vignettes/fmsne.Rmd
fmsne.Rmdfmsne package
The fmsne R package implements the fast multi-scale
neighbour embedding methods developed by Cyril de Bodt.
The fast algorithms which are implemented are described in the article Fast Multiscale Neighbor Embedding, from Cyril de Bodt, Dounia Mulders, Michel Verleysen and John A. Lee, published in IEEE Transactions on Neural Networks and Learning Systems, in 2020.
The implementations are provided using the python programming language, but involve some C and Cython codes for performance purposes.
If you use the codes in this repository or the article, please cite as (Bodt et al. 2022):
and this package:
citation("fmsne")
#> Warning in citation("fmsne"): could not determine year for 'fmsne' from package
#> DESCRIPTION file
#> To cite package 'fmsne' in publications use:
#>
#> Cyril de Bodt, Laurent Gatto (????). _fmsne: Fast Multi-scale
#> Neighbour Embedding_. R package version 0.8.1.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {fmsne: Fast Multi-scale Neighbour Embedding},
#> author = {{Cyril de Bodt} and {Laurent Gatto}},
#> note = {R package version 0.8.1},
#> }To install this R package, simply run
BiocManager::install("lgatto/fmsne")The package on the following Bioconductor packages:
SingleCellExperiment for the infrastructure to hold the single-cell and reduced dimension data.
basilisk to install and run the underlying Python implementation.
If you are looking to apply fast multi-scale neighbor embedding in
Pyhton, you can install the fmsne python package with
pip install fmsnerunMSSNE(): nonlinear dimensionality reduction
through multi-scale (MS) stochastic neighbor embedding (SNE) (Maaten and Hinton 2008; Van Der Maaten 2014),
as presented in the reference (Lee,
Peluffo-Ordóñez, and Verleysen 2015) below and summarized in
(Bodt et al. 2022).
runMSTSNE(): nonlinear dimensionality reduction
through multi-scale t-distributed SNE (t-SNE) (Maaten and Hinton 2008; Van Der Maaten 2014),
as presented in the reference (Bodt et al.,
n.d.) below and summarized in (Bodt et al.
2022).
runFMSSNE(): nonlinear dimensionality reduction
through fast multi-scale SNE (FMS SNE), as presented in the reference
(Bodt et al. 2022).
runFMSTSNE(): nonlinear dimensionality reduction
through fast multi-scale t-SNE (FMS t-SNE), as presented in the
reference (Bodt et al. 2022).
See the function manual pages for further details
drQuality(): unsupervised evaluation of the quality of
a low-dimensional embedding, as introduced in (Lee and Verleysen 2009, 2010) and applied and
summarized in (Bodt et al. 2022; Lee,
Peluffo-Ordóñez, and Verleysen 2015; Lee et al. 2013). This
function assesses the dimensionality reduction quality measuring the
neighborhood preservation from the high-dimensional space to the
low-dimensional one. The documentation of the function explains the
meaning of the criteria and how to interpret them.#> R version 4.3.1 (2023-06-16)
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#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
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#> other attached packages:
#> [1] BiocStyle_2.29.2
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#> loaded via a namespace (and not attached):
#> [1] vctrs_0.6.4 cli_3.6.1 knitr_1.44
#> [4] rlang_1.1.1 xfun_0.40 stringi_1.7.12
#> [7] purrr_1.0.2 textshaping_0.3.7 jsonlite_1.8.7
#> [10] glue_1.6.2 rprojroot_2.0.3 htmltools_0.5.6.1
#> [13] ragg_1.2.6 sass_0.4.7 rmarkdown_2.25
#> [16] evaluate_0.22 jquerylib_0.1.4 fastmap_1.1.1
#> [19] lifecycle_1.0.3 yaml_2.3.7 memoise_2.0.1
#> [22] bookdown_0.35 BiocManager_1.30.22 stringr_1.5.0
#> [25] compiler_4.3.1 fs_1.6.3 systemfonts_1.0.5
#> [28] digest_0.6.33 R6_2.5.1 magrittr_2.0.3
#> [31] bslib_0.5.1 tools_4.3.1 pkgdown_2.0.7.9000
#> [34] cachem_1.0.8 desc_1.4.2See also this shared bibliography (with pdfs).