bmda-unibas / DeepArchetypeAnalysisLinks
TensorFlow-Implementation of Deep Archetypal Analysis
☆13Updated 4 years ago
Alternatives and similar repositories for DeepArchetypeAnalysis
Users that are interested in DeepArchetypeAnalysis are comparing it to the libraries listed below
Sorting:
- Creating multi-resolution embeddings and clusters from high dimensional data☆56Updated last year
- Simulate single-cell RNA-SEQ data using the Splatter statistical framework but implemented in python. In addition, simulate doublet cells…☆27Updated 3 years ago
- Work with trained factor models in Python☆35Updated 11 months ago
- Maximum mean discrepancy comparisons for single cell profiling experiments☆18Updated 3 years ago
- Archetypal Analysis network (AAnet)☆37Updated 8 months ago
- Source code for integrative nonnegative matrix factorization☆19Updated 7 years ago
- generalized principal component analysis (GLM-PCA) implemented in python☆59Updated 4 years ago
- A multi-view latent variable model with domain-informed structured sparsity for integrating noisy feature sets.☆31Updated 2 months ago
- Gaussian process regression package for counts data with negative binomial and zero-inflated negative binomial likelihoods☆22Updated 7 months ago
- Analaysis for the batch correction paper☆10Updated 5 months ago
- Multi-omics factor analysis v2☆49Updated 11 months ago
- ☆28Updated 2 years ago
- This Python package will allow you to replicate the experiments from our research on applying Optimal Transport as a similarity metric in …☆42Updated 2 years ago
- A data analysis package for high-dimensional, multi-block data.☆12Updated 3 years ago
- Conditional out-of-distribution prediction☆63Updated last year
- Single-cell multi-omics integration using Optimal Transport☆46Updated last month
- Dynamical systems methods for RNA velocity analysis☆24Updated 4 years ago
- Nonnegative spatial factorization for multivariate count data☆59Updated 2 years ago
- ☆37Updated 3 months ago
- A quantitative framework for evaluating data structure preservation by dimensionality reduction techniques☆16Updated 4 years ago
- Python code for genetic marker selection using linear programming☆42Updated last year
- Quantifying experimental perturbations at single cell resolution☆110Updated last year
- BERMUDA (Batch Effect ReMoval Using Deep Autoencoders) is a novel transfer-learning-based method for batch correction in scRNA-seq data.☆31Updated 5 years ago
- Regulatory networks with Direct Information in python☆40Updated 2 years ago
- Contrastive Poisson latent variable models (CPLVMs)☆11Updated 3 years ago
- MultiMAP for integration of single cell multi-omics☆56Updated last year
- This is the repository for the single-cell transcriptomics application of the Deep Generative Decoder (DGD), developed by the Krogh group…☆14Updated last year
- A python package for performing single NMF and joint NMF algorithms☆14Updated 2 years ago
- Spatially aware dimension reduction for spatial transcriptomics.☆56Updated 6 months ago
- Companion repository to Lause, Berens & Kobak (2021): "Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data", Gen…☆40Updated 3 years ago