scROCK (single-cell Refinement Of Cluster Knitting) is an algorithm for correcting cluster labels for scRNA-seq data, based on Xinchuan Zeng and Tony R. Martinez. 2001. An algorithm for correcting mislabeled data. Intell. Data Anal. 5, 6 (December 2001), 491–502..
pip install https://github.com/dos257/scROCK/tarball/master
For private repository use:
pip install git+https://{token}@github.com/dos257/scROCK.git
Use keys --upgrade --no-deps --force-reinstall for forced update from git repository.
If X is log1p-preprocessed numpy.array of shape (n_samples, n_genes) and y is integer clustering labels (from Leiden algorithm),
from scrock import scrock
y_fixed = scrock(X, y)For convenience, scrock supports simplified command line:
python3 -m scrock refine_clusters data.h5ad
or find_doublets instead of refine_clusters
For refine_clusters task, from file (here data.h5ad) scrock tries to read (in that order) .obs["seurat_clusters"], .obs["leiden"], .obs["cell_line"].
Also, this command line could be run inside Docker.
Build Docker image:
docker build --tag scrock-image .
Run Docker image passing host path with input file:
docker run --name scrock --volume /host-path-to-input/data:/data scrock-image refine_clusters /data/sce_sc_10x_5cl_qc.h5ad
Output will be written to stdout.
If code consumes high CPU percent (but still works slowly), try:
torch.set_num_threads(1)Torch imperfect CPU parallelization spends most of the time in thread synchronization and slows down all process.
scROCK was developed under the supervision of Dr. Vikas Bansal (Head of Biomedical Data Science Group at German Center for Neurodegenerative Diseases (DZNE), Tübingen).