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diffusion-mapsHere are 34 public repositories matching this topic...Sort: Most stars
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Single cell trajectory detection
markov-chain dimensionality-reduction scrna-seq trajectory-generation diffusion-maps differentiation manifold-learning single-cell-genomics cell-fate-transitions scrna-seq-analysis
[Under development]- Implementation of various methods for dimensionality reduction and spectral clustering implemented with Pytorch
pytorch dimensionality-reduction graph-cut diffusion-maps pytorch-tutorial diffusion-distance laplacian-maps fiedler-vector pytorch-demo pytorch-numpy sorting-distance-matrix
iCellR is an interactive R package designed to facilitate the analysis and visualization of high-throughput single-cell sequencing data. It supports a variety of single-cell technologies, including scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq, and Spatial Transcriptomics (ST).
clustering dropout batch-normalization imputation scrna-seq diffusion-maps clustering-algorithm 3d umap normalization 10xgenomics cell-type-classification intractive-graph cite-seq singel-cell-sequencing pseudotime scvdj-seq icellr
R package for single cell and other data analysis using diffusion maps
A Julia package for manifold learning and nonlinear dimensionality reduction
Fast computation of diffusion maps and geometric harmonics in Python.
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
statistical-analysis thermodynamics neural-networks sampling diffusion-maps langevin-dynamics hamiltonian-dynamics
Discovering Conservation Laws using Optimal Transport and Manifold Learning
machine-learning physics chaos conservation-laws diffusion-maps manifold-learning optimal-transport jax laplacian-eigenmaps integrable-systems wasserstein-distance
The code for the MAPSS measures for source separation evaluation (ICLR, 2026)
ai mos diffusion-maps source-separation psychoacoustics speech-separation perceptual-evaluation audio-quality mert hubert wav2vec2 wavlm music-sources-separation speech-measures
Diffusion Net TensorFlow implementation
Matlab implementation of Diffusion Maps
Various embedding methods such as UMAP, tSNE, diffusion maps etc. implemented in Rust. Now with Python bindings
pyquest: diffusion analysis of transposable arrays
clustering diffusion-maps manifold-learning tensor-decomposition bi-clustering hierarchical-clustering
Single cell trajectory detection
machine-learning entropy dimensionality-reduction scrna-seq trajectory-generation diffusion-maps differentiation manifold-learning manifold-learing single-cell-genomics single-cell-rna-seq single-cell-analysis cell-fate-transitions
(GA_CBC) http://gdev.tv/cbcgithub
blender game-development game-assets diffusion-maps 3d-printing 3d blend-files uv-mapping 3d-models blender-3d game-dev 3d-modelling displacement-map datablock
This toolbox allows the implementation of the following diffusion-based clustering algorithms on synthetic and real datasets.
graph-algorithms clustering dimensionality-reduction hyperspectral-image-classification diffusion-maps spectral-clustering hyperspectral-imaging hyperspectral-images hyperspectral-image-analysis
A Python implementation of the Diffusion Maps algorithm for non-linear dimensionality reduction, designed to follow scikit-learn conventions.
A novel implementation of diffusion maps using deep learning (neural networks).
deep-learning neural-networks dimensionality-reduction diffusion-maps manifold-learning deep-diffusion-maps
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