SOMDE

Algorithm for finding gene spatial pattern based on Gaussian process accelerated by SOM

Install

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

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

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

ndf,ninfo = som.mtx(df)

mtxfunction 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.

nres = som.norm()
result, SVnum =som.run()

The identification step is mainly based on the adjusted Gaussian Process, which was first proposed by SpatialDE. Visualization results can be found at https://github.com/WhirlFirst/somde/blob/master/slide_seq0819_11_SOM.ipynb