Single cell trajectory detection
-
Updated
Oct 11, 2024 - Jupyter Notebook
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.
Numerical experiments showing artifacts resulting from dimensionality reduction.
Mainly Non-Theory (Coding) Homework; Mainly Python
Add a description, image, and links to the diffusion-maps topic page so that developers can more easily learn about it.
To associate your repository with the diffusion-maps topic, visit your repo's landing page and select "manage topics."