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destiny specifications


Unique identifier OMICS_09476
Name destiny
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 3.0
Computer skills Advanced
Version 1.0.0
Stability Stable
methods, Biobase
Maintained Yes


No version available


  • person_outline Buettner F.

Publication for destiny

destiny citations


Single cell full length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs

Nat Commun
PMCID: 5809388
PMID: 29434199
DOI: 10.1038/s41467-018-02866-0

[…] x < 24 000, G2M if x > 38 000; otherwise S. To compare scRT-qPCR data with RamDA-seq data with cells sampled across the ES-PrE time series, we constructed a diffusion map of scRT-qPCR data using the “destiny” R package and used DC1 as the pseudotime. […]


Integrated analysis of single cell embryo data yields a unified transcriptome signature for the human pre implantation epiblast

PMCID: 5818005
PMID: 29361568
DOI: 10.1242/dev.158501

[…] computed with the Bioconductor packages DESeq2 (), Sincell () or FactoMineR () in addition to custom scripts. t-SNE () and diffusion maps were produced with the Rtsne ( and destiny () packages, respectively. Differential expression analysis was performed with scde (), which fits individual error models for the assessment of differential expression between sample groups. […]


Single cell RNA sequencing resolves self antigen expression during mTEC development

Sci Rep
PMCID: 5766627
PMID: 29330484
DOI: 10.1038/s41598-017-19100-4

[…] endently in three different settings: 1. PCA was used as dimension reduction method, and R package pseudogp (Campbell and Yau 2016) was used to infer trajectory based on the PCA results; 2. R package Destiny was used to reduce dimension of the data with diffusion maps, and pseudogp was used to infer trajectory; 3. R package Monocle, was used to infer trajectory with the original high dimensional d […]


Differentiation dynamics of mammary epithelial cells revealed by single cell RNA sequencing

Nat Commun
PMCID: 5723634
PMID: 29225342
DOI: 10.1038/s41467-017-02001-5

[…] detected the HVGs as described above. The log-transformed (log2(count + 1)) gene counts were then used to compute the diffusion components using the ‘DiffusionMap’ function (default parameters as in destiny). In Fig. , we then focused on the luminal compartment and recomputed the diffusion map based only on the luminal cells, using the aforementioned procedure. Notably, the structure inferred by […]


Heterogeneity of human lympho myeloid progenitors at the single cell level

Nat Immunol
PMCID: 5884424
PMID: 29167569
DOI: 10.1038/s41590-017-0001-2

[…] etween the two clusters was < 0.01. Diffusion maps were used for dimensionality reduction of the single cell gene expression data. This method was implemented using the DiffusionMap function from the destiny R package with Euclidean distance, .Single cell RNA sequencing reads were aligned using G-SNAP and mapped reads were assigned to Ensembl genes (release 81) by using HTSeq. Cells with fewer tha […]


Mbd3/NuRD controls lymphoid cell fate and inhibits tumorigenesis by repressing a B cell transcriptional program

PMCID: 5626393
PMID: 28899870
DOI: 10.1084/jem.20161827

[…] eeping genes Ubc and Polr2a, along with two genes not expressed in any cells (Cd3e and Ptcra), were excluded from further analysis. Diffusion map dimensionality reduction (; ) was performed using the destiny R package () with centered cosine distance and sigma = 0.5. Pseudotime values were assigned to single cells by applying the Wanderlust algorithm () to coordinates of cells in diffusion compone […]

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destiny institution(s)
German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany; Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Garching, Germany
destiny funding source(s)
The UK Medical Research Council; the ERC and a DFG fellowship through the Graduate School of Quantitative Biosciences Munich (QBM)

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