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

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.

Matlab implementation of Diffusion Maps

Numerical experiments showing artifacts resulting from dimensionality reduction.

Mainly Non-Theory (Coding) Homework; Mainly Python