Quick start

Set up environment

# clone the github repo and then type the following in the shell
conda env create -f environment.yml
# activate the enviornment 
conda activate pastaenv

A quick example

The example dataset can be downloaded from our github page under the folder example_data. The data can be extracted by

import pickle
file = open('test.pkl', 'rb')
sp_adata = pickle.load(file)
sc_adata = pickle.load(file)
cluster = pickle.load(file)
coords = pickle.load(file)
pthw_genes = pickle.load(file)
file.close()

The ST dataset (sp_adata) contains 15823 cells and 133 genes. The scRNA-seq dataset (sc_adata) contains 3000 cells and 712 genes. Both of them are in h5ad format. The cell type annotation and coordinates of the ST dataset can be found in cluster and coords. pthw_genes is a list of genes from a GOBP pathway.

Then we can run the analysis using

import os
sys.path.append('./pasta')
import __init__
import _version
import optimizer
import mapper
import utils

mapper.pp_adatas(sc_adata, sp_adata)
ad_map = mapper.mapping(sc_adata, sp_adata, genes, sp_coords=coords, ncell_thres=10,
    sp_celltypes=cluster["Cluster"], lambda_g2=2, num_epochs=500)
pthw_exp = utils.project_genes(adata_map=ad_map, adata_sc=sc_adata, pthw=genes)

If you would like to run the imputation for multiple pathways, you can use the run_batch function.

# we still use the pickle file as an example
pathway = ["p1"] * (len(pthw) // 2) + ["p2"] * (len(pthw) - len(pthw) // 2)
pthw_d = pd.DataFrame({'gene': pthw, 'pathway': pathway}) # input to the run_batch function

# the pathway input to the run_batch function should be a dataframe contains two columns with name as "genes" (gene names) and "pathway" (pathway names)
result = utils.run_batch(sc_adata, sp_adata, pthw_d, sp_coords=coords, ncell_thres=10, lambda_1=1, lambda_2=1, lambda_3=1, lambda_4=1,
    sp_celltypes=cluster["Cluster"], num_epochs=500, folder="./test/")
# we ask users to provide a folder to write each pathway as a csv file