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.
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.
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.
Offers a mapping strategy based on spatially distributed scores. DistMap is an algorithm which uses measured gene expression and allows researchers to map single cell RNA sequencing data without requiring transcript-level imputation. The software also includes functions that can be utilized to visualize the expression pattern corresponding to a gene’s gradient calculation.
Assists in analyzing single-cell RNA-seq data. SCDE is a set of statistical methods that fits individual error models for single-cell RNA-seq measurements. It also compares groups of single cells and tests for differential expression. It contains pagoda routines that characterize aspects of transcriptional heterogeneity in populations of single cells using pre-defined gene sets as well as 'de novo' gene sets derived from the data.
Provides single-cell application of the Drosophila brain. Scope provides comprehensive access and allows exploration of gene expression and gene regulatory analyses from large-scale single-cell datasets. This application is available as a user-friendly online application that can also be installed on a local computer, server, or cloud-based infrastructure using a Singularity container.
A computational method to reconstruct full-length, paired T cell receptor (TCR) sequences from T lymphocyte single-cell RNA sequence data. TraCeR links T cell specificity with functional response by revealing clonal relationships between cells alongside their transcriptional profiles. TraCeR extracts TCR-derived sequencing reads for each cell by alignment against ‘combinatorial recombinomes’ comprising all possible combinations of V and J segments. Reads are then assembled into contiguous sequences that are analyzed to find full-length, recombined TCR sequences. Importantly, the reconstructed recombinant sequences typically contain nearly the complete length of the TCR V(D)J region and so allow high-confidence discrimination between closely related gene segments. Our method is sensitive, accurate and easy to adapt to any species for which annotated TCR gene sequences are available.
Serves for single-cell data analysis. Granatum is a program that provides biologists with access to single-cell bioinformatics methods, and software developers with the opportunity to promote and combine their tools with various others in customizable pipelines. Its architecture simplifies the incorporation of cutting-edge tools and enables handling of large datasets. Moreover, it can eliminate inter-module incompatibilities by isolating the dependencies of each module.
Allows analysis of single-cell gene expression data. Scanpy integrates preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing and simulation of gene regulatory networks. It enables interfacing of advanced machine learning packages. This tool provides pseudotemporal-ordering and the reconstruction of branching trajectories. It allows simulating single cells governed by gene regulatory networks.
Assists in navigating through the expression profile. SAKE is an R package that uses non-negative matrix factorization (NMF) method for unsupervised clustering. It offers (i) quality controls modules to compare total sequenced reads to total gene transcripts detected, (ii) sample correlation heatmap plot, (iii) heatmap of sample assignment from NMF run, with dark red indicating high confidence in cluster assignments, and (iv) t-distributed stochastic neighbor embedding (t-SNE) plot to compare NMF assigned groups with t-SNE projections.
Offers a simple interface for creating complex simulations that are reproducible and well-documented. Splatter is an R package for reproducible and accurate simulation of single-cell RNA sequencing data. It enables researchers to quickly simulate scRNA-seq count data in a reproducible fashion and make comparisons between simulations and real data. This framework can empower researchers to rapidly and rigorously develop new scRNA-seq analysis methods, ultimately leading to new discoveries in cell biology.
Provides an analytical framework for the sensitive detection of population markers and differentially expressed genes. bigSCale aims to improve detection in large scRNAseq datasets. The software uses large sample sizes to estimate a highly accurate and comprehensive numerical model of noise and it determines the extent of the variation between cells without estimating actual gene expression value.
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.
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.
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.
Preserves distinct structural properties of the data. dropClust uses Locality Sensitive Hashing (LSH), a logarithmic-time algorithm to determine approximate neighborhood for individual transcriptomes. It employs an exponential decay function to select higher number of expression profiles from clusters of relatively smaller sizes. This tool is able to detect principal components (PCs) with multi-modal distribution of the projected transcriptomes by using mixtures of Gaussians.
Simulates and evaluates differential expression from bulk and especially single-cell RNA-seq data. powsimR can not only estimate sample sizes necessary to achieve a certain power, but also informs about the power to detect differential expression (DE) in a data set at hand. This module integrates estimated and simulated expression differences to calculate marginal and conditional error matrices. To calculate these matrices, the user can specify nominal significance levels, methods for multiple testing correction and gene filtering schemes.
An easy to use R package allowing for easy creation and plotting of diffusion maps. Diffusion maps are a spectral method for non-linear dimension reduction and have recently been adapted for the visualization of single cell expression data. This allows to visualize high-dimensional relations between data points in a low-dimensional plot. destiny includes a single-cell specific noise model allowing for missing and censored values.
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).
Implements a methodological toolbox allowing flexible workflows under such a framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies.
An integrated software tool for quality filtering, normalization, feature selection, iterative dimensionality reduction, clustering and the estimation of gene-expression gradients from large ensembles of single-cell RNA-seq datasets. SCell is open source, and implemented with an intuitive graphical interface.
A tool for uncovering high-dimensional structure in single-cell gene expression data. From a table of gene expression measurement for single-cells, SPRING is able to build a k-nearest neighbor (knn) graph and display the graph using a force-directed layout algorithm that renders a real-time simulation in an interactive viewing window. SPRING offers an open-ended data exploration, including interactive discovery of markers genes, genes expression comparison between different subpopulations and selection tools for isolating subpopulations of interest.
Aims at the complete analysis of scRNA-seq data post genome alignment: from the parsing, filtering, and normalization of the input count data files, to the visual representation of the data, identification of cell clusters, differentially expressed genes (including cluster-specific marker genes), and functional gene set enrichment. ASAP combines a wide range of commonly used algorithms with sophisticated visualization tools. It allows researchers to interact with the data in a straightforward fashion and in real time.
Provides a method based on a modeling of Waddington’s epigenetic landscape for retrieving pseudotimes from single-cell data. HopLand is a standalone software that does not depend on prior knowledge of key marker genes and permits users to simulate real biological processes. It can also be applied to determine key regulators and interactions, and, to a broader understanding of various cellular processes such as embryonic development or cancer cell proliferation.
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.
Allows users to fit Grade of membership models (GoM) for clustering of RNA-seq gene expression count data. CountClust also provides tools to identify which genes are most distinctively expressed in each cluster and to aid interpretation of results. The results can provide a richer summary of the structure in RNA-seq data than existing widely-used visualization methods such as Principal Components Analysis (PCA) and hierarchical clustering.
Fits probabilistic pseudotime trajectories to two-dimensional reduced-dimension representations of genomic data using Bayesian Gaussian Process Latent Variable Models. Pseudogp is an R package that forms a wrapper round a stan model, as well as a set of functions to plot the posterior mean curves. It acts on a reduced dimension representation of the data in which it fits a probabilistic curve, allowing posterior pseudotime uncertainty to be quantified.
Guides and summarizes the hierarchical exploration of large single-cell data. CyteGuide is an integrated visualization for that extends Hierarchical Stochastic Neighborhood Embedding (HSNE) by providing effective navigation and visualization of the exploration hierarchy. It can be applied to the analysis of other high-dimensional data as well as other hierarchical techniques. An interactive demonstration is available on the site.
Reconstructs and trains asynchronous Boolean models using single-cell expression data. BTR is a model learning algorithm that can infer both network structure and Boolean rules without needing information on trajectories through cell states. This package can be a useful addition to the current toolbox for processing and understanding single-cell expression data, as it provides significant new capabilities for regulatory network modelling in a user-friendly way.
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.
Supplies a platform for large scale scRNA-seq analysis. Scedar is an application able to handle large datasets to browse a dataset of interest, cluster cells, and determine cluster separating genes. This package can be used to process: (i) quality control and identification of cell outliers; (ii) visualization and; (iii) clustering. Besides, it can be customized and was developed to be integrated into external workflows.
An R package providing functions for fitting a modified Michaelis-Menten (MM) equation to the pattern of dropouts observed in a single-cell sequencing experiment. Analyses based on the MM equation, such as identifying differentially expressed genes or biased quantification, are provided with automatically generated visualizations.
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.
Assists in probabilistic representation and analysis of gene expression in single cells. scVI is a probabilistic approach to normalization and downstream analysis of scRNA-seq data. This method is based on a hierarchical Bayesian model with conditional distributions specified by deep neural networks. It permits to rely instead on stochastic optimization, sampling a fixed number of cells at each iteration.
Provides an interface for interactive data exploration. iSEE allows users to simultaneously visualize multiple aspects of a given data set, including experimental data, metadata and analysis results. The interface is flexible and can be dynamically customized by the user. It can also be used for data exploration for hypothesis generation during the course of a research project.
Discovers key biological properties that dominate the variability between cells in a sample. VISION highlights numerous gradients or sub-clusters which reflect different cellular functions or states, and which may not be captured by a rigid precomputed labeling. It can recognize biological properties that differ between precomputed stratifications or that change smoothly along a given cellular trajectory.
Allows users to investigate and explore single-cell gene expression data. CellexalVR consists of a virtual reality (VR) platform built to work with the HTC Vive controller. It offers a way to select sub-populations directly by passing them through a selection tool from which heatmaps and transcription factor (TF) correlation networks can be constructed. This tool is able to integrate cell surface marker intensities captured during index sorting.
Allows creation of workflow for the analysis of Single cell RNA sequencing (scRNA-seq) experiments. ascend can handle data generated from any single cell library preparation platform. It includes functions to leverage multiple CPUs, allowing most analyses to be performed on a standard desktop or laptop. In summary, this tool implements a state-of-the-art unsupervised clustering method and integrates established analysis techniques for normalization and differential gene expression.
Offers a method for handling and extracting structure from single-cell RNA-sequencing and CyTOF data. SAUCIE is a standalone software that provides a deep learning approach developed for the analysis of single-cell data from a cohort of patients. The application is based on different layers able to performs several tasks such as data imputation, clustering, batch correction or visualization. The approach is based on the autoencoder neural network framework for unsupervised learning.
Assists in visualizing high dimensional datasets. SWNE is based on a method that apprehends both the local and global structure of the data and enables relevant biological factors and genes to be embedded directly onto the visualization. By capturing global structure, this software finds differentiation trajectories and layer specific neuron structure that is not visible in other visualizations. It can also capture the layer specific structure of excitatory neurons.
Assists in implementing and assessing the performance of a range of normalization workflows. SCONE evaluates the performance of each workflow and ranks them by aggregating over a set of performance metrics. It is applicable to different single-cell RNASeq (scRNAseq) protocols including microfluidic, plate, and droplet, methods. It allows researchers to compare a set of default normalizations as well as to include user-defined normalization methods.
Provides easy and intuitive exploration of single cell transcriptome data. CellView allows knowledge-based and hypothesis-driven exploration of processed single cell transcriptomic data. It can automatically determine cluster numbers, updates the user interface, and render a 3D scatter plot displaying cells clustered in tSNE space. The tool provides a powerful complement to current command line approaches to cluster and identify cell types in single cell experiments.
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.
Provides a generalizable method for visualizing single cells for scRNA-seq analysis. net-SNE is a visualization method which uses a neural network to learn a parametric embedding function that emulates t-stochastic neighbor embedding's (t-SNE) visualization while newly achieving the ability to map unseen cells. It also learns high quality visualizations of single cells.
Enables immersive visualization of single-cell data for hundreds of thousands of cells using a mobile-enabled web browser. starmap is an online application that combines the benefit of a three-dimensional scatter plot for exploring clustering structure and the benefit of star plots for multivariate visualization of an individual cell. Moreover, this tool is designed to allow production of 3D and virtual reality (VR) experiences, such as A-Frame and Three.js, which are cross-platform and can be adapted to computer screens and VR devices.
Allows users to transform raw data from dropSeq/scrbSeq experiment to the final count matrix with QC plots. dropSeqPipe is an open source application that can perform five different tasks: (i) generate fastqc reports of the input data, (ii) obtain the final file for the aligned sorted data, (iii) produce plots based on pre-processing and alignement, (iv) create species plot, and (v) extract the expression data.
Allows Next Generation Sequencing (NGS), microarray analysis, mass spectrometry, and gene ontology. Bioinformatics Toolbox deals with genomic and proteomic information and furnishes functions to explore and display this data with sequence browsers, spatial heatmaps, and clustergrams. It can find peaks, impute values for missing data, and select features using statistical techniques.
Investigates multiple single-cell RNA-seq samples. MUDAN aims to conduct joint annotation of cell types across patients, time-points, and batches. It identifies clusters and artificially separated to simplify their visualization. This tool can recognize fast subpopulation and characterize them. It provides a collection of differential gene expression and marker selection features.
Furnishes functions for preprocessing, normalization, interpretation and visualization of raw microarray gene expression data. IBD identifies new biomarkers in gene expression and comparative genomic hybridization (CGH) data. It can be applied for experimental design, quality control investigation, array normalization, differential expression analysis or functional analysis, between others.