Single cell trajectory detection
[Under development]- Implementation of various methods for dimensionality reduction and spectral clustering implemented with Pytorch
Single (i) Cell R package (iCellR) is an interactive R package to work with high-throughput single cell sequencing technologies (i.e scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST)).
A Julia package for manifold learning and nonlinear dimensionality reduction
R package for single cell and other data analysis using diffusion maps
Fast computation of diffusion maps and geometric harmonics in Python. Moved to https://git.sr.ht/~jmbr/diffusion-maps
A hands-free DTI, DKI, FBI and FBWM preprocessing pipeline. Information on algorithms and preprocessing steps are available at https://www.biorxiv.org/content/10.1101/2021.10.20.465189v1 A video tutorial on PyDesigner and its usage is now available at https://www.youtube.com/watch?v=mChQFuQqX3k
Sampling-based approach to analyse neural networks using TensorFlow
Discovering Conservation Laws using Optimal Transport and Manifold Learning
Diffusion Net TensorFlow implementation
Matlab implementation of Diffusion Maps
The code for the MAPSS measures for source separation evaluation.
pyquest: diffusion analysis of transposable arrays
A library for diffusion maps (Numerical software development project)
Single cell trajectory detection
(GA_CBC) http://gdev.tv/cbcgithub
This toolbox allows the implementation of the following diffusion-based clustering algorithms on synthetic and real datasets.
Numerical experiments showing artifacts resulting from dimensionality reduction.
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