In [1]:
import scanpy as sc
import pandas as pd
import numpy as np
import anndata
import re
import scipy.sparse as scs
import concurrent.futures
import scanpy.external as sce
from concurrent.futures import ProcessPoolExecutor
import copy
import os
import glob
import matplotlib.pyplot as plt
import seaborn as sns
from adjustText import adjust_text
import warnings
# Suppress all warnings
warnings.filterwarnings('ignore')
import math
In [2]:
def gen_mpl_labels(
adata, groupby, exclude=(), ax=None, adjust_kwargs=None, text_kwargs=None
):
if adjust_kwargs is None:
adjust_kwargs = {"text_from_points": False}
if text_kwargs is None:
text_kwargs = {}
medians = {}
for g, g_idx in adata.obs.groupby(groupby).groups.items():
if g in exclude:
continue
medians[g] = np.median(adata[g_idx].obsm["X_umap"], axis=0)
if ax is None:
texts = [
plt.text(x=x, y=y, s=k, **text_kwargs) for k, (x, y) in medians.items()]
else:
texts = [ax.text(x=x, y=y, s=k, **text_kwargs) for k, (x, y) in medians.items()]
adjust_text(texts, **adjust_kwargs)
In [3]:
adata=sc.read_h5ad('/home/jupyter/reference_generating_new/NK/NKcells_processed_2023-11-22.h5ad')
In [4]:
np.shape(adata.obs)
Out[4]:
(159245, 57)
In [5]:
# absolute path to search all text files inside a specific folder
path = r'*.parquet'
files = glob.glob(path)
In [6]:
df_list=[pd.read_parquet(x) for x in files]
In [7]:
df_all=pd.concat(df_list)
In [8]:
adata.obs=adata.obs.merge(df_all[['barcodes','AIFI_L1','AIFI_L2','AIFI_L3']], on="barcodes", how='left')
In [9]:
Contamination=['NK+Erythocytes Doublet',"NK+Platelet Doublet","NK+T Doublets"]
adata=adata[~adata.obs['AIFI_L2'].isin(Contamination)]
In [10]:
print(adata.obs['AIFI_L1'].nunique())
print(adata.obs['AIFI_L2'].nunique())
print(adata.obs['AIFI_L3'].nunique())
2 4 8
In [11]:
print(adata.obs['AIFI_L1'].value_counts())
print(adata.obs['AIFI_L2'].value_counts())
print(adata.obs['AIFI_L3'].value_counts())
AIFI_L1 NK cells 144063 ILC 882 Name: count, dtype: int64 AIFI_L2 NK CD56dim 130850 NK CD56bright 12209 NK Proliferating 1004 ILC 882 Name: count, dtype: int64 AIFI_L3 NK CD56dim 103311 NK CD56bright 12209 NK intermediate 10478 NK adaptive-like 9946 NK CD56dimISG+ 3834 NK CD56dimMito+ 3281 NK Proliferating 1004 ILC 882 Name: count, dtype: int64
In [12]:
with plt.rc_context({"figure.figsize": (5, 5), "figure.dpi": 500, "figure.frameon": False}):
ax=sc.pl.umap(adata, color=['AIFI_L1'], size=2,show=False,legend_loc=None,ncols=1 ,frameon=False)
gen_mpl_labels(
adata,
"AIFI_L2",
exclude=(""),
ax=ax,
adjust_kwargs=dict(arrowprops=dict(arrowstyle='-', color='black')),
text_kwargs=dict(fontsize=10),)
fig = ax.get_figure()
fig.tight_layout()
In [13]:
with plt.rc_context({"figure.figsize": (5, 5), "figure.dpi": 500, "figure.frameon": False}):
ax=sc.pl.umap(adata, color=['AIFI_L2'], size=2,show=False,legend_loc=None,ncols=1 ,frameon=False)
gen_mpl_labels(
adata,
"AIFI_L2",
exclude=(""),
ax=ax,
adjust_kwargs=dict(arrowprops=dict(arrowstyle='-', color='black')),
text_kwargs=dict(fontsize=10),)
fig = ax.get_figure()
fig.tight_layout()
In [14]:
with plt.rc_context({"figure.figsize": (5, 5), "figure.dpi": 500, "figure.frameon": False}):
ax=sc.pl.umap(adata, color=['AIFI_L3'], size=2,show=False,legend_loc=None,ncols=1 ,frameon=False)
gen_mpl_labels(
adata,
"AIFI_L3",
exclude=(""),
ax=ax,
adjust_kwargs=dict(arrowprops=dict(arrowstyle='-', color='black')),
text_kwargs=dict(fontsize=10),)
fig = ax.get_figure()
fig.tight_layout()
#plt.savefig('UMAP.png')
In [ ]:
In [15]:
NK_gene_of_interests="CD3E, CD4, CD8A, TRAC, TRDC, LEF1, TCF7, CCR7, SELL, ITGB1, FAS, CD27, CD28, FOXP3, RTKN2, IKZF2, ZNF683, NKG7, KLRC2, KLRF1, KLRB1, KLRD1, GZMA, GZMK, GZMB, PRF1, GNLY, IFI44L, IRF7, MKI67, TRDV1, TRDV2, TRDV3, MME, SOX4, NCAM1, FCGR3A, CD3D, KLRC2"
NK_gene_of_interests = NK_gene_of_interests.split(', ')
In [16]:
adata=adata.raw.to_adata()
adata.raw=adata
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
WARNING: adata.X seems to be already log-transformed.
In [17]:
dp = sc.pl.dotplot(adata, NK_gene_of_interests, 'AIFI_L2', use_raw=False, dendrogram=True,figsize=(15,2),return_fig=True)
dp.add_totals().show()
WARNING: dendrogram data not found (using key=dendrogram_AIFI_L2). Running `sc.tl.dendrogram` with default parameters. For fine tuning it is recommended to run `sc.tl.dendrogram` independently.
In [18]:
dp = sc.pl.dotplot(adata, NK_gene_of_interests, 'AIFI_L3', use_raw=False, dendrogram=True,figsize=(15,4),return_fig=True)
dp.add_totals().show()
WARNING: dendrogram data not found (using key=dendrogram_AIFI_L3). Running `sc.tl.dendrogram` with default parameters. For fine tuning it is recommended to run `sc.tl.dendrogram` independently.
In [19]:
adata.write_h5ad('NK_Labeled.h5ad')
In [ ]:
In [ ]: