作者，Evil Genius
昨天，2024年5月19日，发生了两件大事。
一是在昨天lol在成都举办的2024 MSI决赛中，Gen以3:1获得了冠军，而就在前不久，Gen俱乐部公开表示台湾是一个国家，遭到了国内粉丝的抵制。至于比赛内容，就想问一句，为什么执着于偷家？
二是昨天相亲回来路过迎泽大桥，亲眼目睹了一个姑娘一跃而下，跳进了汾河，等到救护车赶到的时候已经过了很长时间，估计又一个鲜活的生命里离开了人世，而这个跳桥事件，已经是我听到太原今年发生的第四起跳桥事件，之前在长风大桥，晋阳湖等均有发生。
与之鲜明对比的是，太原刚被评为全国最具幸福感的城市。
最后，来分享一下邻域富集
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)
sc.settings.figdir = "./figures/"
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
加载数据
##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"]))
sc.pl.umap(adata_vis, color="Niche_NMF", palette=Niche_NMF_palette,)
import hotspot
def compute_scores_hotspot(adata,
genes,
layer=None,
n_neighbors = 30,
neighborhood_factor = 3,
gene_symbols = None,
use_rep = "X_scVI",
model = 'danb'
):
if use_rep is None:
if layer is None:
index = pynndescent.NNDescent(adata.X, n_neighbors=n_neighbors+1)
else:
index = pynndescent.NNDescent(adata.layers[layer], n_neighbors=n_neighbors+1)
else:
index = pynndescent.NNDescent(adata.obsm[use_rep], n_neighbors=n_neighbors+1)
ind, dist = index.neighbor_graph
ind, dist = ind[:, 1:], dist[:, 1:]
ind = pd.DataFrame(ind, index=list(range(adata.n_obs)))
neighbors = ind
weights = compute_weights(dist, neighborhood_factor=neighborhood_factor)
weights = pd.DataFrame(weights, index=neighbors.index,
columns=neighbors.columns)
if layer is None:
if scipy.sparse.issparse(adata.X):
umi_counts = adata.X.sum(axis=1).A1
else:
umi_counts = adata.X.sum(axis=1)
else:
if scipy.sparse.issparse(adata.layers[layer]):
umi_counts = adata.layers[layer].sum(axis=1).A1
else:
umi_counts = adata.layers[layer].sum(axis=1)
if gene_symbols is None:
counts_dense = Hotspot._counts_from_anndata(
adata[:, adata.var_names.isin(genes)], layer, dense=True)
else:
counts_dense = Hotspot._counts_from_anndata(
adata[:, adata.var[gene_symbols].isin(genes)], layer, dense=True)
scores = modules.compute_scores(
counts_dense,
model,
umi_counts,
neighbors.values,
weights.values,
)
return scores
import pynndescent
import scipy.sparse
import hotspot
from hotspot import modules
from hotspot.knn import compute_weights
from hotspot.hotspot import Hotspot
senescence =["GLB1","TP53","SERPINE1","CDKN1A","CDKN1B","CDKN2B"]
scores = compute_scores_hotspot(adata_vis, senescence,layer="log1p",n_neighbors = 30,neighborhood_factor = 3,gene_symbols = None,use_rep = "X_scVI",model = 'danb')
adata_vis.obs["senescence_hs_score"] = scores
sc.pl.umap(adata_vis, color = "senescence_hs_score", cmap = "bwr", ax = mysize(6, 5, 120), size = 30, vcenter=0,)# save="homeo_hs_score.pdf")
#for i in fibs:
gene = "senescence_hs_score"
vmins = None
vcenters= None
vmaxs = 2
img_keys='lowres'
cmaps= "bwr"
palettes=Niche_NMF_palette
group= None #"Fibrotic"
import matplotlib.pyplot as plt
from cell2location.utils import select_slide
fig, (ax1, ax2, ax3, ax4, ax5,ax6) = plt.subplots(1, 6, figsize=(26,4), )
plt.suptitle(" control ", y=1.05)
with plt.rc_context({'axes.facecolor': 'white','figure.figsize': [4, 4]}):
slide = select_slide(adata_vis, "90_C1_RO-730_Healthy_processed_CM")
sc.pl.spatial(slide, cmap=cmaps,color=gene,size=1.3,img_key=img_keys,vmin=vmins, use_raw=False,layer="log1p",groups=group, vmax=vmaxs, ax=ax1, show=False,vcenter= vcenters,palette=palettes, legend_loc=None)
slide = select_slide(adata_vis, "91_A1_RO-727_Healthy_processed_CM")
sc.pl.spatial(slide, cmap=cmaps,color=gene,size=1.3,img_key=img_keys,vmin=vmins, use_raw=False,layer="log1p",groups=group, vmax=vmaxs, ax=ax2, show=False,vcenter= vcenters,palette=palettes, legend_loc=None)
slide = select_slide(adata_vis, "91_B1_RO-728_Healthy_processed_CM")
sc.pl.spatial(slide, cmap=cmaps,color=gene,size=1.3,img_key=img_keys,vmin=vmins, use_raw=False,layer="log1p",groups=group, vmax=vmaxs, ax=ax3, show=False,vcenter= vcenters,palette=palettes, legend_loc=None)
slide = select_slide(adata_vis, "1217_0002_processed_aligned")
sc.pl.spatial(slide, cmap=cmaps,color=gene,size=1.3,img_key=img_keys,vmin=vmins, use_raw=False,layer="log1p",groups=group, vmax=vmaxs, ax=ax4, show=False,vcenter= vcenters,palette=palettes, legend_loc=None)
slide = select_slide(adata_vis, "92_A1_RO-3203_Healthy_processed_CM")
sc.pl.spatial(slide, cmap=cmaps,color=gene,size=1.3,img_key=img_keys,vmin=vmins, use_raw=False,layer="log1p",groups=group, vmax=vmaxs, ax=ax5, show=False,vcenter= vcenters,palette=palettes, legend_loc=None)
slide = select_slide(adata_vis, "1217_0004_processed_aligned")
sc.pl.spatial(slide, cmap=cmaps,color=gene,size=1.3,img_key=img_keys,vmin=vmins, use_raw=False,layer="log1p",groups=group, vmax=vmaxs, ax=ax6, show=False,vcenter= vcenters,palette=palettes, legend_loc=None)
plt.savefig("./figures/spatial_plot_senescence_hs_score_healthy_vmax2.pdf")
fig, (ax7, ax8, ax9, ax10, ax11,) = plt.subplots(1, 5, figsize=(22,4), )
plt.suptitle(" IPF ", y=1.05)
with plt.rc_context({'axes.facecolor': 'white','figure.figsize': [4, 4]}):
slide = select_slide(adata_vis, "90_A1_H237762_IPF_processed_CM")
sc.pl.spatial(slide, cmap=cmaps,color=gene,size=1.3,img_key=img_keys,vmin=vmins, use_raw=False,layer="log1p",groups=group, vmax=vmaxs, ax=ax7, show=False,vcenter= vcenters,palette=palettes, legend_loc=None)
slide = select_slide(adata_vis, "1217_0001_processed_aligned")
sc.pl.spatial(slide, cmap=cmaps,color=gene,size=1.3,img_key=img_keys,vmin=vmins, use_raw=False,layer="log1p",groups=group, vmax=vmaxs, ax=ax8, show=False,vcenter= vcenters,palette=palettes, legend_loc=None)
slide = select_slide(adata_vis, "91_D1_24513-17_IPF_processed_CM")
sc.pl.spatial(slide, cmap=cmaps,color=gene,size=1.3,img_key=img_keys,vmin=vmins, use_raw=False,layer="log1p",groups=group, vmax=vmaxs, ax=ax9, show=False,vcenter= vcenters,palette=palettes, legend_loc=None)
slide = select_slide(adata_vis, "1217_0003_processed_aligned")
sc.pl.spatial(slide, cmap=cmaps,color=gene,size=1.3,img_key=img_keys,vmin=vmins, use_raw=False,layer="log1p",groups=group, vmax=vmaxs, ax=ax10, show=False,vcenter= vcenters,palette=palettes, legend_loc=None)
slide = select_slide(adata_vis, "92_D1_RO-3736_IPF_processed_CM")
sc.pl.spatial(slide, cmap=cmaps,color=gene,size=1.3,img_key=img_keys,vmin=vmins, use_raw=False,layer="log1p",groups=group, vmax=vmaxs, ax=ax11, show=False,vcenter= vcenters,palette=palettes)
plt.savefig("./figures/spatial_plot_senescence_hs_score_IPF_vmax2.pdf")
生活很好，有你更好
网友评论