python/salus/cmplatform/mbrain.py

   1 #!/usr/bin/env python3
   2
   3 import importlib.metadata
   4 from packaging import version
   5 pkgname = "squidpy"
   6 min_ver = "1.2.3"
   7 got_ver = importlib.metadata.version(pkgname)
   8 if version.parse(got_ver) < version.parse(min_ver):
   9     raise importlib.VersionConflict(f"{pkgname}>={min_ver} is needed, but found {pkgname}=={got_ver}")
  10
  11 import matplotlib; matplotlib.use("module://mplcairo.base")
  12 from matplotlib import pyplot as plt
  13 import mplcairo
  14
  15 import numpy as np
  16 import pandas as pd
  17
  18 import anndata as ad
  19 import scanpy as sc
  20 import squidpy as sq
  21 import seaborn as sns
  22
  23 mbi=sq.read.visium('/share/result/spatial/data/BoAo_sp/illumina/mbrain/outs/')
  24 mbi.var_names_make_unique()
  25 #mbic=sc.read_visium('/share/result/spatial/data/BoAo_sp/illumina/mbrain/outs/')
  26 mbs=sq.read.visium('/share/result/spatial/data/BoAo_sp/salus/mbrain/outs/')
  27 mbs.var_names_make_unique()
  28
  29 mbi.var["mt"] = mbi.var_names.str.startswith("mt-")  # mouse: mt-Nd1 mt-Nd2 mt-Co1 mt-Co2 mt-Atp8 mt-Atp6 mt-Co3 mt-Nd3 mt-Nd4l mt-Nd4 mt-Nd5 mt-Nd6 mt-Cytb
  30 sc.pp.calculate_qc_metrics(mbi, qc_vars=["mt"], inplace=True)
  31 mbs.var["mt"] = mbs.var_names.str.startswith("mt-")
  32 sc.pp.calculate_qc_metrics(mbs, qc_vars=["mt"], inplace=True)
  33
  34 with pd.option_context("mode.copy_on_write", True):
  35     obsmbi = mbi.obs[['n_genes_by_counts', 'total_counts']].copy(deep=False)
  36     obsmbs = mbs.obs[['n_genes_by_counts', 'total_counts']].copy(deep=False)
  37     p1df = pd.concat([obsmbi.assign(Platform='Illumina'), obsmbs.assign(Platform='Salus')], ignore_index=True).replace([np.inf, -np.inf, 0], np.nan).dropna()
  38     p2df = obsmbi.join(obsmbs,lsuffix='_Illumina',rsuffix='_Salus',how='inner').replace([np.inf, -np.inf, 0], np.nan).dropna()
  39
  40 custom_params = {"axes.spines.right": False, "axes.spines.top": False}
  41 sns.set_theme(style="ticks", rc=custom_params, font="STIX Two Text")
  42 figA=sns.JointGrid(data=p1df, x="total_counts", y="n_genes_by_counts", hue='Platform', dropna=True)
  43 #figA.plot(sns.scatterplot, sns.histplot, alpha=.7, edgecolor=".2", linewidth=.5)
  44 figA.plot_joint(sns.scatterplot, s=12.7, alpha=.6)
  45 figA.plot_marginals(sns.histplot, kde=True, alpha=.618)
  46 figA.figure.suptitle('Gene to UMI plot - Mouse Brain')
  47 figA.set_axis_labels(xlabel='UMIs per Barcode', ylabel='Genes per Barcode')
  48 figA.savefig('Dmbrain.pdf', transparent=True, dpi=300, metadata={'Title': 'Gene to UMI plot', 'Subject': 'Mouse Brain Data', 'Author': 'HU Xuesong'})
  49
  50 figB=sns.JointGrid(data=p2df, x="total_counts_Illumina", y="total_counts_Salus", dropna=True)
  51 figB.plot_joint(sns.scatterplot, s=12.7, alpha=.6)
  52 figB.plot_marginals(sns.histplot, kde=True, alpha=.618)
  53 figB.figure.suptitle('UMI per Barcode Counts Comparing - Mouse Brain')
  54 figB.set_axis_labels(xlabel='UMI Counts from Illumina', ylabel='UMI Counts from Salus')
  55 figB.savefig('Embrain.pdf', transparent=True, dpi=300, metadata={'Title': 'UMI per Barcode Counts Comparing', 'Subject': 'Mouse Brain Data', 'Author': 'HU Xuesong'})
  56
  57
  58 '''
  59 x1 = np.random.randn(1000)
  60 y1 = np.random.randn(1000)
  61 x2 = np.random.randn(1000) * 5
  62 y2 = np.random.randn(1000)
  63 fig, ax = plt.subplots()
  64 # The figure and axes background must be made transparent.
  65 fig.patch.set(alpha=0)
  66 ax.patch.set(alpha=0)
  67 pc1 = ax.scatter(x1, y1, c='b', edgecolors='none')
  68 pc2 = ax.scatter(x2, y2, c='r', edgecolors='none')
  69 mplcairo.operator_t.ADD.patch_artist(pc2)  # Use additive blending.
  70 plt.show()
  71 '''