The fmsne R package implements the fast multi-scale neighbour embedding methods developed by Cyril de Bodt.

Fast Multi-scale Neighbor Embedding

This project and the codes in this repository implement fast multi-scale neighbor embedding algorithms for nonlinear dimensionality reduction (DR).

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:

  • C. de Bodt, D. Mulders, M. Verleysen and J. A. Lee, “Fast Multiscale Neighbor Embedding,” in IEEE Transactions on Neural Networks and Learning Systems, 2020, doi: 10.1109/TNNLS.2020.3042807.

Installation

To install this R package:

BiocManager::install("lgatto/fmsne")

The package depends on the following Bioconductor packages:

  • SingleCellExperiment for the infrastructure to hold the single-cell and reduced dimension data.

  • scater for the dimensionality reduction interface.

  • basilisk and reticulate 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 fmsne