Aligns single cells from differentiation systems with bifurcating branches. Wishbone pinpoints bifurcation points and labels each cell as pre-bifurcation or as one of two post-bifurcation cell fates to order cells according to their developmental progression. It is generalizable to additional lineages, as it was demonstrated by applying it to mouse myeloid differentiation. The tool outperforms methods developed specifically for single cell RNA-seq data.
Allows to analyze single-cell gene expression experiments. Monocle can realize differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. The software orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well. The tools Census and BEAM are implemented in Monocle.
Measures progression through branching lineages using a random-walk-based distance in diffusion map space. DPT allows for branching and pseudotime analysis on large-scale RNA-seq data sets. This package is significantly more robust with respect to noise in low-density regions and cell outliers than existing methods, which rely on the estimation of minimum spanning trees or sampling-based distances. Furthermore, DPT is able to remove asynchronity of scRNA-seq snapshot data from several days, aligning cells in terms of their degree of differentiation.
An empirical Bayes model to characterize genes with expression changes in ordered single cell RNA-seq experiments. SCPattern utilizes the non-parametrical Kolmogorov-Smirnov statistic, thus it has the flexibility to identify genes with a wide variety of types of changes. Additionally, the Bayes framework allows SCPattern to classify genes into expression patterns with probability estimates. Simulation results show that SCPattern is well powered for identifying genes with expression changes while the false discovery rate is well controlled. SCPattern is also able to accurately classify these dynamic genes into directional expression patterns. Applied to a scRNA-seq time course dataset studying human embryonic cell differentiation, SCPattern detected a group of important genes that are involved in mesendoderm and definitive endoderm cell fate decisions, positional patterning, and cell cycle.
Allows to reconstruct the differentiation trajectory from the pluripotent state through mesendoderm to definitive endoderm (DE). WaveCrest permits to reorder single cells according to the expression of key gene markers. It can identify candidate genes that could function as pioneer regulators governing the transition from mesendoderm to the DE state. It takes a group of genes of interest and aims to recover a smooth expression profile along time for each of the genes in consideration in implementing a constrained extended nearest-insertion (ENI) algorithm to reorder cells.
Characterizes corresponding transcriptomic and epigenetic changes in embryonic stem cells (ESCs). MATCHER gives insight into the sequential changes of genomic information. It allows the use of both single cell gene expression and epigenetic data in the construction of cell trajectories. The tool can be useful for studying a variety of biological processes, such as differentiation, reprogramming, immune cell activation, and tumorigenesis.
Provides a method for identifying the branching times of individual genes. BGP is an open source software using a sparse variational inference and permits a defined parameter estimation via the maximisation of a bound on the marginal likelihood. The application aims to be accurate in global state estimation of errors and high noise. Its uncertainty can also be used in downstream analysis of the individual gene branching times.
A computational method for extracting lineage relationships from single-cell gene expression data, and modeling the dynamic changes associated with cell differentiation. SCUBA draws techniques from nonlinear dynamics and stochastic differential equation theories, providing a systematic framework for modeling complex processes involving multi-lineage specifications.
Models time series accounting for noise in the temporal dimension. This R package implements the DeLorean model to estimate pseudotimes for cell expression data. The DeLorean model uses a Gaussian process latent variable model to model uncertainty in the capture time of cross-sectional data. This method was specifically designed for single cell transcriptome experiments. It was fit to three separate datasets each using a different biological assay (microarrays, single cell nCounter and single cell RNA-seq) in three organisms (human, mouse and Arabidopsis).
Models differential expression over pseudotime. switchde is a statistical framework that allows inference of switch-like differential expression along single-cell trajectories. The software identifies switch-like differential expression analysis along single-cell trajectories. It provides interpretable parameter estimates corresponding to gene regulation strength and timing, incorporating zero-inflation that is prevalent in many scRNA-seq datasets.
Learns pseudotimes from single-cell RNA-seq data. Ouija is an orthogonal approach implemented in a latent variable model statistical framework. The software can integrate prior expectations of gene behaviour along trajectories using Bayesian nonlinear factor analysis. It is able to recover posterior uncertainty information about key parameters, such as the gene activation time, that allows to explicitly determine a potential ordering of gene (de)activation events over (pseudo)time.
A method for analysis and characterization of single-cell isoform-level gene expression data. ISOP enables analysis of single-cell preference, commitment and heterogeneity of isoform level expression. Based on this method, we defined a set of six principal patterns of isoform expression relationships between isoforms from the same gene, including isoform preference, bimodal isoform preference, and mutually exclusive expression commitment.
Leverages morphing Gaussian mixtures to track biological progression and models the rewiring of these gene networks from static transcriptomes. SCIMITAR models account for heteroscedastic noise and increase the statistical power to detect progression associated genes when compared to traditional differential expression tests. It allows to detect modes in co-expression structure in the trajectory: defined coregulatory states that represent potential metastable and transitionary cell states.
Infers computational models of linear dynamic processes in an accurate and data-driven approach. Scorpius is an R package that enables de novo investigation and characterization of dynamic processes and identified well-known properties of dendritic cells (DCs) in a purely data-driven way. It accurately reconstructs trajectories for a wide variety of dynamic cellular processes, automatically identifies marker genes, speeding up knowledge discovery and is fully unsupervised.
Improves the detection of changes in the transcriptional heterogeneity pattern of in single-cell RNA-seq data using two heterogeneity parameters: "burst proportion" and "burst magnitude", whose changes are validated using RNA FISH. Sphinx provides improved detection of transcriptional changes and new insights into stochastic and noisy nature of single cells.
Allows to model cell-specific detection patterns using random effects. VAMF helps users to identify the factors in the case of systematic bias. This tool represents a probabilistic dimension reduction method for single cell RNA-Seq datasets. It is able to remove differences in the two-batches dataset, and attributes the unwanted variability to differences in detection rates across batches. The particularly of this method is that it removes the batch effect, but did not entirely remove the biological specimen effect.
Enables de novo discovery of both continuous and discrete expression patterns from single cell RNA-sequencing (scRNA-seq). scHPF is a Bayesian factorization method using Hierarchical Poisson Factorization to avoid prior normalization and model variable sparsity across both cells and genes. The software models the nuanced features of scRNA-seq data, while identifying highly variable gene signatures, unconstrained by predefined structures. It was applied to single-cell expression profiles obtained from the core and invasive edge of a high-grade glioma.
Provides convenient functions for users to assign genes into different gene expression patterns such as constant, monotone increasing and increasing then decreasing. SEPA then performs GO enrichment analysis to analysis the functional roles of genes with same or similar patterns.
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