txWang / BERMUDALinks
BERMUDA (Batch Effect ReMoval Using Deep Autoencoders) is a novel transfer-learning-based method for batch correction in scRNA-seq data.
☆30Updated 5 years ago
Alternatives and similar repositories for BERMUDA
Users that are interested in BERMUDA are comparing it to the libraries listed below
Sorting:
- Deep Embedding for Single-cell Clustering☆87Updated last year
- ☆11Updated 2 years ago
- scLearn：Learning for single cell assignment☆35Updated 7 months ago
- ☆45Updated 2 years ago
- Iterative transfer learning with neural network improves clustering and cell type classification in single-cell RNA-seq analysis☆54Updated last month
- Python package for analysis of multiomic single cell RNA-seq and ATAC-seq.☆63Updated last week
- Semi-supervised adversarial neural networks for classification of single cell transcriptomics data☆75Updated 6 months ago
- ☆59Updated 2 years ago
- Generative adversarial networks for single-cell RNA-seq imputation☆39Updated 4 years ago
- An unsupervised approach for the integrative analysis of single-cell multi-omics data☆29Updated 4 years ago
- cTPnet Package☆25Updated 3 years ago
- ☆25Updated 3 years ago
- Reproducing result from the paper☆34Updated 4 years ago
- ☆28Updated 5 years ago
- ☆44Updated 3 years ago
- MAGIC - imputation and de-noising single-cell RNA seq data sets☆27Updated 5 years ago
- scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics☆40Updated 2 years ago
- Simultaneous deep generative modeling and clustering of single cell genomic data☆32Updated 2 years ago
- A benchmark of batch-effect correction methods for single-cell RNA sequencing data☆77Updated 3 years ago
- SoptSC for single cell data analysis: unsupervised inference of clustering, cell lineage, pseudotime and cell-cell communication network …☆22Updated 4 years ago
- Enhancing spatial transcriptomics data by predicting the expression of unmeasured genes from a dissociated scRNA-seq data☆36Updated last year
- ☆48Updated 6 years ago
- Imputation method for scRNA-seq based on low-rank approximation☆78Updated last year
- Code for the MNN manuscript figures☆50Updated 6 years ago
- Quasilinear data representations for single-cell omics data analysis☆42Updated 2 years ago
- Deconvoluting Spatial Transcriptomics Data through Graph-based Artificial Intelligence☆39Updated 2 years ago
- ACTIONet single-cell analysis framework☆42Updated 10 months ago
- ☆16Updated 3 years ago
- Code for Universal Single-Cell Matching with Unpaired Feature Sets☆21Updated 4 years ago
- Deep learning model for single-cell inference of multi-omic profiles from a single input modality.☆42Updated 2 years ago