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.
Infers multi-dimensional pseudotimes. cycleX provides an analysis workflow and integrates Gaussian Process Latent Variable Model (GPLVM) analysis using multiple categories of genes. In this model, GPLVM allows gene expression to follow a smooth (nonlinear) function over time. It is also robust to the removal of cells, when certain group of cells are removed from the original dataset.
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.
Detects distinct subclones, assigns each single cell to a subclone, and permits reconstruction of the evolutionary tree. DENDRO is a program that uses information from single nucleotide mutations in transcribed regions and accounts for single-cell level expression stochasticity and technical noise. This program is able to cluster the cells into genetically distinct clones based on a pairwise divergence matrix, and select the number of clones based on inspection of the intra-cluster divergence curve.
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.
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.
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.