Add a couple more markers, but need to examine how good they are

scripts/14_Plot_Add_Ups.py

@@ -0,0 +1,67 @@
+#!/usr/bin/env python3
+# coding: utf-8
+
+# In this notebook, we're going plot the mouse retinal data and determine proper marker genes.
+
+import datetime
+print(f'{datetime.datetime.now()} Analysis Setup')
+
+import sklearn as sk
+import anndata as ad
+import scanpy as sc
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import os
+
+os.chdir('/project/ycheng11lab/jfmaurer/mouse_retina_atlas_chen_2024/')
+os.makedirs('14_Add_Ups', exist_ok = True)
+sc.settings.n_jobs = -1
+
+adata = ad.read_h5ad('10_Make_Shiny/10_Shiny_Input.h5ad')
+
+
+all_markers = [
+'Tmcc3',
+'Anks1b',
+'Asic2',
+'Vsx2',
+'Rho',
+'Pde6a',
+'Nr2e3',
+'Cnga1',
+'Guca1b',
+'Rp1',
+'Rcvrn']
+
+sc.plotting.DotPlot.DEFAULT_SAVE_PREFIX = ""
+sc.plotting.DotPlot.DEFAULT_LARGEST_DOT = 200.0
+plot_prefix = "/project/ycheng11lab/jfmaurer/mouse_retina_atlas_chen_2024/14_Add_Ups/AddUp_"
+
+raw = True
+data_string = "normCounts"
+max_col = 3
+
+for raw in [False, True]:
+
+  if raw:
+    data_string = "rawCounts"
+    max_col = 12
+    adata.X = adata.raw.X
+
+  sc.pl.dotplot(adata[:, all_markers],
+              var_names = all_markers, gene_symbols = "feature_name",
+              groupby = "minorclass",
+              vmax = max_col,
+              vmin = 0,
+              show = False,
+              save = f"{plot_prefix}Minor-Cell_All-Marker_{data_string}.pdf")
+
+  sc.pl.dotplot(adata[:, all_markers],
+              var_names = all_markers, gene_symbols = "feature_name",
+              groupby = "majorclass",
+              vmax = max_col,
+              vmin = 0,
+              show = False,
+              save = f"{plot_prefix}Major-Cell_All-Marker_{data_string}.pdf")