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scprep provides an all-in-one framework for loading, preprocessing, and plotting matrices in Python, with a focus on single-cell genomics.
The philosophy of scprep:
scprep works with numpy arrays, pandas data frames, and scipy sparse matrices, all of which are popular data formats in Python and accepted as input to most common algorithms.numpy array to a pandas data frame introduces endless technical differences (e.g. in indexing matrices). scprep provides data-agnostic methods that work the same way on all formats.scprep takes care of annoying edge cases and sets nice defaults so you don't have to.numpy, scipy, pandas and matplotlib just as you would if you used them directly.preprocessing is available on pip. Install by running the following in a terminal::
pip install --user scprepAlternatively, scprep can be installed using Conda <https://conda.io/docs/> (most easily obtained via the Miniconda Python distribution <https://conda.io/miniconda.html>)::
conda install -c bioconda scprepYou 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.select.get_gene_set(data, starts_with="MT")
scprep.plot.plot_gene_set_expression(data, genes=mt_genes, percentile=90)
data = scprep.filter.filter_gene_set_expression(data, genes=mt_genes,
percentile=90)
# Library size normalize
data = scprep.normalize.library_size_normalize(data)
# Square root transform
data = scprep.transform.sqrt(data)Scatter plots <https://scprep.readthedocs.io/en/stable/examples/scatter.html>_Jitter plots <https://scprep.readthedocs.io/en/stable/examples/jitter.html>_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