1 - 50 of 62 results


Allows studying of spatial patterning of gene expression at the single-cell level. Seurat is an R package that enables quality control (QC), analysis, and exploration of single cell RNA-seq data. The software includes three computational methods: (1) unsupervised clustering and discovery of cell types and states, (2) spatial reconstruction of single cell data, and (3) integrated analysis of single cell RNA-seq across conditions, technologies, and species. It can also localize rare subpopulations, and map both spatially restricted and scattered groups.

SPADE / Spanning tree Progression of Density normalized Events

Facilitates the analysis of cellular heterogeneity, the identification of cell types, and comparison of functional markers in response to perturbations, based on a versatile method. SPADE helps to organize high-dimensional cytometry data in an unsupervised manner, and to investigate natural and pathogenic cellular heterogeneity for biological insight. The SPADE algorithm consists of four components: (i) density-dependent downsampling, (ii) clustering, (iii) linking clusters with a minimum spanning tree, and (iv) upsampling to restore all cells in the final result. This modularized process allows more efficient sub-algorithms to replace the current components. In this sense, SPADE can be viewed as a framework for cytometric data analysis and visualization that has the capacity to be evolved and adapted.

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.


A statistical pipeline for identifying oscillatory genes in unsynchronized single cell RNA-seq experiments. Oscope capitalizes on the fact that cells from an unsynchronized population represent distinct states in a system. Oscope utilizes co-regulation information among oscillators to identify groups of putative oscillating genes, and then reconstructs the cyclic order of samples for each group, defined as the order that specifies each sample's position within one cycle of the oscillation, referred to as a base cycle. The reconstructed order is based on minimizing distance between each gene's expression and its gene-specific profile defined by the group's base cycle allowing for phase shifts between different genes.

SLICE / Single Cell Lineage Inference Using Cell Expression Similarity and Entropy

Allows to quantitatively measure differentiation states of individual cells and reconstruct their lineages from scRNA-seq data. SLICE consists of two major utilizations: measuring cell differentiation states based on the calculation of single cell entropy (scEntropy) and predicting cell differentiation lineages by reconstructing cell trajectories directed by scEntropy-derived differentiation states. This tool can work to several conditions as cancer, injury and other disease situations.


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.


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).

TSCAN / Tools for Single Cell ANalysis

A software tool developed to better support in silico pseudo-time reconstruction in single-cell RNA-seq analysis. TSCAN uses a cluster-based minimum spanning tree (MST) approach to order cells. Cells are first grouped into clusters and an MST is then constructed to connect cluster centers. Pseudo-time is obtained by projecting each cell onto the tree, and the ordered sequence of cells can be used to study dynamic changes of gene expression along the pseudo-time. Clustering cells before MST construction reduces the complexity of the tree space. This often leads to improved cell ordering. It also allows users to conveniently adjust the ordering based on prior knowledge. TSCAN has a graphical user interface (GUI) to support data visualization and user interaction. Furthermore, quantitative measures are developed to objectively evaluate and compare different pseudo-time reconstruction methods.

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.

ESAT / End Sequence Analysis ToolKit

A toolkit designed for the analysis of short reads obtained from end-sequence RNA-seq. ESAT addresses mis-annotated or sample-specific transcript boundaries by providing a search step in which it identifies possible unannotated ends de novo. It provides a robust handling of multi mapped reads, which is critical in 3’ DGE analysis. ESAT provides a module specifically designed for alternative start or 3’ UTR (untranslated region) differential isoform expression. It also includes a set of features specifically designed for the analysis of single-cell RNA-seq data.

SLICER / Selective Locally Linear Inference of Cellular Expression Relationships

A method for inferring cellular trajectories from single cell RNA-seq data. SLICER uses locally linear embedding to reconstruct cellular trajectories. SLICER provides four significant advantages over existing methods for inferring cellular trajectories: (1) the ability to automatically select genes to use in building a cellular trajectory with no need for biological prior knowledge; (2) use of locally linear embedding, a nonlinear dimensionality reduction algorithm, for capturing highly nonlinear relationships between gene expression levels and progression through a process; (3) automatic detection of the number and location of branches in a cellular trajectory using a novel metric called geodesic entropy; and (4) the capability to detect types of features in a trajectory such as "bubbles" that no existing method can detect. SLICER more accurately recovers the ordering of points along simulated trajectories than existing methods.

Eclair / Ensemble Cell Lineage Analysis with Improved Robustness

A computational method for the statistical inference of cell lineage relationships from single-cell gene expression data. ECLAIR uses an ensemble approach to improve the robustness of lineage predictions, and provides a quantitative estimate of the uncertainty of lineage branchings. We show that the application of ECLAIR to published datasets successfully reconstructs known lineage relationships and significantly improves the robustness of predictions. In conclusion, ECLAIR is a powerful bioinformatics tool for single-cell data analysis. It can be used for robust lineage reconstruction with quantitative estimate of prediction accuracy.


Provides a contribution to the pre-existing arsenal of pseudo-temporal analysis algorithms developed across a range of application areas including single cell omics and cancer. PhenoPath generalises pseudo-time analysis to a wider range of applications where genetic, phenotypic or environmental contexts may vary between samples and be influential in the trajectories. This model was able to identify covariate-pathway interactions that might be driving specific trajectory differences recovering known associations as well as novel genes.


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.

single-cell diffusion map

Analyzes single-cell differentiation data. single-cell diffusion map aims to deal with the problem of defining differentiation trajectories. Diffusion map are appropriate for the dimension-reduction of single-cell qPCR and RNA-Seq cell differentiation data as they can handle high noise levels, sampling density heterogeneities, missing and uncertain values. As a result, they can organize single cells along the non-linear and complex branches of differentiation, maintain the global structure of the differentiation dynamics and detect rare populations.


Produces tree structures outlining the hierarchical relationship between single-cell samples, while identifying latent groups of genes that can provide biological insights. CellTree can infer complex underlying hierarchical structures in cell populations from expression data alone, and also provide biological backing for the model it creates. The package can provide reasonable default values for most of the parameters used by the model inference, visualisation and analysis algorithms, making it possible for an unfamiliar user of the software to quickly evaluate a new dataset in a few simple lines of R code.


Assists users in the estimation of velocity and the related data analysis. Velocyto is an analysis framework developed for the analysis of expression dynamics single cell RNA seq data. This analysis logic is implemented separately in R and python environments. This method consists of two main components: (i) a command line interface (CLI) used to run the pipeline that generates spliced/unspliced expression matrices and (ii) a library that includes functions to estimate RNA velocity from the data matrices.

reCAT / recover Cycle Along Time

Reconstructs cell cycle time-series using single-cell transcriptome data. reCAT is a computational method consists of four steps: (i) the data processing, including quality control, normalization, and clustering of single cells, (ii) the order of the clusters is then recovered by finding a traveling salesman cycle, (iii) two scoring methods, Bayes-scores and mean-scores subsequently discriminate among cycle stages and (iv) a hidden Markov model (HMM) and a Kalman smoother finally estimate the underlying gene expression levels of the single-cell time-series.

PIVOT / Platform for Interactive analysis and Visualization Of Transcriptomics data

Allows users to analyze and visualize RNA-Seq data. PIVOT furnishes four mains functionalities (i) a graphical interface that is able to wrap existing open source packages in a single user-interface (ii) multiple tools to manipulate datasets to perform derivation or normalization (iii) a way for allowing the compatibility between inputs and outputs from different analysis modules and, (iv) functions for automatically generate reports, publication-quality figures, and reproducible computations.

UNCURL / UNified CompUtational framework for scRNA-seq data processing and Learning

Allows unsupervised and semi-supervised learning using Single Cell RNA-Seq data. To operate these learning, UNCURL provides a method for standardizing any prior biological information including bulk RNA-seq data, microarray data or even information about individual marker gene expression to a form compatible with scRNA-Seq data. Additionally, this package allows the integration of prior information which leads to large improvements in accuracy.

scTDA / single-cell Topological Data Analysis

Serves for topology-based computational analyses. scTDA realizes temporal studies and unbiased transcriptional regulation studies. It is an unsupervised statistical framework that can characterize transient cellular states. This tool can be used to any biological system responding to inductive cues or environmental perturbations like cellular differentiation processes such as hematopoiesis, the evolution of cancer cells, neurodegeneration, or developmental disorders.

FORKS / Finding Orderings Robustly using K-means and Steiner trees

Finds a robust pseudo-temporal ordering of cells with no tuning of hyperparameters. FORKS is adaptable to both single-cell or bulk differentiation data and does not requires hyper-parameter tuning. It is related to k-means and uses Principal Component Analysis (PCA) as dimensionality reduction technique. The tool is scalable to thousands of cells and genes. It is able to discover branching trajectories if present in the data.


Determines temporal trajectories, branching and cell assignments in single cell time series experiments. Temporal Assignment of SIngle Cells (TASIC) uses on a probabilistic graphical model to integrate expression and time information making it more robust to noise and stochastic variations. It uses a Hidden Markov Model (HMM) based on a probabilistic Kalman Filter approach to combine time and expression information for determining the branching process associated with time series single cell studies.


Models transcriptional cell fates as mixtures of the Gaussian Process Latent Variable Model and Overlapping Mixtures of Gaussian Processes (OMGP). GPfates is based on first reconstructing the differentiation trajectory from the observed data, thereby establishing an order for the cells. In a second step, GPfates uses the inferred temporal orders as input for a nonparametric time series mixture model. This approach revealed two simultaneous trends emerging during pseudotime, which separated from each other, indicating that a developmental bifurcation occurred. In a third step, GPfates uses a change point model, thereby facilitating to annotate pseudotime after bifurcation. The source code is freely available for download.

SCOUP / Single-Cell expression data during differentiation with Ornstein–Uhlenbeck Process

A method to analyze single-cell expression data for differentiation. Unlike previous methods, which use dimension reduction approaches and reconstruct differentiation trajectories in reduced space, SCOUP describes gene expression dynamics during differentiation directly, including pseudo-time and cell fate. We evaluated pseudo-time using SCOUP and previous methods based on the consistency between pseudo-time and experimental time and showed that the SCOUP results were superior to those of other methods for almost all conditions. We also compared the accuracy of cell lineage estimation using SCOUP and Monocle, and showed that SCOUP can estimate cell lineages with high accuracy, even for the cells at an early stage of bifurcation. SCOUP is based on a probabilistic model and can be extended to many applications.

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.

SCENT / Single Cell ENTropy

Identifies known cell subpopulations of varying potency, enabling reconstruction of cell-lineage trajectories. SCENT is an algorithm that can be used to identify and quantify biologically relevant expression heterogeneity in single-cell populations, as well as to reconstruct cell-lineage trajectories from time-course data. It differs substantially from other single-cell algorithms in that it uses single-cell entropy to independently order single cells in pseudo-time (i.e. differentiation potency), without the need for feature selection or clustering.

MFA / Mixtures of Factor Analysers

Provides probabilistic inference of single-cell bifurcations. MFA is an R package implementing Gibbs sampling for a Bayesian hierarchical mixture of factor analyzers for inference of bifurcating trajectories in single-cell data. This method is fully generative, incorporating measurement noise into inference. It jointly infers both the pseudotimes and branches compared to post-hoc inference of branch detection, and infers which genes are differentially regulated across the branches.