Superscan (Supervised Single-Cell Annotation) is a Python package for annotation of human scRNA-seq data. Superscan is a supervised model that was trained on a collection of publicly available datasets that were manually labeled using corresponding CITE-seq data.
Superscan requires Python 3. The required files can be downloaded by cloning this repository into the desired directory:
git clone https://github.com/cshasha/superscan
The package dependencies can then be installed using a virtual environment:
python3 -m venv superscan_env
source superscan_env/bin/activate
pip install -r requirements.txt
Superscan is run directly from the command line. It can read data in either anndata (.h5ad) or csv format (where the csv contains a counts matrix, with genes as columns). Gene names must be gene symbols (not ENSEMBL IDs), and raw counts (not normalized) should be provided. Gene names must be unique.
usage: python superscan.py [-h] --dataset DATASET [--out_prefix OUT_PREFIX]
required arguments:
--dataset DATASET path to the dataset file
optional arguments:
-h, --help show this help message and exit
--out_prefix OUT_PREFIX output filename prefix
The only required argument is the input dataset. The output filename prefix can be specified, and will default to "predictions". The output will be written to <out_prefix>_superscan.txt. In addition to saving the results in this file, a summary of the predictions will be shown, displaying the number of cells classified to each cell type, as well as the mean normalized entropy score (an indication of the confidence of the prediction) per cell type. Normalized entropy scores less than 0.1 indicate high confidence in the classification, normalized entropy scores between 0.1 and 0.3 indicate medium confidence, and normalized entropy scores greater than 1 indicate low confidence.
Example datasets can be found in the test_datasets folder. An example command, using a dataset found at test_datasets/test_ds.h5ad, would be:
python superscan.py --dataset test_datasets/test_ds.h5ad --out_prefix test
The resulting predictions as well as corresponding entropy scores will be saved to test_superscan.csv.
The training and testing datasets can be downloaded here.