Single cell trajectory detection
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Updated
May 15, 2024 - Jupyter Notebook
Single cell trajectory detection
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
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
This toolbox allows the implementation of the following diffusion-based clustering algorithms on synthetic and real datasets.
R package for single cell and other data analysis using diffusion maps
Discovering Conservation Laws using Optimal Transport and Manifold Learning
Diffusions maps using JAX
Numerical experiments showing artifacts resulting from dimensionality reduction.
A library for diffusion maps (Numerical software development project)
Fast computation of diffusion maps and geometric harmonics in Python. Moved to https://git.sr.ht/~jmbr/diffusion-maps
Mainly Non-Theory (Coding) Homework; Mainly Python
Sampling-based approach to analyse neural networks using TensorFlow
Diffusion map of cistercian abbeys
Single cell trajectory detection
(GA_CBC) http://gdev.tv/cbcgithub
Diffusion Net TensorFlow implementation
Matlab implementation of Diffusion Maps
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