Metadata-Version: 2.1
Name: scprel
Version: 1.1.9
Summary: Single-cell data preprocessing for multiple samples.
Home-page: https://pypi.org/project/scprel/
Author: GPuzanov (Grigory Puzanov)
Author-email: <grigorypuzanov@gmail.com>
License: UNKNOWN
Description: ## Scprel - Single-Cell Data Preprocessing in Python
        
        ### Import scprel as:
        
            import scprel
        
        This package allows to perform basic preprocessing steps for single-cell analysis of multiple samples. It includes scrublets detection, quality control, normalization with `target_sum=1e4`, leiden clustering, annotation of cell types with [PanglaoDB](https://panglaodb.se/) database and infercnv calculations. It integrates some of the [Scanpy](https://scanpy.readthedocs.io/en/stable/), [Decoupler](https://decoupler-py.readthedocs.io/en/latest/), [Infercnvpy](https://infercnvpy.readthedocs.io/en/latest/infercnv.html) and [Anndata](https://anndata.readthedocs.io/en/latest/concatenation.html) functions. It is designed to facilitate workflow when analyzing multiple samples.
        
        ### Example of usage:
        
            scprel.scrun(names = ['sample1', 'sample2'], path = '/content/drive/MyDrive/MyDirectory/')
        
        * *names - list of sample names in your directory (.h5 format);* 
        * *path - path to the directory with samples*
        
        The result of this function is the [anndata.AnnData](https://anndata.readthedocs.io/en/stable/generated/anndata.AnnData.html#anndata.AnnData) object, compressed with [hdf5plugin](https://pypi.org/project/hdf5plugin/), with concatenated samples, filtered by 'mt' and 'ribo' genes, with annotated gene locations and annotated tumor cells based on cnv score. All immune cells in the sample are considered reference cells for infercnv calculations.
        
        ![The obs table for resulting adata file](https://raw.githubusercontent.com/ronnaug/1/Genomic_data_analysis/Example_table.png)
        
        The resulting file will be saved in your default home directory and is ready for batch correction and further analysis.
Keywords: python,jupyter notebook,single-cell,scRNA-seq,single-cell quality control,single-cell data preparation,single-cell multiple samples,samples concatenation
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown
