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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,61 @@ | ||
| #!/usr/bin/env python3 | ||
| # coding: utf-8 | ||
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| # In this notebook, we're going plot the mouse retinal data and determine proper marker genes. | ||
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| import datetime | ||
| print(f'{datetime.datetime.now()} Analysis Setup') | ||
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| 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 | ||
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| os.chdir('/project/ycheng11lab/jfmaurer/mouse_retina_atlas_chen_2024') | ||
| os.makedirs('12_photoreceptor_expression', exist_ok = True) | ||
| sc.settings.n_jobs = -1 | ||
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| adata = ad.read_h5ad('10_Make_Shiny/10_Shiny_Input.h5ad') | ||
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| rod_gene = ['Rho','Pde6a', 'Gngt1', 'Optn','Nrl','Nr2e3', 'Reep6', 'Cnga1', 'Guca1b', 'Pde6a', 'Rp1', | ||
| 'Cngb1','Rcvrn', 'Pdc','Syne2','Mef2c','Fyco1','Atf4'] | ||
| cone_gene = ['Opn1mw','Opn1sw', 'Ccdc136', 'Optn','Nrl','Gngt2', 'Gnat2'] | ||
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| sc.plotting.DotPlot.DEFAULT_SAVE_PREFIX = "" | ||
| sc.plotting.DotPlot.DEFAULT_LARGEST_DOT = 200.0 | ||
| plot_prefix = "/project/ycheng11lab/jfmaurer/mouse_retina_atlas_chen_2024/12_photoreceptor_expression/" | ||
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| raw = True | ||
| data_string = "normCounts" | ||
| max_col = 3 | ||
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| for raw in [False, True]: | ||
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| if raw: | ||
| data_string = "rawCounts" | ||
| max_col = 12 | ||
| adata.X = adata.raw.X | ||
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| # rod_gene = set(rod_gene) | ||
| # cone_gene = set(cone_gene) | ||
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| sc.pl.dotplot(adata[:, rod_gene], | ||
| var_names = rod_gene, gene_symbols = "feature_name", | ||
| groupby = "majorclass", | ||
| vmax = max_col, | ||
| vmin = 0, | ||
| show = False, | ||
| save = f"{plot_prefix}Major-Cell_Rod_{data_string}.pdf") | ||
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| sc.pl.dotplot(adata[:, cone_gene], | ||
| var_names = cone_gene, gene_symbols = "feature_name", | ||
| groupby = "majorclass", | ||
| vmax = max_col, | ||
| vmin = 0, | ||
| show = False, | ||
| save = f"{plot_prefix}Major-Cell_Cone_{data_string}.pdf") |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,66 @@ | ||
| #!/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('13_Plot_Ambiguous', exist_ok = True) | ||
| sc.settings.n_jobs = -1 | ||
|
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| adata = ad.read_h5ad('10_Make_Shiny/10_Shiny_Input.h5ad') | ||
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| resultsPath = "09_Designer_Analysis/PanelDesignV1Ambiguous.txt" | ||
| markers = pd.read_csv(resultsPath, sep = '\t') # NOTE: Nrg1 & 2010007h06rik adjusted | ||
|
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| sc.plotting.DotPlot.DEFAULT_SAVE_PREFIX = "" | ||
| sc.plotting.DotPlot.DEFAULT_LARGEST_DOT = 200.0 | ||
| plot_prefix = "/project/ycheng11lab/jfmaurer/mouse_retina_atlas_chen_2024/13_Plot_Ambiguous/" | ||
|
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| raw = True | ||
| data_string = "normCounts" | ||
| max_col = 3 | ||
|
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| for raw in [False, True]: | ||
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| if raw: | ||
| data_string = "rawCounts" | ||
| max_col = 12 | ||
| adata.X = adata.raw.X | ||
|
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| all_markers = markers["Marker"].unique().tolist() | ||
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| 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") | ||
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| 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") | ||
|
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| sc.pl.dotplot(adata[adata.obs["majorclass"].isin(["RGC"]), 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}_RGC.pdf") |