PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data.
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ABSTRACT:
SUBMITTER: Song D
PROVIDER: S-EPMC8082818 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve. RESULTS:We introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods. CONCLUSIONS:Slingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression.
| S-EPMC6007078 | biostudies-literature
Project description:Pseudotime analyses of single-cell RNA-seq data have become increasingly common. Typically, a latent trajectory corresponding to a biological process of interest-such as differentiation or cell cycle-is discovered. However, relatively little attention has been paid to modelling the differential expression of genes along such trajectories.We present switchde , a statistical framework and accompanying R package for identifying switch-like differential expression of genes along pseudotemporal trajectories. Our method includes fast model fitting that provides interpretable parameter estimates corresponding to how quickly a gene is up or down regulated as well as where in the trajectory such regulation occurs. It also reports a P -value in favour of rejecting a constant-expression model for switch-like differential expression and optionally models the zero-inflation prevalent in single-cell data.The R package switchde is available through the Bioconductor project at https://bioconductor.org/packages/switchde .kieran.campbell@sjc.ox.ac.uk.Supplementary data are available at Bioinformatics online.
| S-EPMC5408844 | biostudies-other
Project description:Motivation:Pseudotime estimation from single-cell gene expression data allows the recovery of temporal information from otherwise static profiles of individual cells. Conventional pseudotime inference methods emphasize an unsupervised transcriptome-wide approach and use retrospective analysis to evaluate the behaviour of individual genes. However, the resulting trajectories can only be understood in terms of abstract geometric structures and not in terms of interpretable models of gene behaviour. Results:Here we introduce an orthogonal Bayesian approach termed 'Ouija' that learns pseudotimes from a small set of marker genes that might ordinarily be used to retrospectively confirm the accuracy of unsupervised pseudotime algorithms. Crucially, we model these genes in terms of switch-like or transient behaviour along the trajectory, allowing us to understand why the pseudotimes have been inferred and learn informative parameters about the behaviour of each gene. Since each gene is associated with a switch or peak time the genes are effectively ordered along with the cells, allowing each part of the trajectory to be understood in terms of the behaviour of certain genes. We demonstrate that this small panel of marker genes can recover pseudotimes that are consistent with those obtained using the entire transcriptome. Furthermore, we show that our method can detect differences in the regulation timings between two genes and identify 'metastable' states-discrete cell types along the continuous trajectories-that recapitulate known cell types. Availability and implementation:An open source implementation is available as an R package at http://www.github.com/kieranrcampbell/ouija and as a Python/TensorFlow package at http://www.github.com/kieranrcampbell/ouijaflow. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC6298060 | biostudies-literature
Project description:One of the major challenges in single-cell data analysis is the determination of cellular developmental trajectories using single-cell data. Although substantial studies have been conducted in recent years, more effective methods are still strongly needed to infer the developmental processes accurately. This work devises a new method, named DTFLOW, for determining the pseudo-temporal trajectories with multiple branches. DTFLOW consists of two major steps: a new method called Bhattacharyya kernel feature decomposition (BKFD) to reduce the data dimensions, and a novel approach named Reverse Searching on k-nearest neighbor graph (RSKG) to identify the multi-branching processes of cellular differentiation. In BKFD, we first establish a stationary distribution for each cell to represent the transition of cellular developmental states based on the random walk with restart algorithm, and then propose a new distance metric for calculating pseudotime of single cells by introducing the Bhattacharyya kernel matrix. The effectiveness of DTFLOW is rigorously examined by using four single-cell datasets. We compare the efficiency of DTFLOW with the published state-of-the-art methods. Simulation results suggest that DTFLOW has superior accuracy and strong robustness properties for constructing pseudotime trajectories. The Python source code of DTFLOW can be freely accessed at https://github.com/statway/DTFLOW.
| S-EPMC8602766 | biostudies-literature
Project description:Single cell gene expression profiling can be used to quantify transcriptional dynamics in temporal processes, such as cell differentiation, using computational methods to label each cell with a 'pseudotime' where true time series experimentation is too difficult to perform. However, owing to the high variability in gene expression between individual cells, there is an inherent uncertainty in the precise temporal ordering of the cells. Pre-existing methods for pseudotime estimation have predominantly given point estimates precluding a rigorous analysis of the implications of uncertainty. We use probabilistic modelling techniques to quantify pseudotime uncertainty and propagate this into downstream differential expression analysis. We demonstrate that reliance on a point estimate of pseudotime can lead to inflated false discovery rates and that probabilistic approaches provide greater robustness and measures of the temporal resolution that can be obtained from pseudotime inference.
| S-EPMC5117567 | biostudies-literature
Project description:Pseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many computational methods have been developed to infer the pseudo-temporal trajectories of cells within a biological sample, methods that compare pseudo-temporal patterns with multiple samples (or replicates) across different experimental conditions are lacking. Lamian is a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis. It can be used to identify changes in a biological process associated with sample covariates, such as different biological conditions, and also to detect changes in gene expression, cell density, and topology of a pseudotemporal trajectory. Unlike existing methods that ignore sample variability, Lamian draws statistical inference after accounting for cross-sample variability and hence substantially reduces sample-specific false discoveries that are not generalizable to new samples. Using both simulations and real scRNA-seq data, including an analysis of differential immune response programs between COVID-19 patients with different disease severity levels, we demonstrate the advantages of Lamian in decoding cellular gene expression programs in continuous biological processes.
| S-EPMC8288148 | biostudies-literature
Project description:[This corrects the article DOI: 10.1371/journal.pcbi.1005212.].
| S-EPMC5388462 | biostudies-literature
Project description:Recent advances in bioinformatics analyses have led to the development of novel tools enabling the capture and trajectory mapping of single-cell RNA sequencing (scRNAseq) data. However, there is a lack of methods to assess the contributions of biological pathways and transcription factors to an overall developmental trajectory mapped from scRNAseq data. In this manuscript, we present a simplified approach for trajectory inference of pathway significance (TIPS) that leverages existing knowledgebases of functional pathways and other gene lists to provide further mechanistic insights into a biological process. TIPS identifies key pathways which contribute to a process of interest, as well as the individual genes that best reflect these changes. TIPS also provides insight into the relative timing of pathway changes, as well as a suite of visualizations to enable simplified data interpretation of scRNAseq libraries generated using a wide range of techniques. The TIPS package can be run through either a web server or downloaded as a user-friendly GUI run in R, and may serve as a useful tool to help biologists perform deeper functional analyses and visualization of their single-cell data.
| S-EPMC8425418 | biostudies-literature
Project description:MotivationRepeated cross-sectional time series single cell data confound several sources of variation, with contributions from measurement noise, stochastic cell-to-cell variation and cell progression at different rates. Time series from single cell assays are particularly susceptible to confounding as the measurements are not averaged over populations of cells. When several genes are assayed in parallel these effects can be estimated and corrected for under certain smoothness assumptions on cell progression.ResultsWe present a principled probabilistic model with a Bayesian inference scheme to analyse such data. We demonstrate our method's utility on public microarray, nCounter and RNA-seq datasets from three organisms. Our method almost perfectly recovers withheld capture times in an Arabidopsis dataset, it accurately estimates cell cycle peak times in a human prostate cancer cell line and it correctly identifies two precocious cells in a study of paracrine signalling in mouse dendritic cells. Furthermore, our method compares favourably with Monocle, a state-of-the-art technique. We also show using held-out data that uncertainty in the temporal dimension is a common confounder and should be accounted for in analyses of repeated cross-sectional time series.Availability and implementationOur method is available on CRAN in the DeLorean package.Contactjohn.reid@mrc-bsu.cam.ac.ukSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC5039927 | biostudies-literature
Project description:We develop scSTEM, single-cell STEM, a method for clustering dynamic profiles of genes in trajectories inferred from pseudotime ordering of single-cell RNA-seq (scRNA-seq) data. scSTEM uses one of several metrics to summarize the expression of genes and assigns a p-value to clusters enabling the identification of significant profiles and comparison of profiles across different paths. Application of scSTEM to several scRNA-seq datasets demonstrates its usefulness and ability to improve downstream analysis of biological processes. scSTEM is available at https://github.com/alexQiSong/scSTEM .
| S-EPMC9264648 | biostudies-literature