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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/"
## 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)

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

####ϸ°ûÀàÐÍ£¬¸ù¾Ý×Ô¼ºµÄÏîÄ¿Ñ¡Ôñ
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']

## All Markers
sc.tl.rank_genes_groups(adata_vis, groupby = 'Niche_NMF', groups = "all", use_raw = False, method = "wilcoxon", pts= True,)
sc.pl.rank_genes_groups(adata_vis)

## Combine into one Data Frame (comparable to Marker Table)
result = adata_vis.uns['rank_genes_groups']
allMarkers = []
for cluster in result['names'].dtype.names:
    current = pd.DataFrame({"gene": result["names"][cluster], "score": result["scores"][cluster],
                            "logfoldchange": result["logfoldchanges"][cluster], "pval": result["pvals"][cluster],
                            "pval_adj": result["pvals_adj"][cluster],
                            "pct_within":result["pts"].loc[result["names"][cluster]][cluster],
                            "pct_outside":result["pts_rest"].loc[result["names"][cluster]][cluster],  "cluster": cluster})
    allMarkers.append(current)
allMarkers = pd.concat(allMarkers)
allMarkers.head()

## Write to file 
#folder =
allMarkers.to_csv("AllMarkers_human_Niche_NMF_CM.txt", sep = "\t", index = False)

allMarkers.drop("pval", axis=1, inplace=True)
for i in adata_vis.obs.Niche_NMF.cat.categories:
    allMarkers_subset =allMarkers[allMarkers["cluster"]== i ]
    allMarkers_subset.to_csv("AllMarkers_human_Niche_NMF_%s.txt" %i, sep = "\t", index = False)

marker3 = allMarkers
marker3 = marker3[marker3['pct_outside'] < 0.25]
marker3 = marker3[marker3['pct_within'] > 0.25]
marker3 = marker3[marker3['logfoldchange'] > 1]
marker3

#Code zum filtern hast ja schon, die folgenden Zeilen w?hlen dann die top n gene per cluster aus (im gleichen ordering wie das dendrogram)
marker3.sort_values(by = ["cluster", "logfoldchange"], ascending = [True, False], inplace = True)

#Get ordering as in Dendrogram
sc.tl.dendrogram(adata_vis, groupby = "Niche_NMF")
order = adata_vis.uns["dendrogram_Niche_NMF"]["dendrogram_info"]["ivl"]

#Get top genes from Markers Table
n_genes = 10
genes = []
for typ in order:
    curgenes = marker3.loc[marker3.cluster == typ, "gene"].values[0:n_genes]
    genes = genes + [g for g in curgenes if g not in genes]

sc.pl.matrixplot(adata_vis, var_names = genes, standard_scale = "var", groupby = "Niche_NMF", dendrogram = True,figsize=(20,3),layer="log1p",
                save = "Allmarkers_matrixplottop%s_per_Niche_NMF_CM_025background_025celltype10logfc.pdf" %n_genes)

##gene set enrichment on DGe genes between Niche_NMF_CM
allMarkers =allMarkers[allMarkers["pval_adj"]<0.05]
import gseapy

#Available databases : ¡®Human¡¯, ¡®Mouse¡¯, ¡®Yeast¡¯, ¡®Fly¡¯, ¡®Fish¡¯, ¡®Worm¡¯ 
gene_set_names = gseapy.get_library_name(organism='Human')

allMarkers_subset =allMarkers[allMarkers["cluster"]== "Alveolar_AT1"]
allMarkers_subset =allMarkers_subset[allMarkers_subset["pval_adj"] < 0.05  ]
allMarkers_subset =allMarkers_subset[allMarkers_subset["logfoldchange"] > 0.5  ]

#?gseapy.enrichr
for i in adata_vis.obs.Niche_NMF.cat.categories:
    genelist = sc.get.rank_genes_groups_df(adata_vis, group=i, 
                                     log2fc_min=0.5, 
                                    pval_cutoff=0.05)['names'].squeeze().str.strip().tolist()
    enr_res = gseapy.enrichr(gene_list=genelist,
                     organism='Human',
                     background=adata_vis.var_names,
                     gene_sets='GO_Biological_Process_2021',
                     cutoff = 0.05,)
    enr_res.results["Niche"] = "%s" %i
    enr_res.results= enr_res.results[enr_res.results["Adjusted P-value"] <0.05]
    #enr_res.results=  enr_res.results.nsmallest(n=6, columns=['Adjusted P-value'],keep="all")
    enr_res.results.to_csv("./GO_enrichment/GO_enrichment_%s.txt" %i, sep = "\t", index = False)

gsea_res = pd.concat([pd.read_csv("./GO_enrichment/GO_enrichment_%s.txt" %i,sep = "\t")for i in adata_vis.obs.Niche_NMF.cat.categories], axis = 0)

gsea_res

gsea_table_new = gsea_res[gsea_res["Term"].isin(gsea_table["Term"])] 
gsea_table_new

gsea_table_new = gsea_table_new.drop(["Gene_set","Overlap","P-value","Old P-value","Old Adjusted P-value","Odds Ratio","Genes","Combined Score"], axis=1)
gsea_table_new

gsea_table_new = gsea_table_new[gsea_table_new["Niche"] != "no tissue"] 
gsea_table_new

gsea_table_new["Adjusted P-value"] = np.log10(gsea_table_new["Adjusted P-value"])
gsea_table_new

gsea_table_new["Adjusted P-value"] = gsea_table_new["Adjusted P-value"]*(-1)
gsea_table_new

gsea_table_new['Adjusted P-value'].values[gsea_table_new['Adjusted P-value'] > 50] = 50

gsea_table_new.sort_values(by=["Adjusted P-value"])
gsea_table_new

gsea_table_new_cm= pd.pivot(gsea_table_new,index="Term", columns="Niche", values="Adjusted P-value")
gsea_table_new_cm = gsea_table_new_cm.fillna(0)

gsea_table_new_cm

%%R -w 550 -h 700 -i gsea_table_new_cm
my_col <- colorRampPalette(c("white","orange", "red","darkred"))(50)
my_col[0] <- "white"
p<-pheatmap(gsea_table_new_cm, color= my_col)