Algorithm for finding gene spatial pattern based on Gaussian process accelerated by SOM
Project description
SOMDE
Algorithm for finding gene spatial pattern based on Gaussian process accelerated by SOM
Install
pip install numpy
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)
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.
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
Project details
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