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paper_figs.py
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import pandas as pd
from viz.data import read_interactions, read_ct_data, prune_ct_data
from viz.figures import pseudotime_interaction_propagation_graph, bipartite_graph, polar_receptor_figure, \
cascading_effects_figure
from viz.util import mouse_colors
def make_pairwise_fig():
interactions = read_interactions("data/mouse_ranked_interactions.tsv")
relevant_interactions = pd.read_excel("links_tabular_minInt10_fdr25_logFC0p12_EE.xlsx", sheet_name="tumor-ligands-shaping-TME")
relevant_interactions['cell_type_receptor'] = relevant_interactions['cell type_receptor']
relevant_interactions['cell_type_ligand'] = relevant_interactions['cell type_ligand']
# Select only interactions that are in the relevant interactions
interactions = interactions.merge(relevant_interactions,
on=['cell_type_ligand', 'cell_type_receptor', "ligand", "receptor"],
how="inner", suffixes=("", "_y"))
fig = bipartite_graph(
df=interactions,
cell1="Tumor cells",
cell2="Macrophages/mMDSC",
cell3=None,
numInteractions=0,
min_logfc_bipartite=0,
logfc_fdr_bipartite_cutoff=1
)
fig.write_image("pairwise.png")
fig.write_image("pairwise.svg")
fig.write_html("pairwise.html")
def make_ligand_spread_fig():
# TODO: Radial plots for each receptor/cell type, with ligands as spokes
# Then draw arrows connecting each plot to the next (positive log2FC)
adata = read_ct_data("data/mouse_deg_fixed_pruned.h5ad")
interactions = read_interactions("data/mouse_ranked_interactions.tsv")
relevant_interactions = pd.read_excel("links_tabular_minInt10_fdr25_logFC0p12_EE.xlsx",
sheet_name="tumor-ligands-shaping-TME") # sheet_name="links_tabular")
relevant_interactions['cell_type_receptor'] = relevant_interactions['cell type_receptor']
relevant_interactions['cell_type_ligand'] = relevant_interactions['cell type_ligand']
# Select only interactions that are in the relevant interactions
interactions = interactions.merge(relevant_interactions,
on=['cell_type_ligand', 'cell_type_receptor', "ligand", "receptor"],
how="inner", suffixes=("", "_y"))
fig = pseudotime_interaction_propagation_graph(
ct=adata,
orig_df=interactions,
seed_cell="Tumor cells",
seed_ligands=["Ccl2", "S100a8", "Serpine2", "S100a9", "Cxcl1", "Apoe", "Col7a1", "Il11", "Timp1"],
iterations=25,
interaction_fdr_cutoff=1,
min_logfc=0,
logfc_fdr_cutoff=1,
min_expression=0,
layout="timeline"
)
fig.write_image("ligand_spread.png")
fig.write_image("ligand_spread.svg")
fig.write_html("ligand_spread.html")
def test_radial_plot():
adata = read_ct_data("data/mouse_deg_fixed_pruned.h5ad")
interactions = read_interactions("data/mouse_ranked_interactions.tsv")
relevant_interactions = pd.read_excel("links_tabular_minInt10_fdr25_logFC0p12_EE.xlsx",
sheet_name="links_tabular")
relevant_interactions['cell_type_receptor'] = relevant_interactions['cell type_receptor']
relevant_interactions['cell_type_ligand'] = relevant_interactions['cell type_ligand']
# Select only interactions that are in the relevant interactions
#interactions = interactions.merge(relevant_interactions,
# on=['cell_type_ligand', 'cell_type_receptor', "ligand", "receptor"],
# how="inner", suffixes=("", "_y"))
fig, _ = polar_receptor_figure(adata, interactions, "PMN/gMDSC", "Trem2", min_logfc=0.05, max_fdr=.25, main_node_color=mouse_colors["Macrophages/mMDSC"])
fig.show()
def radial_networks():
adata = read_ct_data("data/mouse_deg_fixed_pruned.h5ad")
interactions = read_interactions("data/mouse_ranked_interactions.tsv")
relevant_interactions = pd.read_excel("links_tabular_minInt10_fdr25_logFC0p12_EE.xlsx",
sheet_name="links_tabular")
relevant_interactions['cell_type_receptor'] = relevant_interactions['cell type_receptor']
relevant_interactions['cell_type_ligand'] = relevant_interactions['cell type_ligand']
# Select only interactions that are in the relevant interactions
#interactions = interactions.merge(relevant_interactions,
# on=['cell_type_ligand', 'cell_type_receptor', "ligand", "receptor"],
# how="inner", suffixes=("", "_y"))
# fig = cascading_effects_figure(adata, interactions,
# ["Ccl2", "S100a8", "Serpine2", "S100a9", "Cxcl1", "Apoe", "Col7a1", "Il11", "Timp1"],
# 'Tumor cells', min_logfc=0, max_fdr=.25, iterations=1)
#fig.show()
cascading_effects_figure(adata, interactions,
["Apoe"], 'Tumor cells',
min_logfc=0, max_fdr=.25, numSigI1=10,
iterations=2, celltype_filters=['Tumor cells', 'Macrophages/mMDSC']).show()
def main():
#prune_ct_data("data/mouse_deg_fixed.h5ad")
#make_pairwise_fig()
#test_radial_plot()
#make_ligand_spread_fig()
radial_networks()
if __name__ == '__main__':
main()