Metadata-Version: 2.1
Name: spatial_utils
Version: 0.0.1
Summary: a collection of utilities for spatial-omics analysis
Home-page: https://github.com/kevinyamauchi/spatial-utils
Author: Kevin Yamauchi
Author-email: kevin.yamauchi@gmail.com
License: BSD-3
Description: # spatial-utils
        a collection of utilities to support analysis with [squidpy](https://github.com/theislab/squidpy).
        
        # installation
        ## easy installation
        You can use our environment.yaml file to create an anaconda environment with spatial-utils, squidpy, and all other dependencies. To do so, first download the environment.yaml file from this repo. Then, open a terminal, navigate to the directory you have downloaded the `environment.yaml` file to and run the following command
        
        ```bash
        conda env create -f environment.yaml
        ```
        
        This creates an environment called spatial-analysis. If you would like to use the environment, you can activate the environment with the following:
        
        ```bash
        conda activate spatial-analysis
        ```
        
        ## development installation
        Create an environment for working on your squidpy projects. If you are using anaconda, you can enter the following in your terminal:
        
        ```bash
        conda create -n squidpy python=3.8
        ```
        
        Activate your newly created squidpy environment. For anaconda you can enter:
        
        ```bash
        conda activate squidpy
        ```
        
        Navigate to the directory you would like to download the squidpy-utils to. For example, if you want to clone the repository to your Documents folder, you would enter:
        
        ```bash
        cd ~/Documents
        ```
        
        Clone the squidpy-utils repository
        
        ```bash
        git clone https://github.com/kevinyamauchi/spatial-utils.git
        ```
        
        Navigate to the spatial-utils directory
        
        ```bash
        cd spatial-utils
        ```
        
        Install `spatial-utiils`. If you would like to be able to add/edit functionality, install in editable mode:
        
        ```bash
        pip install -e .
        ```
        
        Otherwise, perform a standard installation:
        
        ```bash
        pip install .
        ```
        
        # Usage
        ### Loading a visium dataset
        
        You can load a dataset that was processed with Kallisto into an AnnData object using the `load_visium_kallisto` function:
        
        ```python
        from spatial_utils import load_visium_kallisto
        
        adata = load_visium_kallisto(
            counts_table,
            gene_names,
            barcodes,
            tissue_positions_list,
            scale_factors,
            hires_im,
            lowres_im,
            library_id,
            chemistry_name
        )
        ```
        The input arguments are explained below. To see an example of the function being used, please see the notebook at `/examples/load_visium_from_kallisto.ipynb`
        
        ```
        Parameters
        ----------
        counts_table : str
                The path to the counts table output from Kallisto.
                This file usually has the extension ".mtx".
        gene_names : str
            The path to the file containing the gene names for the
            Kallisto counts table. This file usually ends with "genes.txt".
        barcodes : str
            The path to the file containing the spot barcodes for the
            Kallisto counts table. This file usually ends with "barcodes.txt".
        tissue_positions_list : str
            The path to the file containing the coordinates of each barcode
            that is output from the 10X space ranger pipeline.
            This file is usually called: "tissue_positions_list.csv".
        scale_factors : str
            The path to the file output by the 10X space ranger pipeline
            containing the scale factors that map the hires and
            lowres image to the original image.
            This file is usually called: "scalefactors_json.json"
        hires_im : str
            The path to the hires image that is output from the
            10X space ranger pipeline.
            This file is usually called: tissue_hires_image.png
        lowres_im : str
            The path to the lowres image that is output from the
            10X space ranger pipeline.
            This file is usually called: tissue_lowres_image.png
        library_id  : str
            The unique identifier for the library that was sequenced.
        chemistry_name : str
            The name of the chemistry used to create the library.
            The default value is: "Spatial 3' v1"
        ```
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Testing
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: BSD License
Requires-Python: >=3.7
Description-Content-Type: text/markdown
