LARIS on human tonsil Slide-tags data, inference and visualization#

Import LARIS#

[1]:

path = '/data1/mdai/Analysis/Jupyter/Python/Package/LARIS'
import sys
sys.path.append(path)
import laris as la

Import other packages#

[2]:

import numpy as np
import pandas as pd
import scanpy as sc
import matplotlib.pyplot as plt
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

[3]:

sc.set_figure_params(dpi=96,dpi_save=300, color_map='viridis',facecolor='white')
plt.rcParams['figure.figsize'] = 4, 4
sc.settings.verbosity = 3
sc.logging.print_header()

scanpy==1.9.6 anndata==0.10.3 umap==0.5.5 numpy==1.26.2 scipy==1.11.4 pandas==2.1.3 scikit-learn==1.3.2 statsmodels==0.14.0 igraph==0.11.3 louvain==0.8.1 pynndescent==0.5.11

[4]:

# Colours
from matplotlib import cm
from matplotlib import colors, colorbar
cmap_own = cm.get_cmap('magma_r', 256)
newcolors = cmap_own(np.linspace(0,0.75 , 256))
Greys = cm.get_cmap('Greys_r', 256)
#newcolors[:1, :] = Greys(np.linspace(0.8125, 0.8725, 1))
newcolors[:10, :] = Greys(np.linspace(0.8125, 0.8725, 10))
pos_cmap = colors.ListedColormap(newcolors)

/tmp/ipykernel_3278614/2129373808.py:4: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
  cmap_own = cm.get_cmap('magma_r', 256)
/tmp/ipykernel_3278614/2129373808.py:6: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
  Greys = cm.get_cmap('Greys_r', 256)

Load data#

mkdir -p /data1/mdai/Result/single-cell/Methods/LARIS/tutorial
cd /data1/mdai/Result/single-cell/Methods/LARIS/tutorial
gdrive files download 170cY6MjzUlOyaHlHq-N8_vqgKKPwQ_8N

[5]:

adata=sc.read('/data1/mdai/Result/single-cell/Methods/LARIS/tutorial/adata_tonsil.h5ad') # Read in the anndata object

[6]:

adata

[6]:

AnnData object with n_obs × n_vars = 5695 × 25583
    obs: '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', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'log1p_total_counts_ribo', 'pct_counts_ribo', 'n_genes', 'cell_type', 'region_name'
    var: 'gene_ids', 'feature_types', 'genome', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'var_across_cell_types'
    uns: 'cell_type_colors', 'hvg', 'log1p', 'neighbors', 'pca', 'region_name_colors', 'umap'
    obsm: 'X_pca', 'X_spatial', 'X_umap'
    varm: 'PCs'
    layers: 'CellBender_raw_X'
    obsp: 'connectivities', 'distances'

[7]:

# Check spatial mapping
x_width=adata.obsm['X_spatial'][:,0].max()-adata.obsm['X_spatial'][:,0].min()
y_width=adata.obsm['X_spatial'][:,1].max()-adata.obsm['X_spatial'][:,1].min()

plt.rcParams['figure.figsize'] = 5, 5*y_width/x_width

sc.pl.embedding(
    adata,
    basis='X_spatial',
    color=['cell_type'],
    size=80,frameon=False)

plt.rcParams['figure.figsize'] = 4, 4

/data1/mdai/.conda/envs/scda/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:394: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
../_images/notebooks_04_LARIS_tutorial_11_1.png 
[8]:

# Check spatial mapping
x_width=adata.obsm['X_spatial'][:,0].max()-adata.obsm['X_spatial'][:,0].min()
y_width=adata.obsm['X_spatial'][:,1].max()-adata.obsm['X_spatial'][:,1].min()

plt.rcParams['figure.figsize'] = 5, 5*y_width/x_width

sc.pl.embedding(
    adata,
    basis='X_spatial',
    color=['cell_type'],
    groups=['T_CD4'],
    size=80,frameon=False)

plt.rcParams['figure.figsize'] = 4, 4

/data1/mdai/.conda/envs/scda/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:394: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
../_images/notebooks_04_LARIS_tutorial_12_1.png 
[9]:

# Check spatial mapping
x_width=adata.obsm['X_spatial'][:,0].max()-adata.obsm['X_spatial'][:,0].min()
y_width=adata.obsm['X_spatial'][:,1].max()-adata.obsm['X_spatial'][:,1].min()

plt.rcParams['figure.figsize'] = 5, 5*y_width/x_width

sc.pl.embedding(
    adata,
    basis='X_spatial',
    color=['cell_type'],
    groups=['T_CD8'],
    size=80,frameon=False)

plt.rcParams['figure.figsize'] = 4, 4

/data1/mdai/.conda/envs/scda/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:394: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
../_images/notebooks_04_LARIS_tutorial_13_1.png 
[10]:

# Check spatial UMAP
x_width=adata.obsm['X_umap'][:,0].max()-adata.obsm['X_umap'][:,0].min()
y_width=adata.obsm['X_umap'][:,1].max()-adata.obsm['X_umap'][:,1].min()

plt.rcParams['figure.figsize'] = 5, 5*y_width/x_width

sc.pl.embedding(
    adata,
    basis='X_umap',
    color=['cell_type'],
    size=40,frameon=False)

plt.rcParams['figure.figsize'] = 4, 4

/data1/mdai/.conda/envs/scda/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:394: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
../_images/notebooks_04_LARIS_tutorial_14_1.png

Run LARIS#

Load the cell-cell interaction database#

[11]:

%%time
# Any dataframe could be used as long as 'ligand' and 'receptor' coloumns exist
lr_df=pd.read_csv('/data1/mdai/Result/single-cell/Methods/LARIS/tutorial/human_lr_database_CellChatDB_formatted_v2.csv',index_col=0)

CPU times: user 55 ms, sys: 3.55 ms, total: 58.5 ms
Wall time: 57.4 ms

[12]:

lr_df.head()

[12]:
pathway_name ligand receptor agonist antagonist co_A_receptor co_I_receptor evidence annotation interaction_name_2 ... receptor.symbol receptor.family receptor.location receptor.keyword receptor.surfaceome_main receptor.surfaceome_sub receptor.adhesome receptor.secreted_type receptor.transmembrane version
interaction_name
TGFB1_TGFBR1_TGFBR2 TGFb TGFB1 TGFBR2 TGFb agonist TGFb antagonist NaN TGFb inhibition receptor KEGG: hsa04350 Secreted Signaling TGFB1 - (TGFBR1+TGFBR2) ... TGFBR2, TGFBR1 Protein kinase superfamily, TKL Ser/Thr protei... Cell membrane, Secreted, Membrane raft, Cell s... Membrane, Secreted, Disulfide bond, Kinase, Tr... Receptors Act.TGFB;Kinase NaN NaN True CellChatDB v1
TGFB1_TGFBR1_TGFBR2 TGFb TGFB1 TGFBR1 TGFb agonist TGFb antagonist NaN TGFb inhibition receptor KEGG: hsa04350 Secreted Signaling TGFB1 - (TGFBR1+TGFBR2) ... TGFBR2, TGFBR1 Protein kinase superfamily, TKL Ser/Thr protei... Cell membrane, Secreted, Membrane raft, Cell s... Membrane, Secreted, Disulfide bond, Kinase, Tr... Receptors Act.TGFB;Kinase NaN NaN True CellChatDB v1
TGFB2_TGFBR1_TGFBR2 TGFb TGFB2 TGFBR2 TGFb agonist TGFb antagonist NaN TGFb inhibition receptor KEGG: hsa04350 Secreted Signaling TGFB2 - (TGFBR1+TGFBR2) ... TGFBR2, TGFBR1 Protein kinase superfamily, TKL Ser/Thr protei... Cell membrane, Secreted, Membrane raft, Cell s... Membrane, Secreted, Disulfide bond, Kinase, Tr... Receptors Act.TGFB;Kinase NaN NaN True CellChatDB v1
TGFB2_TGFBR1_TGFBR2 TGFb TGFB2 TGFBR1 TGFb agonist TGFb antagonist NaN TGFb inhibition receptor KEGG: hsa04350 Secreted Signaling TGFB2 - (TGFBR1+TGFBR2) ... TGFBR2, TGFBR1 Protein kinase superfamily, TKL Ser/Thr protei... Cell membrane, Secreted, Membrane raft, Cell s... Membrane, Secreted, Disulfide bond, Kinase, Tr... Receptors Act.TGFB;Kinase NaN NaN True CellChatDB v1
TGFB3_TGFBR1_TGFBR2 TGFb TGFB3 TGFBR2 TGFb agonist TGFb antagonist NaN TGFb inhibition receptor KEGG: hsa04350 Secreted Signaling TGFB3 - (TGFBR1+TGFBR2) ... TGFBR2, TGFBR1 Protein kinase superfamily, TKL Ser/Thr protei... Cell membrane, Secreted, Membrane raft, Cell s... Membrane, Secreted, Disulfide bond, Kinase, Tr... Receptors Act.TGFB;Kinase NaN NaN True CellChatDB v1

5 rows × 27 columns

Remove ligand and receptors not present in the object#

[13]:

rows_keep=np.logical_and( lr_df['ligand'].isin(adata.var_names), lr_df['receptor'].isin(adata.var_names) )

[14]:

lr_df=lr_df.loc[rows_keep].copy()

Create the ligand-receptor anndata object#

[15]:

%%time
lr_adata=la.tl.prepareLRInteraction(
    adata, # object used to construt the LR object
    lr_df, # dataframe to use for interactions
    number_nearest_neighbors=20,
    use_rep_spatial='X_spatial'
)

CPU times: user 2.41 s, sys: 168 ms, total: 2.58 s
Wall time: 2.58 s

Run LARIS#

To:

  1. identify spatially variable and specific ligand-receptor interactions

  2. infer the interation directionality at cell type level if by_celltype set to True

[16]:

%%time
import warnings
warnings.filterwarnings("ignore")
LARIS_variable_interactions, res_LARIS=la.tl.runLARIS(
    lr_adata,
    adata,
    use_rep='X_spatial',
    n_nearest_neighbors=20,
    random_seed=27,
    n_repeats=5,
    mu = 0.40, # larger value indicates more consideration for specificity
    sigma=100,
    remove_lowly_expressed=False,
    expressed_pct=0.1,
    n_cells_expressed_threshold=100,
    n_top_lr=lr_adata.shape[1],

    by_celltype=True,
    ### Parameters for cell type-level inference when by_celltype set to True
    groupby='cell_type', # label to group by
    use_rep_spatial='X_spatial', # spatial coordinates
    mu_celltype=100, # higher value puts more emphasis on cell type specificity
    expressed_pct_celltype=0.1, # expression percentage cut off per cell type
    remove_lowly_expressed_celltype=False,
    mask_threshold=1e-6, # mask for cosg, lower value is less restrictive
    n_neighbors_permutation=30,
    score_threshold= 1e-10,
    spatial_weight = 3.0,
)


======================================================================
LARIS ANALYSIS
======================================================================

Input data: 5695 cells × 1985 LR pairs
Mode: Cell type-specific analysis

--- Step 1: Calculating spatial specificity scores ---
  - Using 20 nearest neighbors
  - Regularization parameter μ = 0.4
  - Random repeats: 5
  - Generating random permutations...
  ✓ Identified 1985 top spatially-specific LR pairs
  - Score range: [-0.0431, 0.6720]

======================================================================
CELL TYPE-SPECIFIC ANALYSIS
======================================================================

Analyzing 14 cell types from 'cell_type'
Cell types: ['B_germinal_center', 'B_memory', 'B_naive', 'FDC_LZDZ', 'MRC', 'NK', 'T_CD4', 'T_CD8', 'T_double_neg', 'T_follicular_helper']...

======================================================================
LARIS CELL TYPE ANALYSIS
======================================================================

--- Step 1: Calculating ligand and receptor cell type specificity ---
  ✓ Computed specificity for 962 genes across 14 cell types

--- Step 2: Calculating diffused LR-score distribution ---
  ✓ Computed diffused scores for 1985 interactions

--- Step 3: Computing interaction scores (spatial_weight=3.0) ---
  ✓ Processed 1985 interactions

--- Step 3.5: Rescaling interaction scores ---
  - Scaling factor: 0.770511 (based on top 100 scores)

--- Step 4: Incorporating spatial cell type co-localization ---
  ✓ Calculated co-localization for 1985 interactions and 196 sender-receiver pairs
  ✓ Applied co-localization weights

--- Step 4.5: Cleaning up numerical precision (threshold=1e-10) ---
  - Before: 339,668 scores < threshold, 261,703 exactly zero
  - After: 339,668 scores set to exactly 0.0

======================================================================
STATISTICAL SIGNIFICANCE TESTING
======================================================================

--- Step 5.1: Preparing background interaction sets ---
  - Calculating summary statistics for 1985 interactions...
  - Building KDTree to find 30 nearest neighbors...
  - Background set prepared: 1985 interactions

--- Step 5.2: Calculating p-values (1,000 permutations) ---
  - Method: Standard
  - Chunk size: 50,000
  - Testing 196 sender-receiver pairs

  ✓ Calculated p-values for 389,060 interaction combinations

--- Step 5.3: Applying FDR correction (Benjamini-Hochberg) ---
  - Strategy: Pre-filtered
  - Pre-filter threshold: 0.0

  ✓ Corrected 196 sender-receiver pairs
  - Interactions tested: 49,392
  - Interactions filtered: 339,668

  Significant interactions:
  - FDR < 0.05: 4,498 (1.16%)
  - FDR < 0.01: 1,700 (0.44%)

======================================================================
STATISTICAL TESTING COMPLETE
======================================================================

======================================================================
LARIS CELL TYPE ANALYSIS COMPLETE
======================================================================

Final results: 389,060 sender-receiver-LR combinations
Score range: [0.000000, 0.900057]

======================================================================
ANALYSIS COMPLETE
======================================================================

Results summary:
  - Spatially-specific LR pairs: 1985
  - Cell type combinations: 389,060
  - Significant interactions (FDR < 0.05): 4,498
CPU times: user 2min 20s, sys: 268 ms, total: 2min 20s
Wall time: 2min 20s

Check the spatial variable LR interaction#

[17]:

LARIS_variable_interactions.shape

[17]:

(1985, 4)

[18]:

res_LARIS.shape

[18]:

(389060, 9)

[19]:

LARIS_variable_interactions.head(10)

[19]:
ligand receptor score Rank
SEMA4A::PLXNA4 SEMA4A PLXNA4 0.671992 0
LAMB1::CD44 LAMB1 CD44 0.670729 1
FCER2::CR2 FCER2 CR2 0.658593 2
APP::CD74 APP CD74 0.658068 3
PTPRC::MRC1 PTPRC MRC1 0.654901 4
PTPRC::CD22 PTPRC CD22 0.641263 5
LAMB3::CD44 LAMB3 CD44 0.638657 6
SEMA4D::PLXNB1 SEMA4D PLXNB1 0.635213 7
COL4A1::CD44 COL4A1 CD44 0.634731 8
IL4::IL4R IL4 IL4R 0.633580 9 
[20]:

res_LARIS.head()

[20]:
sender receiver interaction_score ligand receptor interaction_name p_value p_value_fdr nlog10_p_value_fdr
0 B_naive B_naive 0.900057 FCER2 CR2 FCER2::CR2 0.000999 0.010570 1.975922
1 B_memory B_memory 0.538235 COL4A3 CD44 COL4A3::CD44 0.000999 0.012469 1.904168
2 B_germinal_center B_germinal_center 0.397066 SEMA4A PLXNB2 SEMA4A::PLXNB2 0.000999 0.014026 1.853067
3 T_double_neg NK 0.344916 CLEC2D KLRB1 CLEC2D::KLRB1 0.000999 0.009419 2.025989
4 B_memory B_naive 0.337151 COL4A3 CD44 COL4A3::CD44 0.000999 0.012947 1.887830 
[21]:

pd.Series(res_LARIS['p_value']<0.05).value_counts()

[21]:

p_value
False    380288
True       8772
Name: count, dtype: int64

[22]:

pd.Series(res_LARIS['p_value_fdr']<0.05).value_counts()

[22]:

p_value_fdr
False    384562
True       4498
Name: count, dtype: int64

[23]:

sc.pl.dotplot(lr_adata,
              LARIS_variable_interactions.index[:10],
              groupby='region_name',
             standard_scale='var',
             cmap='Spectral_r')
../_images/notebooks_04_LARIS_tutorial_33_0.png 
[24]:

sc.pl.dotplot(lr_adata,
              LARIS_variable_interactions.index[:10],
              groupby='cell_type',
             standard_scale='var',
             cmap='Spectral_r')
../_images/notebooks_04_LARIS_tutorial_34_0.png 
[25]:

x_width=adata.obsm['X_spatial'][:,0].max()-adata.obsm['X_spatial'][:,0].min()
y_width=adata.obsm['X_spatial'][:,1].max()-adata.obsm['X_spatial'][:,1].min()

plt.rcParams['figure.figsize'] = 2.5, 2.5*y_width/x_width

sc.pl.embedding(
    lr_adata,
    basis='X_spatial',
    color=LARIS_variable_interactions.index[:4],
    cmap=pos_cmap,
 #   vmin=1,
  #  vmax=1.5,
      # palette=self_palette2,
       ncols=4,
    # size=80,
    frameon=False)
plt.rcParams['figure.figsize'] = 4, 4
../_images/notebooks_04_LARIS_tutorial_35_0.png

Check the cell type-level ligand-receptor interaction inferred by LARIS#

[26]:

res_LARIS.head(10)

[26]:
sender receiver interaction_score ligand receptor interaction_name p_value p_value_fdr nlog10_p_value_fdr
0 B_naive B_naive 0.900057 FCER2 CR2 FCER2::CR2 0.000999 0.010570 1.975922
1 B_memory B_memory 0.538235 COL4A3 CD44 COL4A3::CD44 0.000999 0.012469 1.904168
2 B_germinal_center B_germinal_center 0.397066 SEMA4A PLXNB2 SEMA4A::PLXNB2 0.000999 0.014026 1.853067
3 T_double_neg NK 0.344916 CLEC2D KLRB1 CLEC2D::KLRB1 0.000999 0.009419 2.025989
4 B_memory B_naive 0.337151 COL4A3 CD44 COL4A3::CD44 0.000999 0.012947 1.887830
5 B_memory B_memory 0.324935 COL4A4 CD44 COL4A4::CD44 0.000999 0.012469 1.904168
6 B_memory B_memory 0.282586 SEMA7A PLXNC1 SEMA7A::PLXNC1 0.000999 0.012469 1.904168
7 pDC B_naive 0.276228 APP CD74 APP::CD74 0.000999 0.009686 2.013857
8 B_naive B_naive 0.258380 APP CD74 APP::CD74 0.033966 0.270686 0.567535
9 B_memory B_naive 0.244977 APP CD74 APP::CD74 0.026973 0.292850 0.533355 
[27]:

res_LARIS.head(10)

[27]:
sender receiver interaction_score ligand receptor interaction_name p_value p_value_fdr nlog10_p_value_fdr
0 B_naive B_naive 0.900057 FCER2 CR2 FCER2::CR2 0.000999 0.010570 1.975922
1 B_memory B_memory 0.538235 COL4A3 CD44 COL4A3::CD44 0.000999 0.012469 1.904168
2 B_germinal_center B_germinal_center 0.397066 SEMA4A PLXNB2 SEMA4A::PLXNB2 0.000999 0.014026 1.853067
3 T_double_neg NK 0.344916 CLEC2D KLRB1 CLEC2D::KLRB1 0.000999 0.009419 2.025989
4 B_memory B_naive 0.337151 COL4A3 CD44 COL4A3::CD44 0.000999 0.012947 1.887830
5 B_memory B_memory 0.324935 COL4A4 CD44 COL4A4::CD44 0.000999 0.012469 1.904168
6 B_memory B_memory 0.282586 SEMA7A PLXNC1 SEMA7A::PLXNC1 0.000999 0.012469 1.904168
7 pDC B_naive 0.276228 APP CD74 APP::CD74 0.000999 0.009686 2.013857
8 B_naive B_naive 0.258380 APP CD74 APP::CD74 0.033966 0.270686 0.567535
9 B_memory B_naive 0.244977 APP CD74 APP::CD74 0.026973 0.292850 0.533355 
[28]:

x_width=adata.obsm['X_spatial'][:,0].max()-adata.obsm['X_spatial'][:,0].min()
y_width=adata.obsm['X_spatial'][:,1].max()-adata.obsm['X_spatial'][:,1].min()

plt.rcParams['figure.figsize'] = 2.5, 2.5*y_width/x_width

sc.pl.embedding(
    lr_adata,
    basis='X_spatial',
    color=res_LARIS['interaction_name'][:12].unique(),
    cmap=pos_cmap,
 #   vmin=1,
  #  vmax=1.5,
      # palette=self_palette2,
       ncols=3,
      # size=80,
    frameon=False)
plt.rcParams['figure.figsize'] = 4, 4
../_images/notebooks_04_LARIS_tutorial_39_0.png 
[29]:

x_width=lr_adata.obsm['X_umap'][:,0].max()-lr_adata.obsm['X_umap'][:,0].min()
y_width=lr_adata.obsm['X_umap'][:,1].max()-lr_adata.obsm['X_umap'][:,1].min()

plt.rcParams['figure.figsize'] = 2.5, 2.5*y_width/x_width

sc.pl.embedding(
    lr_adata,
    basis='X_umap',
    color=res_LARIS['interaction_name'][:12].unique(),
    cmap=pos_cmap,
 #   vmin=1,
  #  vmax=1.5,
      # palette=self_palette2,
       ncols=3,
    # size=50,
    frameon=False)
plt.rcParams['figure.figsize'] = 4, 4
../_images/notebooks_04_LARIS_tutorial_40_0.png

Filter hits by ligand/receptor/cell type#

[30]:

res_LARIS[res_LARIS['sender'] == 'MRC'].head(10)

[30]:
sender receiver interaction_score ligand receptor interaction_name p_value p_value_fdr nlog10_p_value_fdr
17 MRC T_CD4 0.184022 CCL21 CCR7 CCL21::CCR7 0.000999 0.016983 1.769986
37 MRC T_CD4 0.137322 CCL19 CCR7 CCL19::CCR7 0.030969 0.324221 0.489159
59 MRC B_memory 0.098216 COL1A1 CD44 COL1A1::CD44 0.000999 0.013820 1.859508
65 MRC T_CD4 0.090855 COL1A1 CD44 COL1A1::CD44 0.000999 0.016983 1.769986
71 MRC T_CD4 0.083999 LAMA4 CD44 LAMA4::CD44 0.028971 0.324221 0.489159
73 MRC T_double_neg 0.083307 LAMA4 CD44 LAMA4::CD44 0.000999 0.008891 2.051045
84 MRC B_memory 0.069352 COL1A2 CD44 COL1A2::CD44 0.000999 0.013820 1.859508
90 MRC T_CD8 0.065867 COL1A2 CD44 COL1A2::CD44 0.000999 0.013362 1.874141
101 MRC T_CD8 0.056886 LAMA4 CD44 LAMA4::CD44 0.000999 0.013362 1.874141
116 MRC T_CD4 0.050082 COL1A2 CD44 COL1A2::CD44 0.103896 0.483723 0.315404

Look for enrichment in different tissue regions#

[31]:

# Import cosg, could also use a Wilcoxon test as well to find region enrichment
import cosg

[32]:

adata.obs.head().T

[32]:
AAACCCAAGCGCCTTG-1 AAACCCAAGTGGACGT-1 AAACCCACAGAAGTGC-1 AAACCCAGTCATTGCA-1 AAACCCATCATCGCAA-1
n_genes_by_counts 1042 2382 3734 1928 441
log1p_n_genes_by_counts 6.949856 7.776115 8.225503 7.564757 6.09131
total_counts 1495.0 4036.0 7292.0 3039.0 554.0
log1p_total_counts 7.31055 8.303257 8.89467 8.019613 6.318968
pct_counts_in_top_50_genes 18.795987 15.237859 9.421284 13.754525 27.617329
pct_counts_in_top_100_genes 27.826087 21.382557 14.413055 20.500165 38.447653
pct_counts_in_top_200_genes 41.204013 30.401388 21.969281 30.404738 56.498195
pct_counts_in_top_500_genes 63.745819 47.59663 38.137685 50.70747 100.0
total_counts_mt 3.0 8.0 100.0 31.0 2.0
log1p_total_counts_mt 1.386294 2.197225 4.61512 3.465736 1.098612
pct_counts_mt 0.200669 0.198216 1.371366 1.020072 0.361011
total_counts_ribo 87.0 73.0 364.0 232.0 45.0
log1p_total_counts_ribo 4.477337 4.304065 5.899898 5.451038 3.828641
pct_counts_ribo 5.819398 1.808722 4.991772 7.63409 8.122744
n_genes 1042 2382 3734 1928 441
cell_type T_CD4 plasma B_germinal_center B_naive B_memory
region_name Paracortex Dark Zone Light Zone Marginal Zone Interfollicular Zone 
[33]:

# Run cosg to identify markers
n_gene=30 # number of genes to save
groupby='region_name'
cosg.cosg(lr_adata,
    key_added='cosg',
    mu=100,
    expressed_pct=0.15, # minimum % of expression to make cut off
    remove_lowly_expressed=True,
    n_genes_user=100, # How many genes to append
    groupby=groupby)

[34]:

sc.tl.dendrogram(lr_adata, groupby=groupby, use_rep='X_pca')
df_tmp=pd.DataFrame(lr_adata.uns['cosg']['names'][:8,]).T
df_tmp=df_tmp.reindex(lr_adata.uns['dendrogram_'+groupby]['categories_ordered'])
marker_genes_list={idx: list(row.values) for idx, row in df_tmp.iterrows()}
marker_genes_list = {k: v for k, v in marker_genes_list.items() if not any(isinstance(x, float) for x in v)}

Storing dendrogram info using `.uns['dendrogram_region_name']`

[35]:

sc.pl.dotplot(lr_adata, marker_genes_list,
             groupby=groupby,
             dendrogram=True,
            swap_axes=False,
             standard_scale='var',
             cmap='Spectral_r')
../_images/notebooks_04_LARIS_tutorial_48_0.png

Visualize the ligand-receptor interaction score in spatial plots#

[36]:

x_width=adata.obsm['X_spatial'][:,0].max()-adata.obsm['X_spatial'][:,0].min()
y_width=adata.obsm['X_spatial'][:,1].max()-adata.obsm['X_spatial'][:,1].min()

plt.rcParams['figure.figsize'] = 2.5, 2.5*y_width/x_width

sc.pl.embedding(
    lr_adata,
    basis='X_spatial',
    color=['BMP7::BMPR1A','IL21::IL21R','FCER2::CR2','CCL19::CCR7'],
    cmap=pos_cmap,
    ncols=4,
    # size=120,
    frameon=False)
plt.rcParams['figure.figsize'] = 4, 4
../_images/notebooks_04_LARIS_tutorial_50_0.png

Visualize the ligand-receptor interaction score in UMAP plots#

[37]:

x_width=adata.obsm['X_spatial'][:,0].max()-adata.obsm['X_spatial'][:,0].min()
y_width=adata.obsm['X_spatial'][:,1].max()-adata.obsm['X_spatial'][:,1].min()

plt.rcParams['figure.figsize'] = 2.5, 2.5*y_width/x_width

sc.pl.embedding(
    lr_adata,
    basis='X_umap',
    color=['BMP7::BMPR1A','IL21::IL21R','FCER2::CR2','CCL19::CCR7'],
    cmap=pos_cmap,
    ncols=4,
    # size=60,
    frameon=False)
plt.rcParams['figure.figsize'] = 4, 4
../_images/notebooks_04_LARIS_tutorial_52_0.png

Visualize LR interaction and cell-cell communication in dfferent ways#

Cell-cell communication (CCC) Heatmap#

First, check the default settings:

[38]:

la.pl.plotCCCHeatmap(
    res_LARIS
)
../_images/notebooks_04_LARIS_tutorial_56_0.png

Use the top ones:

[39]:

fig=la.pl.plotCCCHeatmap(
    res_LARIS,
    cmap='plasma',
    filter_significant=False,
    n_top=2000,
)
../_images/notebooks_04_LARIS_tutorial_58_0.png

Filter by the p value and score:

[40]:

la.pl.plotCCCHeatmap(
    res_LARIS,
    cmap='Purples',
    # n_top=2000,
    figsize=(6, 5),
    axis_label_fontsize=16,
    tick_fontsize=12,
    cbar_label_fontsize=16,
    cbar_tick_fontsize=12,
    filter_significant=True,
    p_value_col='p_value_fdr',
    threshold=0.05,
    filter_by_interaction_score=True,
    threshold_interaction_score=0.01,
    show_borders=False,
    cluster=True,
)
../_images/notebooks_04_LARIS_tutorial_60_0.png

Change the threshold:

[41]:

la.pl.plotCCCHeatmap(
    res_LARIS,
    cmap='Purples',
    # n_top=2000,
    figsize=(6, 5),
    axis_label_fontsize=16,
    tick_fontsize=12,
    cbar_label_fontsize=16,
    cbar_tick_fontsize=12,
    filter_significant=True,
    p_value_col='p_value_fdr',
    threshold=0.1,
    filter_by_interaction_score=True,
    threshold_interaction_score=0.01,
    show_borders=False,
    cluster=True,
)
../_images/notebooks_04_LARIS_tutorial_62_0.png

CCC Network#

First, check the default settings:

[42]:

la.pl.plotCCCNetwork(
    res_LARIS,
    'B_germinal_center',
    interaction_direction='sending',
    adata=adata,
)
../_images/notebooks_04_LARIS_tutorial_65_0.png

Check the receiving pattern for a target cell type:

[43]:

la.pl.plotCCCNetwork(
    res_LARIS,
    'B_germinal_center',
    interaction_direction='receiving',
    adata=adata,
    # edge_width_scale=20,
    # n_top=3000,
    filter_significant=True,
    p_value_col='p_value_fdr',
    threshold=0.05,
    filter_by_interaction_score=True,
    threshold_interaction_score=0.01,
    figsize=[10, 10],
)
../_images/notebooks_04_LARIS_tutorial_67_0.png

Adjust the edge_with_scale parameter and figsize parameter:

[44]:

la.pl.plotCCCNetwork(
    res_LARIS,
    'B_germinal_center',
    interaction_direction='receiving',
    adata=adata,
    edge_width_scale=15,
    # n_top=3000,
    filter_significant=True,
    p_value_col='p_value_fdr',
    threshold=0.05,
    filter_by_interaction_score=True,
    threshold_interaction_score=0.01,
    figsize=[8, 8],
)
../_images/notebooks_04_LARIS_tutorial_69_0.png

CCC Network (cumulative)#

[45]:

la.pl.plotCCCNetworkCumulative(
    res_LARIS,
    adata=adata,
    groupby='cell_type',
    filter_significant=True,
    p_value_col='p_value_fdr',
    threshold=0.05,
    filter_by_interaction_score=True,
    threshold_interaction_score=0.01,
    edge_width_scale=25,
    figsize=[10, 10],

)
../_images/notebooks_04_LARIS_tutorial_71_0.png

Add cutoff = 0.1 to filter out some edges:

[46]:

la.pl.plotCCCNetworkCumulative(
    res_LARIS,
    adata=adata,
    groupby='cell_type',
    filter_significant=True,
    p_value_col='p_value_fdr',
    threshold=0.05,
    filter_by_interaction_score=True,
    threshold_interaction_score=0.01,
    edge_width_scale=25,
    # figsize=[6, 6],
    cutoff=0.1,

)
../_images/notebooks_04_LARIS_tutorial_73_0.png

CCC DotPlot#

First, check the default:

[47]:

la.pl.plotCCCDotPlot(
    res_LARIS,
    interactions_to_plot=['CCL21::CCR7', "CD40LG::CD40",  "PDCD1LG2::PDCD1", "PTPRC::CD22", "LAMA4::CD44",
                        "COL1A1::CD44",
                        "GLS::GRIK1"],
    senders=["MRC", "MRC", "MRC", "MRC", "T_follicular_helper", "B_germinal_center"],
    receivers=["T_CD4", "T_follicular_helper", "B_naive", "B_memory",  "B_germinal_center",'T_follicular_helper'],
)
../_images/notebooks_04_LARIS_tutorial_76_0.png

Adjust paramters for figsize and cmap:

[48]:

la.pl.plotCCCDotPlot(
    res_LARIS,
    interactions_to_plot=['CCL21::CCR7', "CD40LG::CD40",  "PDCD1LG2::PDCD1", "PTPRC::CD22", "LAMA4::CD44",
                        "COL1A1::CD44",
                        "GLS::GRIK1"],
    senders=["MRC", "MRC", "MRC", "MRC", "T_follicular_helper", "B_germinal_center"],
    receivers=["T_CD4", "T_follicular_helper", "B_naive", "B_memory",  "B_germinal_center",'T_follicular_helper'],

    # n_top=3000,
    bubble_size=500,
    cmap='YlGn',
    filter_significant=True,
    p_value_col='p_value_fdr',
    threshold=0.05,
    filter_by_interaction_score=True,
    threshold_interaction_score=0.01,
    figsize=[8,6],
    show_grid=False,
)
../_images/notebooks_04_LARIS_tutorial_78_0.png

Adjust the show_grid = True parameter:

[49]:

la.pl.plotCCCDotPlot(
    res_LARIS,
    interactions_to_plot=['CCL21::CCR7', "CD40LG::CD40",  "PDCD1LG2::PDCD1", "PTPRC::CD22", "LAMA4::CD44",
                        "COL1A1::CD44",
                        "GLS::GRIK1"],
    senders=["MRC", "MRC", "MRC", "MRC", "T_follicular_helper", "B_germinal_center"],
    receivers=["T_CD4", "T_follicular_helper", "B_naive", "B_memory",  "B_germinal_center",'T_follicular_helper'],


    bubble_size=500,
    cmap='Purples',
    # filter_significant=False,
    # n_top=3000,
    filter_significant=True,
    p_value_col='p_value_fdr',
    threshold=0.05,
    filter_by_interaction_score=True,
    threshold_interaction_score=0.01,
    figsize=[8,6],
    show_grid=True,
)
../_images/notebooks_04_LARIS_tutorial_80_0.png 
[50]:

res_LARIS.head(30)

[50]:
sender receiver interaction_score ligand receptor interaction_name p_value p_value_fdr nlog10_p_value_fdr
0 B_naive B_naive 0.900057 FCER2 CR2 FCER2::CR2 0.000999 0.010570 1.975922
1 B_memory B_memory 0.538235 COL4A3 CD44 COL4A3::CD44 0.000999 0.012469 1.904168
2 B_germinal_center B_germinal_center 0.397066 SEMA4A PLXNB2 SEMA4A::PLXNB2 0.000999 0.014026 1.853067
3 T_double_neg NK 0.344916 CLEC2D KLRB1 CLEC2D::KLRB1 0.000999 0.009419 2.025989
4 B_memory B_naive 0.337151 COL4A3 CD44 COL4A3::CD44 0.000999 0.012947 1.887830
5 B_memory B_memory 0.324935 COL4A4 CD44 COL4A4::CD44 0.000999 0.012469 1.904168
6 B_memory B_memory 0.282586 SEMA7A PLXNC1 SEMA7A::PLXNC1 0.000999 0.012469 1.904168
7 pDC B_naive 0.276228 APP CD74 APP::CD74 0.000999 0.009686 2.013857
8 B_naive B_naive 0.258380 APP CD74 APP::CD74 0.033966 0.270686 0.567535
9 B_memory B_naive 0.244977 APP CD74 APP::CD74 0.026973 0.292850 0.533355
10 B_memory T_CD8 0.237543 COL4A3 CD44 COL4A3::CD44 0.000999 0.012488 1.903524
11 B_germinal_center B_germinal_center 0.212287 PTPRC CD22 PTPRC::CD22 0.000999 0.014026 1.853067
12 B_germinal_center B_germinal_center 0.208078 MIF CD74 MIF::CD74 0.000999 0.014026 1.853067
13 B_memory B_germinal_center 0.199604 PTPRC CD22 PTPRC::CD22 0.000999 0.013986 1.854306
14 B_naive B_memory 0.194665 FCER2 CR2 FCER2::CR2 0.000999 0.013387 1.873330
15 B_naive B_memory 0.192835 COL4A3 CD44 COL4A3::CD44 0.028971 0.286856 0.542336
16 plasma plasma 0.186626 WNT5B FZD6 WNT5B::FZD6 0.000999 0.010347 1.985195
17 MRC T_CD4 0.184022 CCL21 CCR7 CCL21::CCR7 0.000999 0.016983 1.769986
18 pDC B_memory 0.179336 APP CD74 APP::CD74 0.000999 0.009078 2.042016
19 T_double_neg B_germinal_center 0.178474 PECAM1 CD38 PECAM1::CD38 0.000999 0.008242 2.083981
20 B_naive B_naive 0.177099 PTPRC CD22 PTPRC::CD22 0.070929 0.323121 0.490635
21 B_germinal_center B_naive 0.170844 MIF CD74 MIF::CD74 0.000999 0.012367 1.907738
22 B_naive B_memory 0.167823 APP CD74 APP::CD74 0.063936 0.368617 0.433425
23 plasma plasma 0.165556 PECAM1 CD38 PECAM1::CD38 0.000999 0.010347 1.985195
24 B_memory B_naive 0.162031 PTPRC CD22 PTPRC::CD22 0.067932 0.343595 0.463954
25 B_naive B_memory 0.159976 TGFB1 TGFBR2 TGFB1::TGFBR2 0.067932 0.368617 0.433425
26 T_follicular_helper B_germinal_center 0.158691 PTPRC CD22 PTPRC::CD22 0.000999 0.010323 1.986194
27 B_naive B_naive 0.158079 TGFB1 TGFBR2 TGFB1::TGFBR2 0.023976 0.245754 0.609499
28 B_memory B_memory 0.154764 APP CD74 APP::CD74 0.101898 0.397187 0.401005
29 B_naive B_memory 0.151714 ANGPTL1 ITGB1 ANGPTL1::ITGB1 0.064935 0.368617 0.433425 
[51]:

res_LARIS[res_LARIS['sender']=='FDC_LZDZ']

[51]:
sender receiver interaction_score ligand receptor interaction_name p_value p_value_fdr nlog10_p_value_fdr
109 FDC_LZDZ B_memory 0.054153 FN1 ITGA4 FN1::ITGA4 0.000999 0.013291 1.876441
124 FDC_LZDZ B_memory 0.046145 FN1 CD44 FN1::CD44 0.000999 0.013291 1.876441
135 FDC_LZDZ mDC 0.043458 FN1 ITGA4 FN1::ITGA4 0.000999 0.007473 2.126533
149 FDC_LZDZ FDC_LZDZ 0.039254 SLC1A2 GRIA3 SLC1A2::GRIA3 0.000999 0.012916 1.888884
151 FDC_LZDZ T_CD8 0.038067 FN1 CD44 FN1::CD44 0.000999 0.011748 1.930027
... ... ... ... ... ... ... ... ... ...
389053 FDC_LZDZ FDC_LZDZ 0.000000 NRG2 ERBB4 NRG2::ERBB4 1.000000 1.000000 0.000000
389054 FDC_LZDZ FDC_LZDZ 0.000000 LAMC2 ITGB4 LAMC2::ITGB4 1.000000 1.000000 0.000000
389055 FDC_LZDZ FDC_LZDZ 0.000000 NRG1 ITGB4 NRG1::ITGB4 1.000000 1.000000 0.000000
389056 FDC_LZDZ FDC_LZDZ 0.000000 NRG3 ERBB4 NRG3::ERBB4 1.000000 1.000000 0.000000
389057 FDC_LZDZ FDC_LZDZ 0.000000 LAMA3 ITGB4 LAMA3::ITGB4 1.000000 1.000000 0.000000

27790 rows × 9 columns

CCC DotPlot (Facet)#

[52]:

la.pl.plotCCCDotPlotFacet(
    res_LARIS,
    senders=["MRC", "FDC_LZDZ", "B_naive", 'T_follicular_helper','B_germinal_center', 'T_CD8', 'T_CD4'],
    receivers=[
        "B_naive",
        "B_germinal_center",
        "B_memory",
        "plasma",
        'T_follicular_helper',
        'MRC',
        'T_CD4',
        'T_CD8'

    ],
    interactions_to_plot=[
        "COL1A1::CD44",
        'APP::CD74',
        "IL7::IL7R",
        "IL21::IL21R",
        'PDCD1LG2::PDCD1',
        "C3::CR2",  "CLEC2D::KLRB1",
        'FN1::ITGA4',
        'PDGFD::PDGFRB','PTPRC::CD22', ],
)
../_images/notebooks_04_LARIS_tutorial_84_0.png

Use top ones, no filtering on P values or scores:

[53]:

la.pl.plotCCCDotPlotFacet(
    res_LARIS,
    senders=["MRC", "FDC_LZDZ", "B_naive", 'T_follicular_helper','B_germinal_center', 'T_CD8', 'T_CD4'],
    receivers=[
        "B_naive",
        "B_germinal_center",
        "B_memory",
        "plasma",
        'T_follicular_helper',
        'MRC',
        'T_CD4',
        'T_CD8'

    ],
    interactions_to_plot=[
        "COL1A1::CD44",
        'APP::CD74',
        "IL7::IL7R",
        "IL21::IL21R",
        'PDCD1LG2::PDCD1',
        "C3::CR2",  "CLEC2D::KLRB1",
        'FN1::ITGA4',
        'PDGFD::PDGFRB','PTPRC::CD22', ],

    cmap='YlGn',

    filter_significant=False, filter_by_interaction_score=False, n_top=3000,

    # filter_significant=True, filter_by_interaction_score=True,
    p_value_col='p_value',
    threshold=0.05,
    threshold_interaction_score=0.01,
    # ncol=6,
)

No filters applied. Using top 3000 interactions.
../_images/notebooks_04_LARIS_tutorial_86_1.png

Change the ncol to control the number of columns, and also turn off the grid lines by show_grid = False:

[54]:

la.pl.plotCCCDotPlotFacet(
    res_LARIS,
    senders=["MRC", "FDC_LZDZ", "B_naive", 'T_follicular_helper','B_germinal_center', 'T_CD8', 'T_CD4'],
    receivers=[
        "B_naive",
        "B_germinal_center",
        "B_memory",
        "plasma",
        'T_follicular_helper',
        'MRC',
        'T_CD4',
        'T_CD8'

    ],
    interactions_to_plot=[
        "COL1A1::CD44",
        'APP::CD74',
        "IL7::IL7R",
        "IL21::IL21R",
        'PDCD1LG2::PDCD1',
        "C3::CR2",  "CLEC2D::KLRB1",
        'FN1::ITGA4',
        'PDGFD::PDGFRB','PTPRC::CD22', ],


    cmap=pos_cmap,

    filter_significant=False, filter_by_interaction_score=False, n_top=3000,

    # filter_significant=True, filter_by_interaction_score=True,
    p_value_col='p_value',
    threshold=0.05,
    threshold_interaction_score=0.01,
    ncol=4,
    show_grid = False
)

No filters applied. Using top 3000 interactions.
../_images/notebooks_04_LARIS_tutorial_88_1.png

CCC DotPlot (Facet), facet by receiver#

[55]:

la.pl.plotCCCDotPlotFacet(
    res_LARIS,
    facet_by='receiver',
    senders=["MRC", "FDC_LZDZ", "B_naive", 'T_follicular_helper','B_germinal_center', 'T_CD8', 'T_CD4'],
    receivers=[
        "B_naive",
        "B_germinal_center",
        "B_memory",
        "plasma",
        'T_follicular_helper',
        'MRC',
        'T_CD4',
        'T_CD8'

    ],
    interactions_to_plot=[
        "COL1A1::CD44",
        'APP::CD74',
        "IL7::IL7R",
        "IL21::IL21R",
        'PDCD1LG2::PDCD1',
        "C3::CR2",  "CLEC2D::KLRB1",
        'FN1::ITGA4',
        'PDGFD::PDGFRB','PTPRC::CD22', ],


    cmap=pos_cmap,
    # filter_significant=False, n_top=3000,

    filter_significant=True,
    p_value_col='p_value',
    threshold=0.05,
    filter_by_interaction_score=True,
    threshold_interaction_score=0.01,
    ncol=3,
)
../_images/notebooks_04_LARIS_tutorial_90_0.png

Create combined object with LR scores and gene expression combined#

[56]:

adata_dotplot = la.pl.prepareDotPlotAdata(lr_adata, adata)

Created combined AnnData: 5695 cells × 27568 features

[57]:

la.pl.plotLRDotPlot(
    adata_dotplot,
    ["FCER2::CR2", "IL7::IL7R", 'PDCD1LG2::PDCD1', "C3::CR2", "IL21::IL21R",'PDGFD::PDGFRB','TNFSF13B::TNFRSF13C'],
    groupby='cell_type',
    cmap_interaction='Spectral_r',
    cmap_ligand='Purples',
    cmap_receptor='Reds',
    figsize=[18, 5],
)
../_images/notebooks_04_LARIS_tutorial_94_0.png 
[58]:

la.pl.plotLRDotPlot(
    adata_dotplot,
    ['ENTPD1::TMIGD3', 'TGFB1::ACVR1', 'COL6A3::ITGA10'],
    groupby='cell_type',
    cmap_interaction='Purples',
    cmap_ligand='Blues',
    cmap_receptor='Reds',
    figsize=[18, 4],
)
../_images/notebooks_04_LARIS_tutorial_95_0.png 
[59]:

la.pl.plotLRDotPlot(
    adata_dotplot,
    ['ENTPD1::TMIGD3', 'TGFB1::ACVR1', 'COL6A3::ITGA10'],
    groupby='cell_type',
    cmap_interaction='Spectral_r',
    cmap_ligand='Purples',
    cmap_receptor='Reds',
    figsize=[6, 10],
    orientation='vertical'
)
../_images/notebooks_04_LARIS_tutorial_96_0.png

Spatial plot of participating cells for an interaction#

[60]:

# Plot selected cells that have an interaction score for a certain interaction ((participating in an interaction))
la.pl.plotCCCSpatial(
    lr_adata=lr_adata,
    basis="X_spatial",
    interaction='C3::CR2',
    cell_type="cell_type",
    selected_cell_types=['B_naive', 'B_germinal_center','B_memory','plasma','MRC','FDC_LZDZ'],
    background_color='lightgrey',
    colors=['#da6c42','#7db0ea','#fbc2a9','#6b200c','#5c4799','#b31130'],  # ['#d53e4f','#fdae61','#3288bd','#3288bd','#3288bd','#3288bd','#3288bd']
    size=120,
    fig_width=6,
)
../_images/notebooks_04_LARIS_tutorial_98_0.png

Spatial plot of participating cells for an interaction, all cells#

[61]:

# Plot any cell that has an interaction score for a cetrain LR pair (participating in an interaction)
lr_adata.uns['cell_type_colors'] = adata.uns['cell_type_colors'].copy()
# Highlight all expressing cells with original colors by setting `highlight_all_expressing=True`
la.pl.plotCCCSpatial(
    lr_adata,
    basis='X_spatial',
    interaction='PDCD1LG2::PDCD1',
    cell_type='cell_type',
    background_color='lightgrey',
    highlight_all_expressing=True,
    size=120,
    fig_width=6,
)
../_images/notebooks_04_LARIS_tutorial_100_0.png 
[62]:

# Plot any cell that has an interaction score for a cetrain LR pair (participating in an interaction)
lr_adata.uns['cell_type_colors'] = adata.uns['cell_type_colors'].copy()
# Highlight all expressing cells with original colors by setting `highlight_all_expressing=True`
la.pl.plotCCCSpatial(
    lr_adata,
    basis='X_spatial',
    interaction='TNXB::ITGB1',
    cell_type='cell_type',
    background_color='lightgrey',
    highlight_all_expressing=True,
    size=120,
    fig_width=6,
)
../_images/notebooks_04_LARIS_tutorial_101_0.png 
[63]:

# Plot any cell that has an interaction score for a cetrain LR pair (participating in an interaction)
lr_adata.uns['cell_type_colors'] = adata.uns['cell_type_colors'].copy()
# Highlight all expressing cells with original colors by setting `highlight_all_expressing=True`
la.pl.plotCCCSpatial(
    lr_adata,
    basis='X_spatial',
    interaction='CCL19::CCR7',
    cell_type='cell_type',
    background_color='lightgrey',
    highlight_all_expressing=True,
    size=120,
    fig_width=6,
)
../_images/notebooks_04_LARIS_tutorial_102_0.png

Save the plot#

To save the plot, please use the save parameter in each plotting function within LARIS:

[64]:

la.pl.plotCCCHeatmap(
    res_LARIS,
    save='./LARIS_ccc_heatmap.pdf'
)

Figure saved to: ./LARIS_ccc_heatmap.pdf
../_images/notebooks_04_LARIS_tutorial_105_1.png