ENVI is a deep learnining based variational inference method to integrate scRNAseq with spatial sequencing data. It creates a combined latent space for both data modalities, from which missing gene can be imputed from spatial data, cell types labels can be transfered and the spatial niche can be reconstructed for the dissociated scRNAseq data
This implementation is written in Python3 and relies on tensorflow>2.4, tensorflow_probability, sklearn, scipy and scanpy.
To install PhenoGraph, simply run the setup script:
python3 setup.py install
Or use:
pip3 install git+https://github.com/DoronHav/PyENVI.git
Expected use is within a script or interactive kernel running Python 3.x. Data are expected to be passed as anndata
To run ENVI:
import PyENVI #import ENVI
model = PyENVI.ENVI(spatial_data = Spatial_anndata, sc_data = scRNAseq_anndata) #Initialize Model
model.Train() #Train VAE
model.impute() #impute missing genes for spatial data, see model.spatial_data.obsm['imputation']
model.pred_type(pred_on = 'sc')/model.pred_type(pred_on = 'spatial')# transfer cell_type labels, see model.spatial_data/sc_data.obs['cell_type']
model.reconstruct_niche(pred_key = 'cell_type') #reconstruct spatial niche for sRNAseq, see model.sc_data.obsm['niche_by_type']