SCIITensor

SCIITensor is a Python tool designed to decipher tumor microenvironment by deconvoluting spatial cellular interaction intensity.

Installation

git clone https://github.com/STOmics/SCIITensor.git

cd SCIITensor

python setup.py install

Tutorial

Single sample analysis

import SCIITensor as sct
import scanpy as sc
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
import pickle

adata = sc.read("/data/work/LR_TME/Liver/LC5M/sp.h5ad")
lc5m = sct.core.scii_tensor.InteractionTensor(adata, interactionDB="/data/work/database/LR/cellphoneDB_interactions_add_SAA1.csv")
sct.core.scii_tensor.build_SCII(lc5m)
sct.core.scii_tensor.process_SCII(lc5m, bin_zero_remove=True, log_data=True)
sct.core.scii_tensor.eval_SCII_rank(lc5m)
sct.core.scii_tensor.SCII_Tensor(lc5m)
with open("LC5M_res.pkl", "wb") as f:
    pickle.dump(lc5m, f)


# Visualization
## heatmap
sct.core.scii_tensor.plot_tme_mean_intensity(lc5m, tme_module = 0, cellpair_module = 2, lrpair_module = 4,
    n_lr = 15, n_cc = 5,
    figsize = (10, 2), save = False, size = 2, vmax=1)
factor_cc = lc5m.cc_factor.copy()
factor_cc.columns = factor_cc.columns.map(lambda x: f"CC_Module {x}")

factor_lr = lc5m.lr_factor.copy()
factor_lr.columns = factor_lr.columns.map(lambda x: f"LR_Module {x}")

factor_tme = pd.DataFrame(lc5m.factors[2])
factor_tme.columns = factor_tme.columns.map(lambda x: f"TME {x}")

#draw the heatmap based on the cell-cell factor matrix
fig = sns.clustermap(factor_cc.T, cmap="Purples", standard_scale=0, metric='euclidean', method='ward', 
                     row_cluster=False, dendrogram_ratio=0.05, cbar_pos=(1.02, 0.6, 0.01, 0.3),
                     figsize=(24, 10),
                     )
fig.savefig("./factor_cc_heatmap.pdf")

#select the top ligand-receptor pairs, then draw the heatmap based on ligan-receptor factor matrix
lr_number = 120 #number of ligand-receptor pairs on the top that will remain
factor_lr_top = factor_lr.loc[factor_lr.sum(axis=1).sort_values(ascending=False).index[0:lr_number]]
fig = sns.clustermap(factor_lr_top.T, cmap="Purples", standard_scale=0, metric='euclidean', method='ward', 
                     row_cluster=False, dendrogram_ratio=0.05, cbar_pos=(1.02, 0.6, 0.01, 0.3),
                     figsize=(28, 10),
                     )
fig.savefig("./factor_lr_heatmap.pdf")

## sankey
core_df = sct.plot.sankey.core_process(lc5m.core)
sct.plot.sankey.sankey_3d(core_df, link_alpha=0.5, interval=0.001, save="sankey_3d.pdf")

## circles
interaction_matrix = sct.plot.scii_circos.interaction_select(lc5m.lr_mt_list, factor_cc, factor_lr, factor_tme, 
                               interest_TME='TME 0',
                               interest_cc_module='CC_Module 3',
                               interest_LR_module='LR_Module 4',
                               lr_number=20,
                               cc_number=10)

plt.figure(figsize=(8, 3))
sns.heatmap(interaction_matrix, vmax=1)

#Draw the circos diagram, which includes cell types, ligand-receptor genes, and the links between ligands and receptors.
cells = ['Hepatocyte', 'Fibroblast', 'Cholangiocyte', 'Endothelial', 'Macrophage', 'Malignant', 'B_cell', 'T_cell', 'DC', 'NK'] #list contains names of all cell types
sct.plot.scii_circos.cells_lr_circos(interaction_matrix, cells, save="cells_lr_circos.pdf")

#Draw the circos which only contains cell types and the links between them.
sct.plot.scii_circos.cells_circos(interaction_matrix, cells, save="cells_circos.pdf")

#Draw circos which only contains ligand-receptor genes
sct.plot.scii_circos.lr_circos(interaction_matrix, cells)

## igraph
sct.plot.scii_net.grap_plot(interaction_matrix, cells,
                   save="igrap_network.pdf")

cc_df = sankey.factor_process(lc5m.factors[0], lc5m.cellpair)
sct.plot.sankey.sankey_2d(cc_df)

Multiple sample analysis

adata_LC5P = sc.read("/data/work/LR_TME/Liver/LC5P/FE1/cell2location_map/sp.h5ad")
lc5p = sct.core.scii_tensor.InteractionTensor(adata_LC5P, interactionDB="/data/work/database/LR/cellphoneDB_interactions_add_SAA1.csv")
sct.core.scii_tensor.build_SCII(lc5p)
sct.core.scii_tensor.process_SCII(lc5p)
sct.core.scii_tensor.eval_SCII_rank(lc5p)
sct.core.scii_tensor.SCII_Tensor(lc5p)
with open('LC5P_res.pkl', "wb") as f:
    pickle.dump(lc5p, f)


adata_LC5T = sc.read("/data/work/LR_TME/Liver/LC5T/FD3/cell2location_map/sp.h5ad")
lc5t = sct.core.scii_tensor.InteractionTensor(adata_LC5T, interactionDB="/data/work/database/LR/cellphoneDB_interactions_add_SAA1.csv")
sct.core.scii_tensor.build_SCII(lc5t)
sct.core.scii_tensor.process_SCII(lc5t)
sct.core.scii_tensor.eval_SCII_rank(lc5t)
sct.core.scii_tensor.SCII_Tensor(lc5t)
with open('LC5T_res.pkl', "wb") as f:
    pickle.dump(lc5t, f)

## merge data
all_data = sct.core.scii_tensor.merge_data([lc5t, lc5m, lc5p], patient_id = ['LC5T', 'LC5M', 'LC5P'])
sct.core.scii_tensor.SCII_Tensor_multiple(all_data)

## heatmap
mpl.rcParams.update(mpl.rcParamsDefault)
sct.core.scii_tensor.plot_tme_mean_intensity_multiple(all_data, sample='LC5T',
                                                     tme_module=0, cellpair_module=0, lrpair_module=0, vmax=1)