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.
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 computational method for extracting lineage relationships from single-cell gene expression data, and modeling the dynamic changes associated with cell differentiation. SCUBA draws techniques from nonlinear dynamics and stochastic differential equation theories, providing a systematic framework for modeling complex processes involving multi-lineage specifications.
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 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.
Improves the detection of changes in the transcriptional heterogeneity pattern of in single-cell RNA-seq data using two heterogeneity parameters: "burst proportion" and "burst magnitude", whose changes are validated using RNA FISH. Sphinx provides improved detection of transcriptional changes and new insights into stochastic and noisy nature of single cells.
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.