OCAT provides a fast and memory-efficient framework for analyzing and integrating large-scale scRNA-seq data. Our paper is now published in Genome Biology!!!
Check out rOCAT to use OCAT in R!
OCAT constructs sparse representation of cell features through ghost cells in the datasets. These ghost cells serve as bridges to inform on cell-cell similarity between the original cells. With the sparse features extracted, OCAT provides an efficient framework for cell type clustering and dataset integration that achieves state-of-the-art performance.
- Linux/Unix
- Python 3.7
Install OCAT package from PyPI. Pre-installation of Numpy and Cython required.
$ pip install numpy
$ pip install OCAT- Clustering and Differential Gene Analysis of Mouse Brain scRNA-seq Data (Zeisel et al. 2015)
- Integration of 6 Human Pancreatic scRNA-seq Datasets
- Clustering of Spatial scRNA-seq Data
- Trajectory and pseudotime inference using HSMM dataset
- Cell Inference of new incoming data based on reference dataset