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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
:
- Data shouldn't be hidden in a complex and bespoke class object.
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. - Your analysis pipeline shouldn't have to change based on data format. Changing from a
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. - Simple analysis should mean simple code.
scprep
takes care of annoying edge cases and sets nice defaults so you don't have to. - Using a framework shouldn't be limiting. Because nothing is hidden from you, you have access to the power of
numpy
, scipy
, pandas
and matplotlib
just as you would if you used them directly.
Installation
preprocessing is available on pip
. Install by running the following in a terminal::
pip install --user scprep
Alternatively, 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 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.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)
Examples
Scatter plots <https://scprep.readthedocs.io/en/stable/examples/scatter.html>
_Jitter plots <https://scprep.readthedocs.io/en/stable/examples/jitter.html>
_
Help
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