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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
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