1 - 17 of 17 results

DPT / Diffusion PseudoTime

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.


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).


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.

MATCHER / Manifold Alignment To CHaracterize Experimental Relationships

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.

VAMF / Varying-censoring Aware Matrix Factorization

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.


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.

SCIMITAR / Single Cell Inference of MorphIng Trajectories and their Associated Regulation

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.


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 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.