Inference of lineage structure has been referred to as “pseudotemporal reconstruction” and it can help us understand how cells change state and how cell fate decisions are made. Furthermore, many systems contain lineages that share a common initial state but branch and terminate at different states. These complicated lineage structures require additional analysis to distinguish between cells that fall along different lineages.
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 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.
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 users 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 enjoins individual cells according to a defined 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 quantitative polymerase chain reaction (qPCR) data, and implements Census and BEAM tools.
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.
Serves for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. LINNAEUS is a program that includes functionalities for detecting the origin of novel cell types, or known cell types under different conditions. This program has been used for reconstructing developmental lineage trees in zebrafish larvae, and in heart, liver, pancreas, and telencephalon of adult fish.
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.
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.
Assists users in comparing expression dynamics along single-cell trajectories describing different conditions. cellAlign can be used for global alignment to quantify the overall similarity in expression dynamics, or for local alignment to find specific areas of highly conserved expression. This method works by calculating a matrix of pairwise distances between ordered points along the two trajectories in marker space, and then finds the optimal alignment.
Identifies stem cells among all detectable cell types within a population. StemID is an algorithm based on RaceID2 (Rare Cell Type Identification) an algorithm for the identification of rare and abundant cell types from single cell transcriptome data. The method is based on transcript counts obtained with unique molecular identifies. StemID is an algorithm for the derivation of cell lineage trees based on RaceID2 results and predicts multipotent cell identities.
Establishes the position of a given cell in developmental continuum. NBOR measures the similarity of each single cell’s gene-expression profile to a defined gene set of a particular cell population. It arranges then each cell according to the similarity score into a spatial continuum around the cell population. This tool can be used to create an unsupervised visualization of the single-cell mRNA profiles into a linear developmental order.
Allows reconstruction of stem cell lineage priming from single cell data. STEMNET is an R package used for estimating the progression of single stem cells to distinct lineages. The software estimates the direction and amount of priming for each stem cell and then creates visualizations of the lineage priming process.
Allows inference of arbitrarily complex branching and recombination processes. B-RGPs is a comprehensive framework that infers branching and recombination processes which are arbitrarily complex both in terms of the number of branches, and richness of the underlying dynamics. The software focuses on transcriptional branching, but framework is equally amenable to investigate the dynamics of other branching processes.
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.
Resolves multi-branching single-cell developmental trajectories, allowing for multiple cell fates stemming from a single progenitor cell type. Mpath is an algorithm able to predict individual cell’s fate using signature genes of differentiated cell types. Via neighborhood-based cell state transition mapping, Mpath is also able to resolve complex nonlinear developmental lineages that could not be explained by pair-wise transcriptional dissimilarities.
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.
Visualizes developmental trajectories and discovers hidden cell populations from time series single cell expression data. TCM is a prototype-based dimension reduction algorithm that preserves the global developmental trajectories over a specified time course and identifies subpopulations of cells within each time point. The software overcomes the problems regarding the balance between the capability of preserving the global structure of gene expression and the sensitivity of discovering subpopulations of cells.
Explores the subpopulation structure of single-cell omics data to reconstruct trajectories of complex transitions between cell states. CellRouter is a single-cell analysis platform that introduces the concept of subpopulation-awareness to identify cellular trajectories and gene expression dynamics between any subpopulations. The software uses a network representation of cell−cell relationships learned from a low-dimensional embedding. It was used for reconstructing multi-lineage differentiation dynamics during erythroid, myeloid, and lymphoid differentiation from hematopoietic stem and progenitor cell (HSPCs).
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.