NicheSphere data preprocessing tutorial

NicheSphere is an sc-verse compatible Python library which allows the user to find differentially co-localized cellular niches and biological processes involved in their interactions based on cell type pairs co-localization probabilities in different conditions. Cell type pair co-localization probabilities are obtained in different ways: from deconvoluted Visium 10x / PIC-seq data (probabilities of finding each cell type in each spot / multiplet), or counting cell boundaries overlaps for each cell type pair in single cell spatial data (MERFISH , CODEX …). This is addressed in the differential co-localization tutorial.

NicheSphere also offers the possibility to look at localized differential cell - cell communication based on Ligand-Receptor pairs expression data. This is addressed in the localized differential communication tutorial.

This tutorial focuses on the data pre-processing needed before doing the differential co-localization and localized process based differential communication analyses.

1. Libraries and functions

[1]:

import pandas as pd
import scipy
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import networkx as nx
import warnings
import scanpy as sc
import mudata as md
import numpy as np
from community_layout.layout_class import CommunityLayout
warnings.filterwarnings("ignore")

import nichesphere

COMMUNITY LAYOUT: Datashader not found, edge bundling not available

2. Data at first glance

In this example we will use data from the Myocardial Infarction atlas from Kuppe, C. et. Al., 2022

[2]:

mudata=md.read('heart_MI_ST_SC_23samples.h5mu')
mudata

[2]:

MuData object with n_obs × n_vars = 206792 × 39120
  2 modalities
    visium: 73904 x 11704
      obs:  'n_counts', 'n_genes', 'percent.mt', 'Adipocyte', 'Cardiomyocyte', 'Endothelial', 'Fibroblast', 'Lymphoid', 'Mast', 'Myeloid', 'Neuronal', 'Pericyte', 'Cycling.cells', 'vSMCs', 'cell_type_original', 'assay_ontology_term_id', 'cell_type_ontology_term_id', 'development_stage_ontology_term_id', 'disease_ontology_term_id', 'ethnicity_ontology_term_id', 'is_primary_data', 'organism_ontology_term_id', 'sex_ontology_term_id', 'tissue_ontology_term_id', 'patient_region_id', 'orig_ident', 'batch'
      var:  'features'
      obsm: 'X_pca', 'X_spatial', 'X_umap'
    sc:     132888 x 27416
      obs:  'orig_ident', 'nCount_RNA', 'nFeature_RNA', 'percent_mt', 'doublet_score', 'doublet', 'dissociation_s1', 'opt_clust', 'patient', 'batch', 'opt_clust_integrated', 'cell_type', 'ident', 'nFeaturess_RNA', 'cell_subtype', 'cell_subtype_available', 'cell_subtype2', 'patient_region_id', 'patient_group', 'sampleType'
      var:  'n_counts'
      obsm: 'X_harmony', 'X_pca', 'X_umap_harmony'

This is a subset with 23 samples (samples with less than 1500 cells in the snRNA-seq data were filtered out), and 33 different cell subtypes

[3]:

mudata['sc'].obsm['umap']=mudata['sc'].obsm['X_umap_harmony']
sc.pl.umap(mudata['sc'],
           color=['cell_type', 'cell_subtype2'], wspace=0.3)
../_images/notebooks_Nichesphere_tutorial_MIvisium_preprocessing_7_0.png

3. For differential co-localization

In this case, we will get cell type co-localization probabilities from deconvoluted Visium data (Cell type probabilities per spot):

In a previous step, we used MOSCOT(Klein et. al., 2025) to deconvolute cell subtypes in visium slices from the same 23 samples , getting matrices of probabilities of each cell being in each spot. Now we will get cell type probabilities per spot summing the probabilities of cells of the same kind in each spot; thus getting cell type probability matrices for all spots per sample. This can be done with the get_spot_ct_props function from NicheSphere. As an example, we will do this for two samples: control sample control_P1 and ischemic sample GT_IZ_P9

[4]:

CTprops=pd.DataFrame()
for smpl in ['control_P1', 'GT_IZ_P9']:
    t=pd.read_csv('cell_probs_'+smpl+'.csv', index_col=0)

    adata_sc=mudata['sc'][mudata['sc'].obs.patient_region_id==smpl]
    adata_sc=adata_sc[[ c in adata_sc.obs.cell_subtype2.value_counts().index[adata_sc.obs.cell_subtype2.value_counts()>2] for c in adata_sc.obs.cell_subtype2]]

    props=nichesphere.tl.get_spot_ct_props(spot_cell_props=t , sc_ct=adata_sc.obs.cell_subtype2)
    CTprops=pd.concat([CTprops, props])

[5]:

CTprops.head()

[5]:
Fib1_SCARA5 damaged_CM Capillary_Endo LYVE_FOLR_Macrophages Fib3_C7 healthy_CM Fib2_Myofib Endocardial_Endo Arterial_Endo Neuronal ... vSMCs_1 CCL18_Macrophages perivascular_fibroblasts CD_4 vSMCs_2 Lymphatic_Endo NK CD_8 Purkinje_fibers Adipo
AAACAAGTATCTCCCA-1-1-0-0-0 0.000000e+00 0.000000e+00 0.0 8.333133e-16 0.000000 0.000000e+00 0.428865 0.000000 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 NaN
AAACAATCTACTAGCA-1-1-0-0-0 0.000000e+00 2.691729e-21 0.0 0.000000e+00 0.445912 5.540884e-01 0.000000 0.000000 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 NaN
AAACACCAATAACTGC-1-1-0-0-0 0.000000e+00 0.000000e+00 0.0 0.000000e+00 0.000000 0.000000e+00 0.000000 0.000030 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.499508 0.0 0.0 NaN
AAACAGAGCGACTCCT-1-1-0-0-0 1.373226e-25 0.000000e+00 0.0 0.000000e+00 0.499762 3.111796e-13 0.500238 0.000000 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 NaN
AAACAGCTTTCAGAAG-1-1-0-0-0 0.000000e+00 0.000000e+00 0.0 0.000000e+00 0.000000 0.000000e+00 0.443230 0.113118 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 NaN

5 rows × 32 columns

You can download the following table of cell type proportions in all samples from zenodo

[6]:

CTprops=pd.read_csv('CTprops.csv', index_col=0)
CTprops.head()

[6]:
Fib1_SCARA5 damaged_CM Capillary_Endo LYVE_FOLR_Macrophages Fib3_C7 healthy_CM Fib2_Myofib Endocardial_Endo Arterial_Endo Neuronal ... CCL18_Macrophages perivascular_fibroblasts CD_4 vSMCs_2 Lymphatic_Endo NK CD_8 Purkinje_fibers Adipo NK_T
AAACAAGTATCTCCCA-1-1-0-0-0 0.000000e+00 0.000000e+00 0.0 8.333133e-16 0.000000 0.000000e+00 0.428865 0.000000 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 NaN NaN
AAACAATCTACTAGCA-1-1-0-0-0 0.000000e+00 2.691729e-21 0.0 0.000000e+00 0.445912 5.540884e-01 0.000000 0.000000 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 NaN NaN
AAACACCAATAACTGC-1-1-0-0-0 0.000000e+00 0.000000e+00 0.0 0.000000e+00 0.000000 0.000000e+00 0.000000 0.000030 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.499508 0.0 0.0 NaN NaN
AAACAGAGCGACTCCT-1-1-0-0-0 1.373226e-25 0.000000e+00 0.0 0.000000e+00 0.499762 3.111796e-13 0.500238 0.000000 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 NaN NaN
AAACAGCTTTCAGAAG-1-1-0-0-0 0.000000e+00 0.000000e+00 0.0 0.000000e+00 0.000000 0.000000e+00 0.443230 0.113118 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 NaN NaN

5 rows × 33 columns

From these deconvolution results, we can look at cell type proportions per sample. For this we will need the spot ID and sample correspondence:

[7]:

spotSamples=mudata['visium'].obs.patient_region_id
spotSamples.reset_index().head()

[7]:
index patient_region_id
0 AAACAAGTATCTCCCA-1-0-0-0-0-0-0-0-0-0-0-0-0-0-0... control_P17
1 AAACACCAATAACTGC-1-0-0-0-0-0-0-0-0-0-0-0-0-0-0... control_P17
2 AAACAGCTTTCAGAAG-1-0-0-0-0-0-0-0-0-0-0-0-0-0-0... control_P17
3 AAACAGGGTCTATATT-1-0-0-0-0-0-0-0-0-0-0-0-0-0-0... control_P17
4 AAACCGGGTAGGTACC-1-0-0-0-0-0-0-0-0-0-0-0-0-0-0... control_P17

A way to check the deconvolution proportions is using a clustermap

[8]:

CTprops_sample=CTprops.copy()
CTprops_sample['sample']=mudata['visium'].obs.patient_region_id
sns.clustermap(CTprops_sample.groupby('sample').sum().T/CTprops_sample.groupby('sample').sum().sum(axis=1) ,
               cmap='Blues', method='ward')

[8]:

<seaborn.matrix.ClusterGrid at 0x15536d3d2920>
../_images/notebooks_Nichesphere_tutorial_MIvisium_preprocessing_17_1.png

Alternativelly, we can check the deconvolution proportions using barplots

[9]:

t1=pd.DataFrame(CTprops[spotSamples=='control_P7'].sum(), columns=['control_P7'])
t2=pd.DataFrame(CTprops[spotSamples=='IZ_P15'].sum(), columns=['IZ_P15'])

[10]:

sns.set(font_scale=1)
sns.set_style(rc = {'axes.facecolor': 'white'})

fig, axes = plt.subplots(1, 2, figsize=(20, 7))

sns.barplot(ax=axes[0], y=t1.sort_values('control_P7', ascending=False).index, x='control_P7',
            data=t1.sort_values('control_P7', ascending=False), color='darkblue')
axes[0].set_title('control_P7')

sns.barplot(ax=axes[1], y=t2.sort_values('IZ_P15', ascending=False).index, x='IZ_P15',
            data=t2.sort_values('IZ_P15', ascending=False), color='darkred')
axes[1].set_title('IZ_P15')

[10]:

Text(0.5, 1.0, 'IZ_P15')
../_images/notebooks_Nichesphere_tutorial_MIvisium_preprocessing_20_1.png

We can visualize cell type deconvolution results in slices (spots are colored by the the cell type with highest proportion). For this we will need the spatial coordinates of the spots in the Visium slices need to be in the slot uns[‘spatial’] of the Visium anndata object:

[11]:

mudata['visium'].uns['spatial']=mudata['visium'].obsm['X_spatial']

[12]:

idPat = 'GT_IZ_P9'
nichesphere.coloc.spatialCTPlot(adata=mudata['visium'][mudata['visium'].obs.patient_region_id==idPat].copy(),
                                CTprobs=CTprops.loc[spotSamples.index[spotSamples==idPat]],
                                cell_types=mudata['sc'].obs.cell_subtype2, spot_size=0.015,
                                legend_fontsize=7)

WARNING: saving figure to file figures/showtest.pdf
../_images/notebooks_Nichesphere_tutorial_MIvisium_preprocessing_23_1.png 
[ ]:

3.1 During differential co-localization analysis

We will compute then co-localization probabilities from the cell type probability matrices. Here will get concatenated co-localization sample matrices of cell type x cell type.

[13]:

CTcolocalizationP=nichesphere.coloc.getColocProbs(CTprobs=CTprops,
                                                  spotSamples=mudata['visium'].obs.patient_region_id #Series of spot samples with spot ID as index
                                                 )

Then we reshape the co-localization data into a matrix of cell type pairs x samples. We set the parameter complete = 1 because we have a simetrical matrix including both co-localization probabilities of cell type pair a - b , as well as b - a (which have the same probability)

[14]:

colocPerSample=nichesphere.coloc.reshapeColoc(CTcoloc=CTcolocalizationP,
                                              complete=1)
colocPerSample.head()

[14]:
Fib1_SCARA5-Fib1_SCARA5 Fib1_SCARA5-damaged_CM Fib1_SCARA5-Capillary_Endo Fib1_SCARA5-LYVE_FOLR_Macrophages Fib1_SCARA5-Fib3_C7 Fib1_SCARA5-healthy_CM Fib1_SCARA5-Fib2_Myofib Fib1_SCARA5-Endocardial_Endo Fib1_SCARA5-Arterial_Endo Fib1_SCARA5-Neuronal ... NK_T-CCL18_Macrophages NK_T-perivascular_fibroblasts NK_T-CD_4 NK_T-vSMCs_2 NK_T-Lymphatic_Endo NK_T-NK NK_T-CD_8 NK_T-Purkinje_fibers NK_T-Adipo NK_T-NK_T
control_P17 0.017603 0.000308 0.000992 0.000251 0.007062 0.002586 0.004724 0.000943 0.000412 0.000351 ... 2.290066e-15 0.0 3.915381e-05 0.0 0.0 4.538656e-08 4.556003e-08 0.0 0.000000e+00 0.000268
RZ_P9 0.009307 0.000429 0.000738 0.000003 0.005204 0.001439 0.001625 0.000065 0.000168 0.000046 ... 0.000000e+00 0.0 4.640548e-05 0.0 0.0 9.954633e-05 1.643486e-05 0.0 0.000000e+00 0.000784
IZ_P15 0.030351 0.000000 0.000027 0.000186 0.001200 0.000000 0.003112 0.000072 0.000062 0.000000 ... 0.000000e+00 0.0 0.000000e+00 0.0 0.0 0.000000e+00 0.000000e+00 0.0 0.000000e+00 0.000000
RZ_P6 0.040470 0.000441 0.002752 0.000361 0.008687 0.002928 0.007878 0.000176 0.001022 0.001170 ... 0.000000e+00 0.0 7.998369e-25 0.0 0.0 8.593925e-28 0.000000e+00 0.0 0.000000e+00 0.000438
RZ_BZ_P3 0.021508 0.000292 0.000567 0.000057 0.002408 0.000483 0.006635 0.000123 0.000052 0.000052 ... 0.000000e+00 0.0 0.000000e+00 0.0 0.0 8.585311e-06 0.000000e+00 0.0 7.294563e-35 0.000897

5 rows × 1089 columns

The sum of the probabilities of every cell type pair in a sample must be = 1

[15]:

colocPerSample.sum(axis=1)

[15]:

control_P17    1.0
RZ_P9          1.0
IZ_P15         1.0
RZ_P6          1.0
RZ_BZ_P3       1.0
FZ_P14         1.0
RZ_BZ_P12      1.0
FZ_GT_P4       1.0
GT_IZ_P13      1.0
GT_IZ_P15      1.0
FZ_P20         1.0
RZ_FZ_P5       1.0
GT_IZ_P9       1.0
RZ_P3          1.0
FZ_GT_P19      1.0
FZ_P18         1.0
IZ_P10         1.0
control_P7     1.0
RZ_P11         1.0
control_P1     1.0
RZ_BZ_P2       1.0
control_P8     1.0
RZ_GT_P2       1.0
dtype: float64

This is how we got the file from zenodo colocPerSample.csv

[16]:

colocPerSample=pd.read_csv('colocPerSample.csv', index_col=0)
colocPerSample.head()

[16]:
Fib1_SCARA5-Fib1_SCARA5 Fib1_SCARA5-damaged_CM Fib1_SCARA5-Capillary_Endo Fib1_SCARA5-LYVE_FOLR_Macrophages Fib1_SCARA5-Fib3_C7 Fib1_SCARA5-healthy_CM Fib1_SCARA5-Fib2_Myofib Fib1_SCARA5-Endocardial_Endo Fib1_SCARA5-Arterial_Endo Fib1_SCARA5-Neuronal ... NK_T-CCL18_Macrophages NK_T-perivascular_fibroblasts NK_T-CD_4 NK_T-vSMCs_2 NK_T-Lymphatic_Endo NK_T-NK NK_T-CD_8 NK_T-Purkinje_fibers NK_T-Adipo NK_T-NK_T
control_P17 0.017603 0.000308 0.000992 0.000251 0.007062 0.002586 0.004724 0.000943 0.000412 0.000351 ... 2.290066e-15 0.0 3.915381e-05 0.0 0.0 4.538656e-08 4.556003e-08 0.0 0.000000e+00 0.000268
RZ_P9 0.009307 0.000429 0.000738 0.000003 0.005204 0.001439 0.001625 0.000065 0.000168 0.000046 ... 0.000000e+00 0.0 4.640548e-05 0.0 0.0 9.954633e-05 1.643486e-05 0.0 0.000000e+00 0.000784
IZ_P15 0.030351 0.000000 0.000027 0.000186 0.001200 0.000000 0.003112 0.000072 0.000062 0.000000 ... 0.000000e+00 0.0 0.000000e+00 0.0 0.0 0.000000e+00 0.000000e+00 0.0 0.000000e+00 0.000000
RZ_P6 0.040470 0.000441 0.002752 0.000361 0.008687 0.002928 0.007878 0.000176 0.001022 0.001170 ... 0.000000e+00 0.0 7.998369e-25 0.0 0.0 8.593925e-28 0.000000e+00 0.0 0.000000e+00 0.000438
RZ_BZ_P3 0.021508 0.000292 0.000567 0.000057 0.002408 0.000483 0.006635 0.000123 0.000052 0.000052 ... 0.000000e+00 0.0 0.000000e+00 0.0 0.0 8.585311e-06 0.000000e+00 0.0 7.294563e-35 0.000897

5 rows × 1089 columns

The sum of the probabilities of every cell type pair in a sample must be = 1

[17]:

colocPerSample.sum(axis=1)

[17]:

control_P17    1.0
RZ_P9          1.0
IZ_P15         1.0
RZ_P6          1.0
RZ_BZ_P3       1.0
FZ_P14         1.0
RZ_BZ_P12      1.0
FZ_GT_P4       1.0
GT_IZ_P13      1.0
GT_IZ_P15      1.0
FZ_P20         1.0
RZ_FZ_P5       1.0
GT_IZ_P9       1.0
RZ_P3          1.0
FZ_GT_P19      1.0
FZ_P18         1.0
IZ_P10         1.0
control_P7     1.0
RZ_P11         1.0
control_P1     1.0
RZ_BZ_P2       1.0
control_P8     1.0
RZ_GT_P2       1.0
dtype: float64

Conditions

In this case, condition can be inferred from abbreviations in the sample names:

RZ = Remote zone BZ = Border Zone FZ = Fibrotic Zone IZ = Ischemic Zone

[18]:

sampleTypesDF=pd.DataFrame(colocPerSample.index, columns=['sample'])
sampleTypesDF['sampleType']='myogenic'

#tmp.obs.sampleType[tmp.obs['sample'].str.contains("BZ")]='border'
sampleTypesDF.sampleType[sampleTypesDF['sample'].str.contains("RZ")]='remote'
sampleTypesDF.sampleType[sampleTypesDF['sample'].str.contains("BZ")]='border'


sampleTypesDF.sampleType[sampleTypesDF['sample'].str.contains("FZ")]='fibrotic'
sampleTypesDF.sampleType[sampleTypesDF['sample'].str.contains("IZ")]='ischemic'
sampleTypesDF

[18]:
sample sampleType
0 control_P17 myogenic
1 RZ_P9 remote
2 IZ_P15 ischemic
3 RZ_P6 remote
4 RZ_BZ_P3 border
5 FZ_P14 fibrotic
6 RZ_BZ_P12 border
7 FZ_GT_P4 fibrotic
8 GT_IZ_P13 ischemic
9 GT_IZ_P15 ischemic
10 FZ_P20 fibrotic
11 RZ_FZ_P5 fibrotic
12 GT_IZ_P9 ischemic
13 RZ_P3 remote
14 FZ_GT_P19 fibrotic
15 FZ_P18 fibrotic
16 IZ_P10 ischemic
17 control_P7 myogenic
18 RZ_P11 remote
19 control_P1 myogenic
20 RZ_BZ_P2 border
21 control_P8 myogenic
22 RZ_GT_P2 remote

This is how we got the file in zenodo MI_sampleTypesDF.csv

4. For differential communication

NicheSphere is suited to work with CrossTalkeR output tables which contain columns called cellpair, indicating the cell types involved in an interaction separated by ‘@’; and allpair, indicating cell types, ligand and receptor involved. Additionally, input tables for NicheSphere should contain columns indicating the ligand (gene_A), receptor (gene_B) and communication score (MeanLR). The names for these last 3 columns can be indicated in NicheSphere functions.

The following tables come from a CrossTalkeR run comparing myogenic and ischemic samples. They are the condition specific tables in the LR_data_final object:

[19]:

crossTalker_ctrlComm=pd.read_csv('./crossTalker_tbl_myo_heart_exp_scSeqComm.csv', index_col=0)
crossTalker_iscComm=pd.read_csv('./crossTalker_tbl_isc_heart_exp_scSeqComm.csv', index_col=0)

CrossTalkeR tables contain strings indicating wether a gene is a ligand (|L), receptor (|R) or transcription factor (|TF). These strings will be removed as they might cause conflicts with code.

[20]:

crossTalker_ctrlComm=nichesphere.tl.processCTKRoutput(crossTalker_ctrlComm)
crossTalker_iscComm=nichesphere.tl.processCTKRoutput(crossTalker_iscComm)

4.1 Database

We are interested in cell communication related to fibrosis, so we will use a database which classifies ligands according to extracellular matrix (ECM) or immune cell recruitment related processes to look for disease related processes :

[21]:

allDBs=pd.read_csv('nichesphereDB_pmid.csv', index_col=0)
allDBs.head()

[21]:
Ligand category db PMID
2 col1a1 Collagens matrisome 36399478
3 col1a2 Collagens matrisome 36399478
4 col1a1 Collagens matrisome 36399478
5 col6a3 Collagens matrisome 36399478
6 col1a2 Collagens matrisome 36399478 
[22]:

sns.barplot(x=allDBs.category.value_counts(), y=allDBs.category.value_counts().index,
            data=pd.DataFrame(allDBs.category.value_counts()))

[22]:

<Axes: xlabel='count', ylabel='category'>
../_images/notebooks_Nichesphere_tutorial_MIvisium_preprocessing_44_1.png

4.2 During differential communication analysis

Now we can separate communication scores by process to know which cell type pairs and niche pairs are interacting through which processes in each condition.

Same cell type interactions will be excluded, so we’ll have a list of same cell type interaction pairs in order to subset the data.

[23]:

cell_types=CTprops.columns
oneCTints=cell_types+'-'+cell_types

For the calculation of the LR scores per process we will need the cell pair - niche pair correspondence table generated in the co-localization tutorial as pairCatDFdir

[24]:

pairCatDFdir=pd.read_csv('./pairCatDFdir_MIvisium_louvain.csv')

[25]:

myoCTpairScores_byCat2_dir_colocClusts=nichesphere.comm.lr_ctPairScores_perCat_dir(ccommTable=crossTalker_ctrlComm,
                                                                                   db=allDBs, dbCatCol='category',
                                                                                   dbMatchCol='Ligand',        #column in database table to match with (ligand) column in the communication data table
                                                                                   ccommMatchCol='gene_A',     #column in communication data table to match with (ligand) column in the database table
                                                                                   ccommLRscoresCol='MeanLR',
                                                                                   oneCTinteractions=oneCTints.str.replace('-', '@'),
                                                                                   condition='myogenic',
                                                                                   pairCatDF=pairCatDFdir)

[27]:

iscCTpairScores_byCat2_dir_colocClusts=nichesphere.comm.lr_ctPairScores_perCat_dir(ccommTable=crossTalker_iscComm,
                                                                                   db=allDBs,
                                                                                   dbCatCol='category',
                                                                                   dbMatchCol='Ligand',
                                                                                   ccommMatchCol='gene_A',
                                                                                   ccommLRscoresCol='MeanLR',
                                                                                   oneCTinteractions=oneCTints.str.replace('-', '@'),
                                                                                   condition='ischemic',
                                                                                   pairCatDF=pairCatDFdir)

[28]:

myoCTpairScores_byCat2_dir_colocClusts.head()

[28]:
Unnamed: 0 cell_pairs niche_pairs LRscores LRcat condition
allpair
Adipo/COL14A1@Arterial_Endo/CD44 1031 Adipo->Arterial_Endo 1_Stromal->3_Healthy_CM 0.717175 Collagens myogenic
Adipo/COL14A1@CCL18_Macrophages/CD44 1046 Adipo->CCL18_Macrophages 1_Stromal->4_Fibrotic 0.967985 Collagens myogenic
Adipo/COL14A1@CD_4/CD44 1048 Adipo->CD_4 1_Stromal->2_Stressed_CM 0.967985 Collagens myogenic
Adipo/COL14A1@CD_8/CD44 1052 Adipo->CD_8 1_Stromal->2_Stressed_CM 0.967985 Collagens myogenic
Adipo/COL14A1@Fib1_SCARA5/CD44 1023 Adipo->Fib1_SCARA5 1_Stromal->1_Stromal 0.967985 Collagens myogenic

Some interactions might be present only in one condition, so we will assign 0 values to the interactions that do not appear in one condition to be able to compare conditions

[31]:

myoCTpairScores_byCat2_dir_colocClusts, iscCTpairScores_byCat2_dir_colocClusts = nichesphere.comm.equalizeScoresTables(ctrlTbl=myoCTpairScores_byCat2_dir_colocClusts,
                                                                                                                       expTbl=iscCTpairScores_byCat2_dir_colocClusts,
                                                                                                                       ctrlCondition='myogenic',
                                                                                                                       expCondition='ischemic')

[32]:

myoCTpairScores_byCat2_dir_colocClusts.head()

[32]:
Unnamed: 0 cell_pairs niche_pairs LRscores LRcat condition
allpair
Adipo/COL14A1@Arterial_Endo/CD44 1031 Adipo->Arterial_Endo 1_Stromal->3_Healthy_CM 0.717175 Collagens myogenic
Adipo/COL14A1@CCL18_Macrophages/CD44 1046 Adipo->CCL18_Macrophages 1_Stromal->4_Fibrotic 0.967985 Collagens myogenic
Adipo/COL14A1@CD_4/CD44 1048 Adipo->CD_4 1_Stromal->2_Stressed_CM 0.967985 Collagens myogenic
Adipo/COL14A1@CD_8/CD44 1052 Adipo->CD_8 1_Stromal->2_Stressed_CM 0.967985 Collagens myogenic
Adipo/COL14A1@Fib1_SCARA5/CD44 1023 Adipo->Fib1_SCARA5 1_Stromal->1_Stromal 0.967985 Collagens myogenic

And this is how we get the tables processed_myo_ctkr_louvainColoc.csv and processed_isc_ctkr_louvainColoc.csv from zenodo

[ ]: