mute=True
)
# score graphs, returns a vector of ranks by similarity
graph ranks = pcreode.pCreode_Scoring(data = pca_reduced_data, file_path = "/ti/workspace/.", num_graphs = params["num_runs"], mute=True)
# select most representative graph
gid = graph_ranks[0]
# extract cell graph
# Wrapper's note: This is actually a cluster graph and a grouping, but none of the objects contain this grouping
# the only thing that is available is a cell graph of only a subset of cells
# so we use this cell graph as milestone network, and then project all cells onto this
analysis = pcreode.Analysis(
file_path = "/ti/workspace/.",
graph_id = gid,
data = pca_reduced_data,
density = density,
noise = params["noise"]
)
checkpoints["method_aftermethod"] = time.time()
# ____________________________________________________________________________
# Save output ####
# save cell_ids
cell_ids = pd.DataFrame({
"cell_ids": expression.index
})
cell_ids.to_csv("/ti/output/cell_ids.csv", index=False)
# save dimred
mute = True ) # score graphs, returns a vector of ranks by similarity graph_ranks = pcreode.pCreode_Scoring(data = pca_reduced_data, file_path = "/", num_graphs = parameters["num_runs"], mute=True) # select most representative graph gid = graph_ranks[0] # extract cell graph # Wrapper's note: This is actually a cluster graph and a grouping, but none of the objects contain this grouping # the only thing that is available is a cell graph of only a subset of cells # so we use this cell graph as milestone network, and then project all cells onto this analysis = pcreode.Analysis( file_path = "/", graph_id = gid, data = pca_reduced_data, density = density, noise = noise ) checkpoints["method_aftermethod"] = time.time() # ____________________________________________________________________________ # Save output #### dataset = dynclipy.wrap_data(cell_ids = expression.index) # save dimred dimred = pd.DataFrame(pca_reduced_data) dimred["cell_id"] = expression.index # get milestone network based on cell_graph