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Protocols

destiny specifications

Information


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
Requirements
methods, Biobase
Maintained Yes

Versioning


No version available

Maintainer


  • person_outline Buettner F. <>

Publication for destiny

destiny citations

 (6)
library_books

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

2018
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 (github.com/jkrijthe/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. […]

library_books

Single cell RNA sequencing resolves self antigen expression during mTEC development

2018
PMCID: 5766627
PMID: 29330484
DOI: 10.1038/s41598-017-19100-4

[…] the trajectory shown in fig. . the inferred curve showed a pattern of differentiation from jtec to mtechi to mteclo. we then used a diffusion map based non-linear dimension reduction implemented in destiny for pseudogp, and also got a clear trajectory showing the same jtec-mtechi-mteclo pattern (supplementary figure ). finally, we used monocle2 which does not take our specified dimension […]

call_split

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

2017
PMCID: 5723634
PMID: 29225342
DOI: 10.1038/s41467-017-02001-5
call_split See protocol

[…] 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 […]

library_books

Heterogeneity of human lympho myeloid progenitors at the single cell level

2017
PMCID: 5884424
PMID: 29167569
DOI: 10.1038/s41590-017-0001-2

[…] 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 […]

library_books

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

2017
PMCID: 5626393
PMID: 28899870
DOI: 10.1084/jem.20161827

[…] 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 […]

library_books

An Immune Atlas of Clear Cell Renal Cell Carcinoma

2017
PMCID: 5422211
PMID: 28475899
DOI: 10.1016/j.cell.2017.04.016

[…] to clusters using the phenograph classification procedure as previously described ()., to assess single-cell trajectories among tams, we used the r implementation of the diffusion map algorithm destiny (). a maximum of 2,000 cells randomly selected from each cluster were included in the analysis. diffusion distances were calculated based on arcsinh transformed data (cofactor of 5). […]


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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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