Run CCCI¶

[1]:

import pandas as pd
import numpy as np
import scanpy as sc
import STCase as st

/home/user/BGM/qij/miniconda3/envs/stcase_tmp1/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
/home/user/BGM/qij/miniconda3/envs/stcase_tmp1/lib/python3.10/site-packages/torch_geometric/typing.py:54: UserWarning: An issue occurred while importing 'pyg-lib'. Disabling its usage. Stacktrace: /usr/lib64/libm.so.6: version `GLIBC_2.29' not found (required by /home/user/BGM/qij/miniconda3/envs/stcase_tmp1/lib/python3.10/site-packages/libpyg.so)
  warnings.warn(f"An issue occurred while importing 'pyg-lib'. "
During startup - Warning message:
package ‘stats’ in options("defaultPackages") was not found

[2]:

DB_interaction = pd.read_csv('/home/user/data3/qij/project/cell_communication/interaction_database/selfdb_finalv/selfdb_human.csv',index_col=0)
DB_complex = pd.read_csv('/home/user/data3/qij/project/cell_communication/interaction_database/selfdb_finalv/selfdb_complex_human.csv',index_col=0)
DATABASES_GLOB = '/home/user/data3/qij/project/cell_communication/pySCENIC/databases/human_hg38_v10/*.genes_vs_motifs.rankings.feather'
MOTIF_ANNOTATIONS_FNAME = '/home/user/data3/qij/project/cell_communication/pySCENIC/resources/motifs-v10nr_clust-nr.hgnc-m0.001-o0.0.tbl'

[3]:

adata_sp311 = sc.read_h5ad('../NG-lung/spdata/sp311_nonceco.h5ad')

Running¶

There are three ways to run the CCCI module

(1) Stringent¶

[4]:

adata_sp311_stringent = st.ccci.spatial_cell_communication_run(adata_sp311,
                                   DB_interaction,
                                   DB_complex,
                                   method='Hill',
                                   ct_key='cell_type',
                                   cell_type=None,
                                   if_hvg=False,
                                   if_filter=False,
                                   if_self=True,
                                   if_intra=True,
                                   if_stringent=True,
                                   DATABASES_GLOB=DATABASES_GLOB,
                                   MOTIF_ANNOTATIONS_FNAME=MOTIF_ANNOTATIONS_FNAME,
                                   background_number=1000,
                                   threads=10,
                                   scope=6,
                                   min_exp=0.1,
                                   cutoff=0.05)

##################################################################
Now start to calulate radius
The radius is: 220.41097976280582
##################################################################
Now start to get LR pair
The number of keep LR pair is 1866
##################################################################
get_LR_gene_exp
##################################################################
get_close_gene
##################################################################
Now start to get true weight matirx
Now processing the unsecreted
Computed weight matrix process: |██████████████████████████████| 100%
Now processing the secreted
Computed weight matrix process: |██████████████████████████████| 100%
##################################################################
Now start to permutation test
Permutation test process: |██████████████████████████████| 100%
Now filter low confidence value
Filter low confidence value process: |██████████████████████████████| 100%
##################################################################
Now  start to add intracellular signals
Run SCENIC
Phase I: Inference of co-expression modules
preparing dask client

/home/user/BGM/qij/miniconda3/envs/stcase_tmp1/lib/python3.10/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.
Perhaps you already have a cluster running?
Hosting the HTTP server on port 46744 instead

parsing input
creating dask graph
16 partitions
computing dask graph

/home/user/BGM/qij/miniconda3/envs/stcase_tmp1/lib/python3.10/site-packages/distributed/client.py:3125: UserWarning: Sending large graph of size 107.19 MiB.
This may cause some slowdown.
Consider scattering data ahead of time and using futures.

shutting down client and local cluster
finished


2024-04-11 03:59:51,484 - pyscenic.utils - INFO - Calculating Pearson correlations.

2024-04-11 03:59:51,854 - pyscenic.utils - WARNING - Note on correlation calculation: the default behaviour for calculating the correlations has changed after pySCENIC verion 0.9.16. Previously, the default was to calculate the correlation between a TF and target gene using only cells with non-zero expression values (mask_dropouts=True). The current default is now to use all cells to match the behavior of the R verision of SCENIC. The original settings can be retained by setting 'rho_mask_dropouts=True' in the modules_from_adjacencies function, or '--mask_dropouts' from the CLI.
        Dropout masking is currently set to [False].

2024-04-11 04:00:08,021 - pyscenic.utils - INFO - Creating modules.

Phase II: Prune modules for targets with cis regulatory footprints
Create regulons from a dataframe of enriched features.
Additional columns saved: []
Phase III: Cellular regulon enrichment matrix
Add intracellular signals: |██████████████████████████████| 100%
##################################################################
Now  start to aggregate
Spatial cell communication finished!

[36]:

adata_sp311_stringent.write_h5ad('./adata_sp311_stringent.h5ad')

(2) non-stringent¶

[ ]:

adata_sp311_nonstringent = st.ccci.spatial_cell_communication_run(adata_sp311,
                                   DB_interaction,
                                   DB_complex,
                                   method='Hill',
                                   ct_key='cell_type',
                                   cell_type=None,
                                   if_hvg=False,
                                   if_filter=False,
                                   if_self=True,
                                   if_intra=True,
                                   if_stringent=True,
                                   DATABASES_GLOB=DATABASES_GLOB,
                                   MOTIF_ANNOTATIONS_FNAME=MOTIF_ANNOTATIONS_FNAME,
                                   background_number=1000,
                                   threads=10,
                                   scope=6,
                                   min_exp=0.1,
                                   cutoff=0.05)

[ ]:

adata_sp311_nonstringent.write_h5ad('./adata_sp311_nonstringent.h5ad')

(3) With no regard for the downstream path¶

[ ]:

adata_sp311_nodownstram = st.ccci.spatial_cell_communication_run(adata_sp311,
                                   DB_interaction,
                                   DB_complex,
                                   method='Hill',
                                   ct_key='cell_type',
                                   cell_type=None,
                                   if_hvg=False,
                                   if_filter=False,
                                   if_self=True,
                                   if_intra=False,
                                   if_stringent=False,
                                   DATABASES_GLOB=DATABASES_GLOB,
                                   MOTIF_ANNOTATIONS_FNAME=MOTIF_ANNOTATIONS_FNAME,
                                   background_number=1000,
                                   threads=10,
                                   scope=6,
                                   min_exp=0.1,
                                   cutoff=0.05)

[ ]:

adata_sp311_nodownstram.write_h5ad('./adata_sp311_nodownstram.h5ad')

Results¶

[10]:

adata_sp311_stringent

[10]:

AnnData object with n_obs × n_vars = 2238 × 12912
    obs: 'in_tissue', 'array_row', 'array_col', 'sample', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'mt_frac', 'Perichondrium', 'Weird_morphology', 'Cartilage', 'Glands', 'Tissue', 'Multilayer_epithelium', 'Nerve', 'Venous_vessel', 'Airway_Smooth_Muscle', 'Arterial_vessel', 'Parenchyma', 'Pulmonary_vessel', 'Mesothelium', 'Small_airway', 'iBALT', '_indices', '_scvi_batch', '_scvi_labels', 'cell_type', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt'
    var: 'feature_types', 'genome', 'SYMBOL', 'mt', 'n_cells_by_counts-WSA_LngSP10193345', 'mean_counts-WSA_LngSP10193345', 'log1p_mean_counts-WSA_LngSP10193345', 'pct_dropout_by_counts-WSA_LngSP10193345', 'total_counts-WSA_LngSP10193345', 'log1p_total_counts-WSA_LngSP10193345', 'n_cells_by_counts-WSA_LngSP10193346', 'mean_counts-WSA_LngSP10193346', 'log1p_mean_counts-WSA_LngSP10193346', 'pct_dropout_by_counts-WSA_LngSP10193346', 'total_counts-WSA_LngSP10193346', 'log1p_total_counts-WSA_LngSP10193346', 'n_cells_by_counts-WSA_LngSP10193347', 'mean_counts-WSA_LngSP10193347', 'log1p_mean_counts-WSA_LngSP10193347', 'pct_dropout_by_counts-WSA_LngSP10193347', 'total_counts-WSA_LngSP10193347', 'log1p_total_counts-WSA_LngSP10193347', 'n_cells_by_counts-WSA_LngSP10193348', 'mean_counts-WSA_LngSP10193348', 'log1p_mean_counts-WSA_LngSP10193348', 'pct_dropout_by_counts-WSA_LngSP10193348', 'total_counts-WSA_LngSP10193348', 'log1p_total_counts-WSA_LngSP10193348', 'n_cells_by_counts-WSA_LngSP8759311', 'mean_counts-WSA_LngSP8759311', 'log1p_mean_counts-WSA_LngSP8759311', 'pct_dropout_by_counts-WSA_LngSP8759311', 'total_counts-WSA_LngSP8759311', 'log1p_total_counts-WSA_LngSP8759311', 'n_cells_by_counts-WSA_LngSP8759312', 'mean_counts-WSA_LngSP8759312', 'log1p_mean_counts-WSA_LngSP8759312', 'pct_dropout_by_counts-WSA_LngSP8759312', 'total_counts-WSA_LngSP8759312', 'log1p_total_counts-WSA_LngSP8759312', 'n_cells_by_counts-WSA_LngSP8759313', 'mean_counts-WSA_LngSP8759313', 'log1p_mean_counts-WSA_LngSP8759313', 'pct_dropout_by_counts-WSA_LngSP8759313', 'total_counts-WSA_LngSP8759313', 'log1p_total_counts-WSA_LngSP8759313', 'n_cells_by_counts-WSA_LngSP9258463', 'mean_counts-WSA_LngSP9258463', 'log1p_mean_counts-WSA_LngSP9258463', 'pct_dropout_by_counts-WSA_LngSP9258463', 'total_counts-WSA_LngSP9258463', 'log1p_total_counts-WSA_LngSP9258463', 'n_cells_by_counts-WSA_LngSP9258464', 'mean_counts-WSA_LngSP9258464', 'log1p_mean_counts-WSA_LngSP9258464', 'pct_dropout_by_counts-WSA_LngSP9258464', 'total_counts-WSA_LngSP9258464', 'log1p_total_counts-WSA_LngSP9258464', 'n_cells_by_counts-WSA_LngSP9258467', 'mean_counts-WSA_LngSP9258467', 'log1p_mean_counts-WSA_LngSP9258467', 'pct_dropout_by_counts-WSA_LngSP9258467', 'total_counts-WSA_LngSP9258467', 'log1p_total_counts-WSA_LngSP9258467', 'n_cells_by_counts-WSA_LngSP9258468', 'mean_counts-WSA_LngSP9258468', 'log1p_mean_counts-WSA_LngSP9258468', 'pct_dropout_by_counts-WSA_LngSP9258468', 'total_counts-WSA_LngSP9258468', 'log1p_total_counts-WSA_LngSP9258468', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts'
    uns: '_scvi_manager_uuid', '_scvi_uuid', 'cell_type_colors', 'log1p', 'mod', 'spatial', 'radius', 'LR_pair_information', 'LR_gene_complex_information', 'LR_gene_complex_exp', 'LR_close_gene', 'LR_close_gene_exp', 'LR_cell_weight', 'Cell_neighbors', 'scenic_res', 'cell_type_list', 'LR_celltype_weight', 'LR_celltype_mean_weight', 'LR_celltype_edge_num', 'LR_celltype_aggregate_weight', 'LR_pathway_celltype_weight', 'LR_pathway_celltype_mean_weight', 'LR_pathway_celltype_edge_num', 'LR_pathway_celltype_count', 'LR_pathway_cell_weight', 'data_for_LRI'
    obsm: 'means_cell_abundance_w_sf', 'q05_cell_abundance_w_sf', 'q95_cell_abundance_w_sf', 'spatial', 'stds_cell_abundance_w_sf', 'distances'

[39]:

## Read the CCC score of a ligand receptor pair at the cell/spot level
LRP_CellLevel_CCCscore = pd.DataFrame(adata_sp311_stringent.uns['LR_cell_weight']['IL6|COMPLEX:IL6R_IL6ST'].toarray(),
                                         index=adata_sp311_stringent.obs.index, columns=adata_sp311_stringent.obs.index
                                        )

[40]:

LRP_CellLevel_CCCscore.iloc[:10,:10]

[40]:

spot_id	WSA_LngSP8759311_AAACAAGTATCTCCCA-1	WSA_LngSP8759311_AAACAGAGCGACTCCT-1	WSA_LngSP8759311_AAACATTTCCCGGATT-1	WSA_LngSP8759311_AAACCCGAACGAAATC-1	WSA_LngSP8759311_AAACCGTTCGTCCAGG-1	WSA_LngSP8759311_AAACCTAAGCAGCCGG-1	WSA_LngSP8759311_AAACGAAGAACATACC-1	WSA_LngSP8759311_AAACGAGACGGTTGAT-1	WSA_LngSP8759311_AAACGGGCGTACGGGT-1	WSA_LngSP8759311_AAACGGTTGCGAACTG-1
spot_id
WSA_LngSP8759311_AAACAAGTATCTCCCA-1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
WSA_LngSP8759311_AAACAGAGCGACTCCT-1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
WSA_LngSP8759311_AAACATTTCCCGGATT-1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
WSA_LngSP8759311_AAACCCGAACGAAATC-1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
WSA_LngSP8759311_AAACCGTTCGTCCAGG-1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
WSA_LngSP8759311_AAACCTAAGCAGCCGG-1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
WSA_LngSP8759311_AAACGAAGAACATACC-1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
WSA_LngSP8759311_AAACGAGACGGTTGAT-1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
WSA_LngSP8759311_AAACGGGCGTACGGGT-1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
WSA_LngSP8759311_AAACGGTTGCGAACTG-1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

[41]:

## Read the sum CCC score of a ligand receptor pair at the cell type level
LRP_CellTypeLevel_weight = pd.DataFrame(adata_sp311_stringent.uns['LR_celltype_weight']['IL6|COMPLEX:IL6R_IL6ST'],
                                         index=adata_sp311_stringent.uns['cell_type_list'], columns=adata_sp311_stringent.uns['cell_type_list']
                                        )

[42]:

LRP_CellTypeLevel_weight

[42]:

	Others	Arterial_vessel	Venous_vessel	Glands	Cartilage	Multilayer_epithelium	Nerve	Airway_Smooth_Muscle	Perichondrium	Weird_morphology
Others	44.895301	1.834348	5.810078	10.125569	3.698377	1.465692	3.854100	4.764654	2.223017	13.828947
Arterial_vessel	1.039287	2.237669	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Venous_vessel	0.000000	0.000000	3.908923	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.673586
Glands	1.417303	0.000000	0.000000	8.018459	0.000000	0.000000	0.000000	0.422328	0.000000	0.000000
Cartilage	2.722322	0.000000	0.000000	0.266071	5.055546	0.000000	0.000000	0.000000	0.707860	0.000000
Multilayer_epithelium	0.503930	0.000000	0.000000	0.000000	0.000000	4.177691	0.000000	0.000000	0.000000	0.000000
Nerve	1.257532	0.000000	0.000000	0.423734	0.000000	0.000000	4.901593	0.000000	0.000000	0.538051
Airway_Smooth_Muscle	4.200898	0.000000	0.000000	0.000000	0.000000	2.972851	0.000000	6.919369	0.000000	0.000000
Perichondrium	2.433086	0.000000	0.000000	0.000000	1.191374	0.000000	0.000000	0.000000	3.054677	0.000000
Weird_morphology	12.403792	0.000000	0.000000	2.604088	0.000000	0.000000	2.724591	0.399174	0.000000	21.088744

[43]:

## Read the average interaction score for each interaction edge of a ligand receptor pair at the cell type level
LRP_CellTypeLevel_mean_weight = pd.DataFrame(adata_sp311_stringent.uns['LR_celltype_mean_weight']['IL6|COMPLEX:IL6R_IL6ST'],
                                         index=adata_sp311_stringent.uns['cell_type_list'], columns=adata_sp311_stringent.uns['cell_type_list']
                                        )

[44]:

LRP_CellTypeLevel_mean_weight

[44]:

	Others	Arterial_vessel	Venous_vessel	Glands	Cartilage	Multilayer_epithelium	Nerve	Airway_Smooth_Muscle	Perichondrium	Weird_morphology
Others	0.774057	0.458587	0.830011	0.562532	0.924594	0.732846	0.770820	0.680665	0.741006	0.813467
Arterial_vessel	0.519643	0.447534	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Venous_vessel	0.000000	0.000000	0.781785	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.673586
Glands	0.354326	0.000000	0.000000	0.400923	0.000000	0.000000	0.000000	0.422328	0.000000	0.000000
Cartilage	0.544464	0.000000	0.000000	0.266071	0.561727	0.000000	0.000000	0.000000	0.707860	0.000000
Multilayer_epithelium	0.503930	0.000000	0.000000	0.000000	0.000000	0.696282	0.000000	0.000000	0.000000	0.000000
Nerve	0.419177	0.000000	0.000000	0.423734	0.000000	0.000000	0.980319	0.000000	0.000000	0.538051
Airway_Smooth_Muscle	0.700150	0.000000	0.000000	0.000000	0.000000	0.371606	0.000000	0.864921	0.000000	0.000000
Perichondrium	1.216543	0.000000	0.000000	0.000000	0.397125	0.000000	0.000000	0.000000	1.018226	0.000000
Weird_morphology	0.729635	0.000000	0.000000	0.868029	0.000000	0.000000	0.908197	0.399174	0.000000	0.753169

[45]:

## Read the sum interaction edge number of a ligand receptor pair at the cell type level
LRP_CellTypeLevel_edge_num = pd.DataFrame(adata_sp311_stringent.uns['LR_celltype_edge_num']['IL6|COMPLEX:IL6R_IL6ST'],
                                         index=adata_sp311_stringent.uns['cell_type_list'], columns=adata_sp311_stringent.uns['cell_type_list']
                                        )

[46]:

LRP_CellTypeLevel_edge_num

[46]:

	Others	Arterial_vessel	Venous_vessel	Glands	Cartilage	Multilayer_epithelium	Nerve	Airway_Smooth_Muscle	Perichondrium	Weird_morphology
Others	58.0	4.0	7.0	18.0	4.0	2.0	5.0	7.0	3.0	17.0
Arterial_vessel	2.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Venous_vessel	0.0	0.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
Glands	4.0	0.0	0.0	20.0	0.0	0.0	0.0	1.0	0.0	0.0
Cartilage	5.0	0.0	0.0	1.0	9.0	0.0	0.0	0.0	1.0	0.0
Multilayer_epithelium	1.0	0.0	0.0	0.0	0.0	6.0	0.0	0.0	0.0	0.0
Nerve	3.0	0.0	0.0	1.0	0.0	0.0	5.0	0.0	0.0	1.0
Airway_Smooth_Muscle	6.0	0.0	0.0	0.0	0.0	8.0	0.0	8.0	0.0	0.0
Perichondrium	2.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	3.0	0.0
Weird_morphology	17.0	0.0	0.0	3.0	0.0	0.0	3.0	1.0	0.0	28.0

[47]:

## Read the number of LRPs that occur between cell types
LRP_number_CellTypeLevel = pd.DataFrame(adata_sp311_stringent.uns['LR_celltype_aggregate_weight']['count'],
                                         index=adata_sp311_stringent.uns['cell_type_list'], columns=adata_sp311_stringent.uns['cell_type_list']
                                        )

[48]:

LRP_number_CellTypeLevel

[48]:

	Others	Arterial_vessel	Venous_vessel	Glands	Cartilage	Multilayer_epithelium	Nerve	Airway_Smooth_Muscle	Perichondrium	Weird_morphology
Others	1025.0	563.0	313.0	875.0	413.0	768.0	487.0	770.0	587.0	598.0
Arterial_vessel	540.0	686.0	0.0	4.0	0.0	0.0	6.0	0.0	0.0	98.0
Venous_vessel	290.0	0.0	482.0	0.0	0.0	0.0	0.0	0.0	0.0	159.0
Glands	895.0	4.0	0.0	1007.0	37.0	130.0	177.0	384.0	370.0	277.0
Cartilage	411.0	0.0	0.0	80.0	839.0	0.0	22.0	0.0	491.0	38.0
Multilayer_epithelium	805.0	0.0	0.0	116.0	0.0	880.0	0.0	707.0	2.0	3.0
Nerve	476.0	18.0	7.0	157.0	18.0	0.0	567.0	0.0	13.0	323.0
Airway_Smooth_Muscle	768.0	0.0	0.0	335.0	0.0	646.0	0.0	861.0	0.0	73.0
Perichondrium	629.0	0.0	0.0	380.0	481.0	10.0	32.0	0.0	720.0	92.0
Weird_morphology	636.0	197.0	191.0	305.0	43.0	3.0	321.0	126.0	114.0	664.0

[49]:

## Read the CCC score of a pathway at the cell/spot level
Pathway_CellLevel_CCCscore = pd.DataFrame(adata_sp311_stringent.uns['LR_pathway_cell_weight']['CCL'].toarray(),
                                         index=adata_sp311_stringent.obs.index, columns=adata_sp311_stringent.obs.index
                                        )

[51]:

Pathway_CellLevel_CCCscore.iloc[:10,:10]

[51]:

spot_id	WSA_LngSP8759311_AAACAAGTATCTCCCA-1	WSA_LngSP8759311_AAACAGAGCGACTCCT-1	WSA_LngSP8759311_AAACATTTCCCGGATT-1	WSA_LngSP8759311_AAACCCGAACGAAATC-1	WSA_LngSP8759311_AAACCGTTCGTCCAGG-1	WSA_LngSP8759311_AAACCTAAGCAGCCGG-1	WSA_LngSP8759311_AAACGAAGAACATACC-1	WSA_LngSP8759311_AAACGAGACGGTTGAT-1	WSA_LngSP8759311_AAACGGGCGTACGGGT-1	WSA_LngSP8759311_AAACGGTTGCGAACTG-1
spot_id
WSA_LngSP8759311_AAACAAGTATCTCCCA-1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
WSA_LngSP8759311_AAACAGAGCGACTCCT-1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
WSA_LngSP8759311_AAACATTTCCCGGATT-1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
WSA_LngSP8759311_AAACCCGAACGAAATC-1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
WSA_LngSP8759311_AAACCGTTCGTCCAGG-1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
WSA_LngSP8759311_AAACCTAAGCAGCCGG-1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
WSA_LngSP8759311_AAACGAAGAACATACC-1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
WSA_LngSP8759311_AAACGAGACGGTTGAT-1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
WSA_LngSP8759311_AAACGGGCGTACGGGT-1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
WSA_LngSP8759311_AAACGGTTGCGAACTG-1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

[33]:

## Read the CCC score of a pathway at the cell type level
Pathway_CellTypeLevel_wight = pd.DataFrame(adata_sp311_stringent.uns['LR_pathway_celltype_weight']['CCL'],
                                           index=adata_sp311_stringent.uns['cell_type_list'], columns=adata_sp311_stringent.uns['cell_type_list']
                                          )
Pathway_CellTypeLevel_mean_weight = pd.DataFrame(adata_sp311_stringent.uns['LR_pathway_celltype_mean_weight']['CCL'],
                                                 index=adata_sp311_stringent.uns['cell_type_list'], columns=adata_sp311_stringent.uns['cell_type_list']
                                                )
Pathway_CellTypeLevel_edge_num = pd.DataFrame(adata_sp311_stringent.uns['LR_pathway_celltype_edge_num']['CCL'],
                                              index=adata_sp311_stringent.uns['cell_type_list'], columns=adata_sp311_stringent.uns['cell_type_list']
                                              )
Pathway_CellTypeLevel_count = pd.DataFrame(adata_sp311_stringent.uns['LR_pathway_celltype_count']['CCL'],
                                           index=adata_sp311_stringent.uns['cell_type_list'], columns=adata_sp311_stringent.uns['cell_type_list']
                                           )

[35]:

## Read all Scenic result
Scenic_result = adata_sp311_stringent.uns['scenic_res']

[54]:

Scenic_result['auc_mtx'].head()

[54]:

Regulon	ATF2(+)	ATF4(+)	ATF5(+)	CEBPB(+)	CLOCK(+)	DLX5(+)	E2F1(+)	E2F3(+)	E2F4(+)	EGR1(+)	...	THRA(+)	THRB(+)	TP53(+)	TWIST1(+)	XBP1(+)	ZFHX3(+)	ZNF398(+)	ZNF430(+)	ZNF587(+)	ZNF611(+)
Cell
WSA_LngSP8759311_AAACAAGTATCTCCCA-1	0.108328	0.093427	0.069433	0.012242	0.011166	0.0	0.014957	0.029883	0.008086	0.017228	...	0.009210	0.014643	0.011487	0.073802	0.036905	0.006195	0.085418	0.0	0.0	0.000000
WSA_LngSP8759311_AAACAGAGCGACTCCT-1	0.000000	0.074548	0.006270	0.021819	0.011246	0.0	0.020478	0.003840	0.010671	0.025917	...	0.004947	0.061523	0.031192	0.029992	0.043080	0.034313	0.034427	0.0	0.0	0.038396
WSA_LngSP8759311_AAACATTTCCCGGATT-1	0.000000	0.100603	0.025635	0.045186	0.009883	0.0	0.017017	0.000473	0.006903	0.031353	...	0.031014	0.000000	0.009068	0.011124	0.093036	0.006681	0.037972	0.0	0.0	0.000000
WSA_LngSP8759311_AAACCCGAACGAAATC-1	0.001860	0.124458	0.018883	0.063018	0.023382	0.0	0.026290	0.006872	0.012462	0.024987	...	0.012721	0.000000	0.035857	0.045622	0.034919	0.044688	0.052010	0.0	0.0	0.000000
WSA_LngSP8759311_AAACCGTTCGTCCAGG-1	0.000000	0.142454	0.000000	0.011417	0.028632	0.0	0.015208	0.025063	0.006036	0.022820	...	0.017319	0.000000	0.012471	0.007040	0.038779	0.024019	0.063190	0.0	0.0	0.000000

5 rows × 102 columns

[ ]: