Metadata-Version: 1.2
Name: svgbit
Version: 0.2.4
Summary: UNKNOWN
Home-page: https://github.com/CPenglab/svgbit
Author: CPenglab
Author-email: chengpeng@ynu.edu.cn
License: MIT
Description: #################
        svgbit quickstart
        #################
        For further detail, please visit our `API reference`_ on readthedocs.org
        
        
        
        Installation
        ============
        Install svgbit with ``pip``::
        
            pip install svgbit
        
        
        
        Command-line Interface
        ======================
        svgbit has a command line version. Just tape::
        
            svgbit --help
        
        after installation, and you may get a short help massage::
        
            usage: svgbit [-h] [--k K] [--n_svgs N_SVGS] [--n_svg_clusters N_SVG_CLUSTERS]
                          [--he_image HE_IMAGE] [--savedir SAVEDIR] [--cores CORES] read_dir
        
            Find spatial variable genes for Spatial Trasncriptomics data.
        
            positional arguments:
            read_dir              a location points to 10X outs dir. Assume directories
                                  ``filtered_feature_bc_matrix`` and ``spatial`` are in this path.
        
            optional arguments:
            -h, --help            show this help message and exit
            --k K                 number of nearest neighbors for KNN network (default: 6)
            --n_svgs N_SVGS       number of SVGs to find clusters (default: 1000)
            --n_svg_clusters N_SVG_CLUSTERS
            number of SVG clusters to find (default: 8)
            --he_image HE_IMAGE   path to H&E image. Only used for visualization (default: None)
            --savedir SAVEDIR     path to save results (default: .)
            --cores CORES         number of threads to run svgbit (default: 8)
        
        Follow the introduction and results will save to --savedir.
        
        .. note::
            svgbit will use all available CPUs as default. While python's child process
            will inherit all resources from parent, this may consume much memory. Specify
            ``cores`` keyword argument to avoid memory error.
        
        .. note::
           svgbit may consume ~35 Gib memory when running with a 2980 spots, 32285 genes
           matrix with 8 cores.
        
        
        
        Python Interface
        ================
        svgbit has a set of python API. You may run svgbit through command line or
        python. We recommend the usage of python API for more feature and convient
        control of your input data.
        
        
        Load Data
        ---------
        svgbit could load data from Space Ranger output directory::
        
            import svgbit as sb
            dataset = sb.load_10X("spaceranger_output/outs")
        
        Or load data from csv files::
        
            import svgbit as sb
            dataset = sb.STDataset(
                count_df="Data/count_df.csv",
                coordinate_df="Data/coor_df.csv",
                count_df_kwargs={"index_col": 0, "header": 0},
                coordinate_df_kwargs={"index_col": 0, "header": 0},
            )
        
        .. note::
            ``sb.STDataset`` also accept ``pd.DataFrame`` and ``np.array`` as
            ``count_df`` and ``coordinate_df``. If ``str`` or ``pathlib.Path`` are
            given, svgbit would try to load data with ``pandas``.
        
        .. note::
           When init STDataset instance in this way, svgbit assume that the shape
           of count matrix and coordinate file is  (spot * gene) and (spot * 2).
           Specify ``count_transpose`` or ``coordinate_transpose`` as ``True``
           when necessary.
        
        
        Data Preprocessing
        ------------------
        Genes with low variance may filter with::
        
            dataset = sb.filters.low_variance_filter(dataset, var=0)
        
        .. note::
           With var=0, genes with no expression may be filtered. We recommend
           apply a var=0 filter first for better computational performing.
        
        Genes with extremely high expressions usually show no pattern and may
        distrub performing. Filter with::
        
            dataset = sb.filters.quantile_filter(dataset, 0.99)
           
        svgbit alse has count normalization functions::
        
            dataset = sb.normalizers.logcpm_normailzer(dataset)
        
        Feel free for choosing gene filters and data normalizers. Other filters
        and normalizers are also provided. Visit our `API reference`_ for further
        detail.
        
        
        Run svgbit
        ----------
        To perform full pipeline of svgbit, running::
        
            sb.run(dataset)
        
        
        Visualization
        -------------
        Draw SVG heatmap and spot type distribution map with::
        
            sb.svg_heatmap(dataset, save_path="heatmap.jpg", he_image="he_image.jpg")
            sb.spot_type_map(dataset, save_path="spot_type.jpg", he_image="he_image.jpg")
        
        Parameter ``he_image`` is optional. If not specified, hotspot discription
        map will show without morphological information.
        
        
        Details about sb.run()
        ----------------------
        When you perform ``sb.run()``, sevaral steps will be done as below.
        For further detail of calculation, please refer to our publication.
        
        Acquire weight
        ::::::::::::::
        
        To calculate hotspot matrix, svgbit needs a weight network which discribes
        association across spots. svgbit uses k-nearest neighbors with 6 neighbors
        as a default. You may pass key word argument ``k`` to ``sb.run()`` to
        change this behavior.
        
        In this step, ``sb.run()`` will execute ``STDataset.acquire_weight()``
        method with given parameters. You may also perform this step by::
        
            dataset.acquire_weight()
        
        Weight will save as attribute ``weight`` of ``STDataset`` and detailed
        discription of weight is saved to ``weight_type`` attribute. Users may
        provide a ``libpysal.weights.W`` instance as user-specified weight::
        
            dataset.weight = user_specified_weight
        
        Acquire hotspot
        :::::::::::::::
        
        Hotspot matrix is estimated by::
        
            dataset.acquire_hotspot()
        
        and save to ``hotspot_df`` attribute.
        
        Density
        :::::::
        
        AI and Di value discribed in our paper will be calculate by::
        
            dataset.acquire_density()
        
        and save to ``AI`` and ``Di`` attribute as ``pd.Series``.
        
        Find SVG clusters
        :::::::::::::::::
        
        SVG clusters is estimated by::
        
            dataset.find_clusters()
        
        and save to ``svg_cluster`` attribute.
        
        For further discription of hotspot, AI, Di and SVG cluster, please refer to
        our manuscript.
        
        
        
        Citation
        ========
        
        
        
        .. _API reference: https://svgbit.readthedocs.io/en/latest/
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.8.12
