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

By Philipp Angerer, Laleh Haghverdi, Maren Büttner, Fabian J Theis, Carsten Marr, Florian Buettner. Bioinform., 2015

621 citations recorded by the source corpus.

UNLABELLED : Diffusion 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. AVAILABILITY AND IMPLEMENTATION destiny is an open-source R/Bioconductor package "bioconductor.org/packages/destiny" also available at www.helmholtz-muenchen.de/icb/destiny A detailed vignette describing functions and workflows is provided with the package. CONTACT carsten.marr@helmholtz-muenchen.de or f.buettner@helmholtz-muenchen.de SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Subfields: Biophysics, Modeling and Simulation, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8831d66e509fe7022e17ec191b06d9005b8eb1e6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

In Molecular Biology

Citations in the corpus

The Human Cell Atlas (2017, background): “…provide proofs-of-principle for how simultaneous and orthogonal biological processes can be inferred from single-cell RNA-seq data (Figure 3) (Angerer et al., 2016; Bendall et al., 2014; Chen et al., 2016b; Haghverdi et al., 2015; Haghverdi et al., 2016; Lönnberg et al., 2017; Marco et al.,…”
Fundamental limits on dynamic inference from single-cell snapshots (2017, background): “3 to model trajectories (10, 13, 20) were limited to one or two dimensions, far below the intrinsic dimensionality of typical scRNA-seq data (24).”
Toward Minimal Residual Disease-Directed Therapy in Melanoma. (2018, positive): “…Trapnell et al., 2014 https://bioconductor.org/packages/ release/bioc/html/monocle.html Destiny diffusion maps Angerer et al., 2016 http://bioconductor.org/packages/ release/bioc/html/destiny.html Scater McCarthy et al., 2017…”
Batch effects in single-cell RNA sequencing data are corrected by matching mutual nearest neighbours (2018, result): “A diffusion map [19] of the MNN corrected data (Supplementary Figure 3 f-h) shows the same differentiation hierarchy of cell types as observed in Figure 3e.”
SCANPY: large-scale single-cell gene expression data analysis (2018, methodology): “Specifically, SCANPY provides preprocessing comparable to SEURAT [10] and CELL RANGER [6], visualization through TSNE [11, 12], graph-drawing [13– 15] and diffusion maps [11, 16, 17], clustering similar”
CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets (2017, neutral): “, 2015) via the destiny package (Angerer et al., 2016)), and SIMLR (Wang et al.”
Structural Remodeling of the Human Colonic Mesenchyme in Inflammatory Bowel Disease (2018, methodology): “Diffusion maps and diffusion pseudotime Diffusion maps and diffusion pseudotime analysis was performed with the R package ‘destiny’ (Angerer et al., 2016).”
Interpretable dimensionality reduction of single cell transcriptome data with deep generative models (2017, methodology): “For analysis, dimensionality reduction projecting high-dimensional data into low-dimensional space (typically two or three dimensions) to visualize the cluster structures 27 – 29 and development trajectories 30 – 33 is commonly used.”