¿Õ¼äת¼×éÊý¾Ý·ÖÎöÖ®niche¡°»·¡±£¨ring£©

import scanpy as sc
import squidpy as sq
import numpy as np
import pandas as pd
import os
import sys
import seaborn as sb
import matplotlib.pyplot as plt
from matplotlib import colors

#import scvi
import anndata as ad

import warnings
warnings.filterwarnings("ignore")

from collections import Counter

import ipywidgets as widgets
from ipywidgets import interact, interact_manual

plt.rcParams['figure.figsize'] = (6, 6)

from IPython.core.display import display, HTML
import random

#Define a colour map for gene expression
colors2 = plt.cm.Reds(np.linspace(0, 1, 128))
colors3 = plt.cm.Greys_r(np.linspace(0.7,0.8,20))
#colorsComb = np.vstack([colors3, colors2])
#mymap = colors.LinearSegmentedColormap.from_list('my_colormap', colorsComb)
from matplotlib import colors
colorsComb = np.vstack([plt.cm.Reds(np.linspace(0, 1, 128)), plt.cm.Greys_r(np.linspace(0.7, 0.8, 0))])
mymap = colors.LinearSegmentedColormap.from_list('my_colormap', colorsComb)

# Helper function to split list in chunks
def chunks(lista, n):
    for i in range(0, len(lista), n):
        yield lista[i:i + n]
        
        plt.rcParams['figure.figsize'] = (6, 5)
sc.set_figure_params(dpi=100, vector_friendly=True)
def mysize(w, h, d):
    fig, ax = plt.subplots(figsize = (w, h), dpi = d)
    return(fig.gca())
plt.rcParams['figure.figsize'] = (6, 5)
sc.set_figure_params(dpi=100, vector_friendly=True)

import scvi

## frequently used variables
from matplotlib import colors
import matplotlib.pyplot as plt
colorsComb = np.vstack([plt.cm.Reds(np.linspace(0, 1, 128)), plt.cm.Greys_r(np.linspace(0.7, 0.8, 0))])
mymap = colors.LinearSegmentedColormap.from_list('my_colormap', colorsComb)

## Along these Lines, a colourmap diverging from gray to red
gray_red = colors.LinearSegmentedColormap.from_list("grouping", ["lightgray", "red", "darkred"], N = 128)

## Some more Colour Maps
gray_violet = colors.LinearSegmentedColormap.from_list("grouping", ["lightgray", "mediumvioletred", "indigo"], N = 128)
gray_blue = colors.LinearSegmentedColormap.from_list("grouping", ["lightgray", "cornflowerblue", "darkblue"], N = 128)


def mysize(w, h, d):
    fig, ax = plt.subplots(figsize = (w, h), dpi = d)
    return(fig.gca())
#plt.rcParams['figure.figsize'] = (6, 5)
#sc.set_figure_params(dpi=120, vector_friendly=True)
import matplotlib.colors as colors
c_low = colors.colorConverter.to_rgba('orange', alpha = 0)
c_high = colors.colorConverter.to_rgba('red',alpha = 1)
cmap_transparent = colors.LinearSegmentedColormap.from_list('rb_cmap',[c_low, c_high], 512)
import matplotlib.colors as colors
c_low2 = colors.colorConverter.to_rgba('green', alpha = 0)
c_high2 = colors.colorConverter.to_rgba('darkblue',alpha = 1)
cmap_transparent2 = colors.LinearSegmentedColormap.from_list('rb_cmap',[c_low2, c_high2], 512)

import cell2location as c2l
from cell2location.utils import select_slide

##load
adata_vis = sc.read(f".h5ad")

Niche_NMF_palette = dict(zip(adata_vis.obs.Niche_NMF.cat.categories, adata_vis.uns["Niche_NMF_colors"]))
####ϸ°ûÀàÐÍ
cts = ['AT0', 'AT1', 'AT2', 'Aberrant basaloid', 'Adventitial fibroblast', 'Alveolar fibroblast',
       'Artery', 'B/Plasma', 'Basal', 'Basophil/Mast', 'Bronchial Vessel', 'Capillary', 'Capillary Aerocyte',
       'Ciliated', 'Ciliated SFTPB+/SCGB1A1+', 'Dendritic', 'Ionocyte', 'Lymphatic', 'Macrophage C1Q hi',
       'Macrophage CHI3L1+/CD9 hi/', 'Macrophage FABP4+', 'Macrophage IL1B+', 'Macrophage LYVE1+', 
       'Macrophage RETN+/VCAN+', 'Mesothelial', 'Monocyte', 'Mucous', 'Myofibroblast', 'NK', 
       'Peribronchial fibroblast', 'Pericyte', 'Smooth Muscle', 'T cell', 'TB-SC', 'Vein', 'pDC', 'preTB-SC/RAS']

IPF = adata_vis[adata_vis.obs["treatment"].isin(["IPF"])]
sq.gr.spatial_neighbors(IPF, coord_type="grid", spatial_key="spatial",library_key="sampleID",n_rings=1)
sq.gr.nhood_enrichment(IPF, cluster_key="Niche_NMF")

sq.pl.nhood_enrichment(IPF, cluster_key="Niche_NMF", method="ward", cmap="bwr",vmax=90, vmin=-90, figsize=(5,5),title="1-Ring", save="IPF_nhood_enrichment_zscore_1_ring.pdf")#mode="zscore")

niche_IPF_1_ring = pd.DataFrame(IPF.uns["Niche_NMF_nhood_enrichment"]["count"], index=IPF.obs['Niche_NMF'].cat.categories, columns = IPF.obs['Niche_NMF'].cat.categories)
niche_IPF_1_ring

sb.reset_defaults()
sb.heatmap(niche_IPF_1_ring, annot=False, cmap=mymap, )

niche_IPF_1_ring = niche_IPF_1_ring.div(niche_IPF_1_ring.sum(axis=1), axis=0)
niche_IPF_1_ring

sb.heatmap(niche_IPF_1_ring, annot=True, cmap=mymap, fmt=".2f" )
plt.savefig("./figures/PercentageOfInteractions_IPF_1_Ring.pdf")

sq.gr.spatial_neighbors(IPF, coord_type="grid", spatial_key="spatial",library_key="sampleID",n_rings=3)
sq.gr.nhood_enrichment(IPF, cluster_key="Niche_NMF")
sq.pl.nhood_enrichment(IPF, cluster_key="Niche_NMF", method="ward", cmap="bwr",vmax=90, vmin=-90,  figsize=(5,5),title="3-Ring", save="IPF_nhood_enrichment_zscore_3_ring.pdf")

niche_IPF_3_ring = pd.DataFrame(IPF.uns["Niche_NMF_nhood_enrichment"]["count"], index=IPF.obs['Niche_NMF'].cat.categories, columns = IPF.obs['Niche_NMF'].cat.categories)
niche_IPF_3_ring

sb.reset_defaults()
sb.heatmap(niche_IPF_3_ring, annot=False, cmap=mymap, )

niche_IPF_3_ring = niche_IPF_3_ring.div(niche_IPF_3_ring.sum(axis=1), axis=0)
niche_IPF_3_ring

sb.heatmap(niche_IPF_3_ring, annot=True, cmap=mymap, fmt=".2f" )
plt.savefig("./figures/PercentageOfInteractions_IPF_3_Ring.pdf")