dynamical properties of human gene expression. Cell fate changes are a key feature of development, regeneration and cancer, and are often thought of as a "landscape" that cells move through 1,2. Cell fate changes are driven by changes in gene expression: turning genes on or off, or changing their levels above or below a threshold where a cell fate change occurs. "Omic" technologies have been successful in cataloguing changes in gene expression during cell fate transitions. Many computational tools have been developed for the ordering of gene expression changes in pseudotime, delineating cell fate bifurcation points and linking genes into networks 3-5. However, while we have a good understanding of the fates/states that cells transition through and their order in time/space, the mechanisms that allow cells to move through the fate/ state landscape are not well understood. Gene regulatory networks are maps of interactions between different transcription factors (TFs), cofactors, and the genes or transcripts they target 6. Networks are commonly represented diagrammatically as graphs of the connecting components, such as TFs and their targets. Network motifs are small repeating patterns found within larger networks 6. Modelling of networks in this manner allows us to develop an understanding of how components interact and what behaviours they may generate 6-9. Although it is clear that gene interactions are dynamic and change over time, current approaches in many biological studies focus on the qualitative analysis of genes or simple interactions between gene pairs: in short, how the perturbation of one gene affects the expression of another. However, gene expression is...

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BGP: identifying gene-specific branching dynamics from single-cell data with a branching Gaussian process

Cited by 25 publications

References 26 publications

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

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

Bayesian statistical learning for big data biology

Dynamical gene regulatory networks are tuned by transcriptional autoregulation with microRNA feedback

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