Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc. List of software packages (and the people developing these methods) for single-cell data analysis, including RNA-seq, ATAC-seq, etc. Rapid, accurate and memory-frugal preprocessing of single-cell and single-nucleus RNA-seq data. Find bimodal, unimodal, and multimodal features in your data. Ascend is an R package comprised of fast, streamlined analysis functions optimized to address the statistical challenges of single cell RNA-seq. The package incorporates novel and established methods to provide a flexible framework to perform filtering, quality control, normalization, dimension reduction, clustering, differential expression and a wide-range of plotting. An analytical framework for big-scale single cell data. Transform percentage-based units into a 2d space to evaluate changes in distribution with both magnitude and direction.
Features
- Find bimodal, unimodal, and multimodal features in your data
- An analytical framework for big-scale single cell data
- Cell population analysis and visualization from single cell RNA-seq data using a Latent Dirichlet Allocation model
- Representation Learning for detection of phenotype-associated cell subsets
- Bayesian pseudotime estimation algorithms
- Basic PCA-based workflow for analysis and plotting of single cell RNA-seq data
- And more
Project Samples
