Metadata-Version: 2.1
Name: somde
Version: 0.1.8
Summary: Algorithm for finding gene spatial pattern based on Gaussian process accelerated by SOM
Home-page: https://github.com/WhirlFirst/somde
Author: Minsheng Hao
Author-email: hmsh653@gmail.com
License: MIT
Description: 
        # SOMDE ![pypi](https://img.shields.io/pypi/v/somde)
        Algorithm for finding gene spatial pattern based on Gaussian process accelerated by SOM
        
        ## Install
        
        ```bash
        conda install -c conda-forge somoclu
        conda install pandas
        conda install patsy
        pip install somde
        ```
        
        ## Data
        Slide-seq data we used can be downloaded from SpatialDB website:
        http://www.spatialomics.org/SpatialDB/download.php
        
        ## Tutorial
        
        
        ### load data
        ```python
        df = pd.read_csv(dataname+'count.csv',sep=',',index_col=1)
        corinfo = pd.read_csv(dataname+'idx.csv',sep=',',index_col=0)
        corinfo["total_count"]=df.sum(0)
        X=corinfo[['x','y']].values.astype(np.float32)
        ```
        After data loading, we can generate a SOM on the tissue spatial domain.
        ### build SOM
        ```python
        from somde import SomNode
        som = SomNode(X,20)
        ```
        You can use `som.view()` to visualize the distribution of all SOM nodes.
        
        ### integrate data sites and expression
        ```python
        ndf,ninfo = som.mtx(df)
        ```
        `mtx`function will generate pesudo gene expression and spatial data site information at reduced resolution.
        
        ### normalize data and identify SVgenes
        Since we integrated the original count data, we need to normalize gene expression matrix in each `SomNode` object.
        ```python
        nres = som.norm()
        result, SVnum =som.run()
        ```
        The identification step is mainly based on the adjusted Gaussian Process, which was first proposed by [SpatialDE](https://github.com/Teichlab/SpatialDE).
        Visualization results can be found at https://github.com/WhirlFirst/somde/blob/master/slide_seq0819_11_SOM.ipynb 
        
        
        
Keywords: spatial transcriptomics,SpatialDE,bioinformatics,self organizing map(SOM)
Platform: UNKNOWN
Description-Content-Type: text/markdown
