Running SCALAR on human cortex snRNA-seq data for ligand-receptor interaction analysis

From the previous release

This page was recovered from the PIASO v1.1.0 documentation and has not been re-run against the current release. The code is preserved as published; some calls may have changed. The pages under All tutorials are the ones executed against this release.

The original v1.1.0 site is archived at https://genecell.github.io/PIASO/.

Running SCALAR on human cortex snRNA-seq data for ligand-receptor interaction analysis

import piaso

import numpy as np
import pandas as pd
import scanpy as sc
sc.set_figure_params(dpi=80,dpi_save=300, color_map='viridis',facecolor='white')
from matplotlib import rcParams
# To modify the default figure size, use rcParams.
rcParams['figure.figsize'] = 4, 4
rcParams['font.sans-serif'] = "Arial"
rcParams['font.family'] = "Arial"
sc.settings.verbosity = 3
sc.logging.print_header()

save_dir='.../Result/single-cell/Methods/PIASO'
sc.settings.figdir = save_dir
prefix='SEAAD_SCALAR_CCI_tutorial'
import os
if not os.path.exists(save_dir):
    os.makedirs(save_dir)

sc.set_figure_params(dpi=80,dpi_save=300, color_map='viridis',facecolor='white')
rcParams['figure.figsize'] = 4, 4

adata=sc.read('.../Result/single-cell/Enhancer/SEA-AD/SEA-AD_RNA_MTG_subsample_excludeReference_20k_piaso.h5ad')

adata

sc.pl.embedding(adata,
            basis='X_umap',
           color=['Subclass'],
           palette=piaso.pl.color.d_color3,
           legend_fontoutline=2,
           legend_fontweight=5,
           cmap='Spectral_r',
           ncols=3,
           size=10,
           frameon=False)

sc.pl.embedding(adata,
    basis='X_umap',
    color=['Subclass'],
    palette=piaso.pl.color.d_color3,
    legend_fontoutline=2,
    legend_fontsize=7,
    legend_fontweight=5,
    legend_loc='on data',
    cmap='Spectral_r',
    ncols=3,
    size=10,
    frameon=False)

import cosg

adata.X.data

%%time
groupby='Subclass'
cosg.cosg(adata,
    key_added='cosg',
    # use_raw=False, layer='log1p', ## e.g., if you want to use the log1p layer in adata
    mu=100,
    expressed_pct=0.05,
    remove_lowly_expressed=True,
    n_genes_user=adata.n_vars, ### Use all the genes, to enable the calculation of transformed COSG scores
    # n_genes_user=100,
    groupby=groupby,
    return_by_group=True,
    verbosity=1
)

cosg_scores=cosg.indexByGene(
    adata.uns['cosg']['COSG'],
    # gene_key="names", score_key="scores",
    set_nan_to_zero=True,
    convert_negative_one_to_zero=True
)

cosg_scores=cosg.iqrLogNormalize(cosg_scores)

cosg_scores

### Load the mouse version
# cellchatdb=pd.read_csv('.../')
### Load the human version
cellchatdb=pd.read_csv('.../')

cellchatdb.head()

pd.Series(cellchatdb['annotation']).value_counts().head(30)

pd.Series(cellchatdb['pathway_name']).value_counts().head(30)

# # cellchatdb=cellchatdb.loc[:,['ligand', 'receptor', 'pathway_name']].copy()
cellchatdb=cellchatdb.loc[:,['ligand', 'receptor', 'annotation']].copy()

%%time
specific_interactions_cellchat = piaso.tl.runSCALAR(
    adata=adata,
    specificity_matrix=cosg_scores,
    lr_pairs=cellchatdb,
    ligand_col = 'ligand',
    receptor_col = 'receptor',
    annotation_col='annotation',
    sender_cell_types=list(adata.obs['Subclass'].cat.categories.values),
    receiver_cell_types=list(adata.obs['Subclass'].cat.categories.values),
    n_permutations=1000,
    n_nearest_neighbors=30,
    chunk_size=500000,
    random_seed=42
)

specific_interactions_cellchat

pd.Series(specific_interactions_cellchat['nlog10_p_value_fdr']>-np.log10(0.2)).value_counts()

pd.Series(specific_interactions_cellchat['nlog10_p_value_fdr']>-np.log10(0.05)).value_counts()

specific_interactions_cellchat['CellTypeXCellType']=piaso.pp.getCrossCategories(specific_interactions_cellchat, 'sender', 'receiver')
specific_interactions_cellchat['CellTypeXCellType']=specific_interactions_cellchat['CellTypeXCellType'].astype('str')
specific_interactions_cellchat['ligandXreceptor']=piaso.pp.getCrossCategories(specific_interactions_cellchat, 'ligand', 'receptor', delimiter='-->')
specific_interactions_cellchat['ligandXreceptor']=specific_interactions_cellchat['ligandXreceptor'].astype('str')

specific_interactions_cellchat.head()

len(specific_interactions_cellchat['CellTypeXCellType'].unique())

adata.obs['Subclass'].cat.categories

si_fdr=specific_interactions_cellchat[specific_interactions_cellchat['nlog10_p_value_fdr']>-np.log10(0.5)].copy()

piaso.pl.plotLigandReceptorInteraction(
    interactions_df=si_fdr,
    specificity_df=cosg_scores,
    cell_type_pairs=['L5 ET@Sst', 'L5/6 NP@Sst', 'L5 ET@Pvalb', 'L5/6 NP@Pvalb',],
    ligand_receptor_sep='-->',
    top_n=50,
    y_max=10,
    heatmap_cmap='Purples',
    shared_legend=True
)

# specific_interactions_subset=specific_interactions.loc[
#     specific_interactions['annotation'].isin(['Secreted Signaling'])
# ]

specific_interactions_cellchat_subset=specific_interactions_cellchat.loc[
    specific_interactions_cellchat['annotation'].isin(['ECM-Receptor'])
]


# specific_interactions_subset=specific_interactions.loc[
#     specific_interactions['annotation'].isin(['Cell-Cell Contact'])
# ]


# specific_interactions_cellchat_subset=specific_interactions_cellchat.loc[
#     specific_interactions_cellchat['annotation'].isin(['Non-protein Signaling'])
# ]

piaso.pl.plotLigandReceptorInteraction(
    interactions_df=specific_interactions_cellchat_subset,
    specificity_df=cosg_scores,
    cell_type_pairs=['L5 ET@Sst', 'L5/6 NP@Sst', 'L5 ET@Pvalb', 'L5/6 NP@Pvalb',],
    ligand_receptor_sep='-->',
    top_n=50,
    y_max=10,
    heatmap_cmap='Purples',
    fig_height_per_pair=6,
    fig_width=20,
    shared_legend=True
)

piaso.pl.plotLigandReceptorInteraction(
    interactions_df=si_fdr,
    specificity_df=cosg_scores,
    cell_type_pairs=['L5 ET@Sst', 'L5/6 NP@Sst', 'L5 ET@Pvalb', 'L5/6 NP@Pvalb',],
    ligand_receptor_sep='-->',
    top_n=50,
    y_max=10,
    heatmap_cmap='Purples',
    heatmap_cmap_ligand='Blues',
    heatmap_cmap_receptor='Reds',
    shared_legend=True,
    vertical_layout=False,
    fig_height_per_pair=6,
    fig_width=20,
    color_labels_by_annotation=True,

)

piaso.pl.plotLigandReceptorInteraction(
    interactions_df=si_fdr,
    specificity_df=cosg_scores,
    cell_type_pairs=['L5 ET@Sst', 'L5/6 NP@Sst', 'L5 ET@Pvalb', 'L5/6 NP@Pvalb',],
    # cell_type_pairs=['L5 NP@SST-Chrna2'],
    ligand_receptor_sep='-->',
    top_n=50,
    y_max=10,
    # heatmap_cmap='Purples',
    heatmap_cmap_ligand='Purples',
    heatmap_cmap_receptor='Reds',
    shared_legend=True,
    vertical_layout=True,
    fig_height_per_pair=6,
    fig_width=12,
    color_labels_by_annotation=True
)

piaso.pl.plotLigandReceptorInteraction(
    interactions_df=si_fdr,
    specificity_df=cosg_scores,
    cell_type_pairs=['L5 ET@Sst', 'L5/6 NP@Sst', 'L5 ET@Pvalb', 'L5/6 NP@Pvalb',],
    cell_type_sep='@',
    ligand_receptor_sep='-->',
    top_n=50,
    y_max=10,
    # heatmap_cmap='Purples',
    heatmap_cmap_ligand='Blues',
    heatmap_cmap_receptor='Reds',
    barplot_palette=piaso.pl.color.d_color10,
    shared_legend=True,
    vertical_layout=False,
    fig_height_per_pair=6,
    fig_width=20,
    color_labels_by_annotation=True,
    sort_by_category=True,
    category_agg_method='sum'
)

piaso.pl.plotLigandReceptorInteraction(
    interactions_df=si_fdr,
    specificity_df=cosg_scores,
    cell_type_pairs=['L5 ET@Sst', 'L5/6 NP@Sst', 'L5 ET@Pvalb', 'L5/6 NP@Pvalb',],
    # col_cell_type_pair='Category',
    # col_annotation= 'ConfirmedCategories',
    cell_type_sep='@',
    ligand_receptor_sep='-->',
    top_n=50,
    y_max=10,
    # heatmap_cmap='Purples',
    heatmap_cmap_ligand='Blues',
    heatmap_cmap_receptor='Reds',
    barplot_palette=piaso.pl.color.d_color10,
    shared_legend=True,
    vertical_layout=True,
    fig_height_per_pair=6,
    fig_width=12,
    color_labels_by_annotation=True,
    sort_by_category=True,
    # category_agg_method='sum'
)

si_fdr.head()

adata.obs['Subclass'].cat.categories

piaso.pl.plotLigandReceptorLollipop(
    si_fdr,
    specificity_df=cosg_scores,
    cell_type_pairs=[
                     'Microglia-PVM@L6 CT'
                    ],
    top_n=50,
    col_cell_type_pair='CellTypeXCellType', ## Specify the cell type conlumn
    # col_annotation= 'annotation',
    sort_by_category=True,
    fig_height_per_pair=4,
    fig_width=16,
    vertical_layout=False,
    background_colors=True,
    # show_grid=False,
    logfc_range=1, ### To control the log fold chaneg range
    base_circle_size=30,
    # size_dramatic_level=1,
    color_labels_by_annotation=True,
    # score_range_max=8, score_range_min=-8, ### To control the y-axis ranges

)

piaso.pl.plotLigandReceptorLollipop(
    si_fdr,
    specificity_df=cosg_scores,
    cell_type_pairs=['L5 ET@Sst', 'L5/6 NP@Sst', 'L5 ET@Pvalb', 'L5/6 NP@Pvalb',
                    ],
    top_n=50,
    col_cell_type_pair='CellTypeXCellType', ## Specify the cell type conlumn
    # col_annotation= 'annotation',
    sort_by_category=True,
    fig_height_per_pair=4,
    fig_width=16,
    vertical_layout=False,
    background_colors=True,
    # show_grid=False,
    logfc_range=1, ### To control the log fold chaneg range
    base_circle_size=30,
    # size_dramatic_level=1,
    color_labels_by_annotation=True,
    # score_range_max=8, score_range_min=-8, ### To control the y-axis ranges

)

piaso.pl.plotLigandReceptorLollipop(
    si_fdr,
    specificity_df=cosg_scores,
    cell_type_pairs=['L5 ET@Sst', 'L5/6 NP@Sst', ],
    top_n=50,
    col_cell_type_pair='CellTypeXCellType', ## Specify the cell type conlumn
    # col_annotation= 'annotation',
    sort_by_category=True,
    fig_height_per_pair=10,
    fig_width=5,
    vertical_layout=True,
    background_colors=True,
    # show_grid=False,
    logfc_range=1, ### To control the log fold chaneg range
    base_circle_size=30,
    # size_dramatic_level=1,
    color_labels_by_annotation=True,
    # score_range_max=8, score_range_min=-8, ### To control the y-axis ranges

)