Laleh Haghverdi | bioRxiv Science

bioRxiv2016

Diffusion pseudotime robustly reconstructs lineage branching

Single-cell gene expression profiles of differentiating cells encode their intrinsic latent temporal order. We describe an efficient way to robustly estimate this order according to a diffusion pseudotime, which measures transitions on all length scales between cells using diffusion-like random walks. This allows us to identify cells that undergo branching decisions or are in metastable states, and thereby genes differentially regulated at these states.

Bioinformatics↗

bioRxiv2015

destiny – diffusion maps for large-scale single-cell data in R

SummaryDiffusion maps are a spectral method for non-linear dimension reduction and have recently been adapted for the visualization of single cell expression data. Here we present destiny, an efficient R implementation of the diffusion map algorithm. Our package includes a single-cell specific noise model allowing for missing and censored values. In contrast to previous implementations, we further present an efficient nearest-neighbour approximation that allows for the processing of hundreds of thousands of cells and a functionality for projecting new data on existing diffusion maps. We exemplarily apply destiny to a recent time-resolved mass cytometry dataset of cellular reprogramming.\n\nAvailability and implementationdestiny is an open-source R/Bioconductor package http://bioconductor.org/packages/ destiny also available at https://www.helmholtz-muenchen.de/icb/destiny. A detailed vignette describing functions and workflows is provided with the package.\n\nContactcarsten.marr@helmholtz-muenchen.de, f.buettner@helmholtz-muenchen.de

Bioinformatics↗