A collection of scripts and tools for loading, processing, and handling single cell data.
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Tools for loading and preprocessing biological matrices in Python and MATLAB.



Scprep is available on pip. Install by running the following in a terminal::

pip install --user scprep

Alternatively, scprep can be installed using Conda (most easily obtained via the Miniconda Python distribution)::

conda install -c bioconda scprep

Quick Start

You can use scprep with your single cell data as follows::

import scprep
# Load data
data_path = "~/mydata/my_10X_data"
data = scprep.io.load_10X(data_path)
# Remove empty columns and rows
data = scprep.filter.remove_empty_cells(data)
data = scprep.filter.remove_empty_genes(data)
# Filter by library size to remove background
scprep.plot.plot_library_size(data, cutoff=500)
data = scprep.filter.filter_library_size(data, cutoff=500)
# Filter by mitochondrial expression to remove dead cells
mt_genes = scprep.utils.get_gene_set(data, starts_with="MT")
scprep.plot.plot_gene_set_expression(data, mt_genes, percentile=90)
data = scprep.filter.filter_gene_set_expression(data, mt_genes, 
# Library size normalize
data = scprep.normalize.library_size_normalize(data)
# Square root transform
data = scprep.transform.sqrt(data)


Read the docs at If you have any questions or require assistance using scprep, please read the documentation at https://scprep.readthedocs.io/ or contact us at https://krishnaswamylab.org/get-help