Metric2

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import omicverse as ov
#import scvelo as scv
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt

ov.ov_plot_set()
import omicverse as ov
#import scvelo as scv
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt

ov.ov_plot_set()

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import omicverse as ov
ov.utils.ov_plot_set()
from matplotlib import rcParams

# 设置全局字体为Arial
rcParams['font.family'] = 'Arial'
import omicverse as ov
ov.utils.ov_plot_set()
from matplotlib import rcParams

# 设置全局字体为Arial
rcParams['font.family'] = 'Arial'

cell size¶

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import os
metric_li=[i for i in os.listdir('result') if 'cell.pkl' in i]
metric_li
import os
metric_li=[i for i in os.listdir('result') if 'cell.pkl' in i]
metric_li

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['metric_hpc_cell.pkl', 'metric_dg_cell.pkl']

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import pickle
metric_dict={}
for i in metric_li:
    with open(f'result/{i}','rb') as f:
        metric_dict[i.split('.')[0]]=pickle.load(f)
import pickle
metric_dict={}
for i in metric_li:
    with open(f'result/{i}','rb') as f:
        metric_dict[i.split('.')[0]]=pickle.load(f)

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plot_data=pd.DataFrame(columns=['dataset','model','Cor_mean','non_Cor_mean',
                               'Cos_mean','non_Cos_mean','Trans_raw','Trans_after',
                               'Var_raw','Var_after','noisy','Inter_cells'])
for i in metric_dict.keys():
    for j in metric_dict[i].keys():
        test_li=[i.split('_')[-2],j]+list(metric_dict[i][j].values())
        plot_data.loc[str(i)+'-'+str(j)]=test_li
plot_data=pd.DataFrame(columns=['dataset','model','Cor_mean','non_Cor_mean',
                               'Cos_mean','non_Cos_mean','Trans_raw','Trans_after',
                               'Var_raw','Var_after','noisy','Inter_cells'])
for i in metric_dict.keys():
    for j in metric_dict[i].keys():
        test_li=[i.split('_')[-2],j]+list(metric_dict[i][j].values())
        plot_data.loc[str(i)+'-'+str(j)]=test_li

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plot_data['Trans_dif']=plot_data['Trans_after'].values-plot_data['Trans_raw'].values
plot_data['Var_dif']=plot_data['Var_after'].values-plot_data['Var_raw'].values
plot_data['Trans_dif']=plot_data['Trans_after'].values-plot_data['Trans_raw'].values
plot_data['Var_dif']=plot_data['Var_after'].values-plot_data['Var_raw'].values

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plot_data['Cor_unique']=plot_data['Cor_mean'].values-plot_data['non_Cor_mean'].values
plot_data['Cos_unique']=plot_data['Cos_mean'].values-plot_data['non_Cos_mean'].values
plot_data['Cor_unique']=plot_data['Cor_mean'].values-plot_data['non_Cor_mean'].values
plot_data['Cos_unique']=plot_data['Cos_mean'].values-plot_data['non_Cos_mean'].values

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plot_data['dataset_full']=plot_data['dataset'].map(
    {'dg':'Dentategyrus','hpc':'Hematopoietic'}
)
plot_data['dataset_full']=plot_data['dataset'].map(
    {'dg':'Dentategyrus','hpc':'Hematopoietic'}
)

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plot_data
plot_data

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	dataset	model	Cor_mean	non_Cor_mean	Cos_mean	non_Cos_mean	Trans_after	Var_raw	Var_after	noisy	Inter_cells	Trans_dif	Var_dif	Cor_unique	Cos_unique	dataset_full
metric_hpc_cell-1000	hpc	1000	0.953148	0.624671	0.362940	0.038173	0.014868	0.000353	0.062044	18	784	0.014868	0.061691	0.328477	0.324767	Hematopoietic
metric_hpc_cell-2000	hpc	2000	0.954833	0.578063	0.425209	0.042525	0.015930	0.000353	0.063351	26	784	0.015930	0.062998	0.376769	0.382685	Hematopoietic
metric_hpc_cell-5000	hpc	5000	0.983790	0.554874	0.525947	0.046353	0.019735	0.000353	0.034537	41	641	0.019735	0.034184	0.428916	0.479594	Hematopoietic
metric_hpc_cell-10000	hpc	10000	0.993506	0.475332	0.587532	0.046398	0.000000	0.000353	0.013302	62	550	0.000000	0.012949	0.518174	0.541134	Hematopoietic
metric_hpc_cell-20000	hpc	20000	0.996557	0.471008	0.617970	0.046891	0.016031	0.000353	0.086319	39	712	0.016031	0.085966	0.525549	0.571080	Hematopoietic
metric_dg_cell-1000	dg	1000	0.882065	0.320870	0.293635	0.030905	0.000000	0.000524	0.001707	27	91	0.000000	0.001183	0.561195	0.262730	Dentategyrus
metric_dg_cell-2000	dg	2000	0.955197	0.331714	0.443954	0.034564	0.019383	0.000524	0.101010	34	94	0.019383	0.100486	0.623483	0.409390	Dentategyrus
metric_dg_cell-5000	dg	5000	0.993609	0.287323	0.548843	0.039390	0.023046	0.000524	0.000136	19	106	0.023046	-0.000388	0.706286	0.509453	Dentategyrus

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import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Cor_unique", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.red_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Pearsonr",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3.5, 3)
g.set_xticklabels(fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
plt.title('The size of scRNA-seq\nExpression Correlation\n(Unique)',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_cor_sc-size.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_cor_sc-size.pdf',dpi=300,bbox_inches='tight')
import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Cor_unique", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.red_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Pearsonr",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3.5, 3)
g.set_xticklabels(fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
plt.title('The size of scRNA-seq\nExpression Correlation\n(Unique)',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_cor_sc-size.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_cor_sc-size.pdf',dpi=300,bbox_inches='tight')

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import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Cos_unique", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.red_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Cosine Similarity",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3.5, 3)
g.set_xticklabels(fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
plt.title('The size of scRNA-seq\nMarker Similarity\n(Unique)',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_cos_sc-size.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_cos_sc-size.pdf',dpi=300,bbox_inches='tight')
import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Cos_unique", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.red_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Cosine Similarity",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3.5, 3)
g.set_xticklabels(fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
plt.title('The size of scRNA-seq\nMarker Similarity\n(Unique)',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_cos_sc-size.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_cos_sc-size.pdf',dpi=300,bbox_inches='tight')

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import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="noisy", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.red_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Noisy Clusters Size",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3.5, 3)
g.set_xticklabels(fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
plt.title('The size of scRNA-seq\nNoisy clusters',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_noisy_sc-size.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_noisy_sc-size.pdf',dpi=300,bbox_inches='tight')
import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="noisy", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.red_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Noisy Clusters Size",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3.5, 3)
g.set_xticklabels(fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
plt.title('The size of scRNA-seq\nNoisy clusters',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_noisy_sc-size.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_noisy_sc-size.pdf',dpi=300,bbox_inches='tight')

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import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Trans_dif", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.red_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Transitions Confidence",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3, 3)
g.set_xticklabels(['Basophil','OPC'],fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
#plt.ylim(-0.01,0.05)
plt.title('The size of scRNA-seq\nInterpolation\nTransformation',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_trans_sc-size.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_trans_sc-size.pdf',dpi=300,bbox_inches='tight')
import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Trans_dif", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.red_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Transitions Confidence",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3, 3)
g.set_xticklabels(['Basophil','OPC'],fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
#plt.ylim(-0.01,0.05)
plt.title('The size of scRNA-seq\nInterpolation\nTransformation',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_trans_sc-size.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_trans_sc-size.pdf',dpi=300,bbox_inches='tight')

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import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Inter_cells", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.red_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "The number of Inter-cells",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3, 3)
g.set_xticklabels(['Basophil','OPC'],fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
#plt.ylim(-0.01,0.05)
plt.title('The size of scRNA-seq\nInterpolation',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_ic_sc-size.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_ic_sc-size.pdf',dpi=300,bbox_inches='tight')
import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Inter_cells", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.red_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "The number of Inter-cells",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3, 3)
g.set_xticklabels(['Basophil','OPC'],fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
#plt.ylim(-0.01,0.05)
plt.title('The size of scRNA-seq\nInterpolation',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_ic_sc-size.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_ic_sc-size.pdf',dpi=300,bbox_inches='tight')

Scale size¶

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import os
metric_li=[i for i in os.listdir('result') if 'cell_' in i]
metric_li
import os
metric_li=[i for i in os.listdir('result') if 'cell_' in i]
metric_li

Out[112]:

['metric_dg_cell_scale.pkl', 'metric_hpc_cell_scale.pkl']

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import pickle
metric_dict={}
for i in metric_li:
    with open(f'result/{i}','rb') as f:
        metric_dict[i.split('.')[0]]=pickle.load(f)
import pickle
metric_dict={}
for i in metric_li:
    with open(f'result/{i}','rb') as f:
        metric_dict[i.split('.')[0]]=pickle.load(f)

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plot_data=pd.DataFrame(columns=['dataset','model','Cor_mean','non_Cor_mean',
                               'Cos_mean','non_Cos_mean','Trans_raw','Trans_after',
                               'Var_raw','Var_after','noisy','Inter_cells'])
for i in metric_dict.keys():
    for j in metric_dict[i].keys():
        test_li=[i.split('_')[-3],j]+list(metric_dict[i][j].values())
        plot_data.loc[str(i)+'-'+str(j)]=test_li
plot_data=pd.DataFrame(columns=['dataset','model','Cor_mean','non_Cor_mean',
                               'Cos_mean','non_Cos_mean','Trans_raw','Trans_after',
                               'Var_raw','Var_after','noisy','Inter_cells'])
for i in metric_dict.keys():
    for j in metric_dict[i].keys():
        test_li=[i.split('_')[-3],j]+list(metric_dict[i][j].values())
        plot_data.loc[str(i)+'-'+str(j)]=test_li

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plot_data['Trans_dif']=plot_data['Trans_after'].values-plot_data['Trans_raw'].values
plot_data['Var_dif']=plot_data['Var_after'].values-plot_data['Var_raw'].values
plot_data['Trans_dif']=plot_data['Trans_after'].values-plot_data['Trans_raw'].values
plot_data['Var_dif']=plot_data['Var_after'].values-plot_data['Var_raw'].values

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plot_data['Cor_unique']=plot_data['Cor_mean'].values-plot_data['non_Cor_mean'].values
plot_data['Cos_unique']=plot_data['Cos_mean'].values-plot_data['non_Cos_mean'].values
plot_data['Cor_unique']=plot_data['Cor_mean'].values-plot_data['non_Cor_mean'].values
plot_data['Cos_unique']=plot_data['Cos_mean'].values-plot_data['non_Cos_mean'].values

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plot_data['dataset_full']=plot_data['dataset'].map(
    {'dg':'Dentategyrus','hpc':'Hematopoietic'}
)
plot_data['dataset_full']=plot_data['dataset'].map(
    {'dg':'Dentategyrus','hpc':'Hematopoietic'}
)

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plot_data
plot_data

Out[118]:

	dataset	model	Cor_mean	non_Cor_mean	Cos_mean	non_Cos_mean	Trans_after	Var_raw	Var_after	noisy	Inter_cells	Trans_dif	Var_dif	Cor_unique	Cos_unique	dataset_full
metric_dg_cell_scale-1	dg	1	0.991141	0.247988	0.556061	0.038165	0.017826	0.000524	0.001173	1	53	0.017826	0.000649	0.743153	0.517896	Dentategyrus
metric_dg_cell_scale-2	dg	2	0.986118	0.238093	0.520849	0.036509	0.012129	0.000524	0.002307	17	106	0.012129	0.001783	0.748025	0.484340	Dentategyrus
metric_dg_cell_scale-4	dg	4	0.993960	0.212104	0.515252	0.037088	0.000000	0.000524	0.058464	72	212	0.000000	0.057940	0.781856	0.478164	Dentategyrus
metric_dg_cell_scale-6	dg	6	0.964988	0.302982	0.484663	0.039640	0.023109	0.000524	0.007461	162	0	0.023109	0.006938	0.662005	0.445022	Dentategyrus
metric_dg_cell_scale-8	dg	8	0.962592	0.344744	0.466051	0.028750	0.023109	0.000524	0.007461	266	0	0.023109	0.006938	0.617848	0.437302	Dentategyrus
metric_dg_cell_scale-10	dg	10	0.964934	0.292920	0.482357	0.033685	0.023109	0.000524	0.007461	336	0	0.023109	0.006938	0.672014	0.448672	Dentategyrus
metric_hpc_cell_scale-1	hpc	1	0.992801	0.415896	0.459931	0.040868	0.010508	0.000353	0.011806	1	63	0.010508	0.011453	0.576905	0.419062	Hematopoietic
metric_hpc_cell_scale-2	hpc	2	0.994122	0.467403	0.472380	0.041679	0.000000	0.000353	0.009516	0	126	0.000000	0.009163	0.526720	0.430701	Hematopoietic
metric_hpc_cell_scale-4	hpc	4	0.993809	0.457815	0.452121	0.041665	0.015025	0.000353	0.015055	6	252	0.015025	0.014702	0.535994	0.410456	Hematopoietic
metric_hpc_cell_scale-6	hpc	6	0.994657	0.473551	0.473141	0.044046	0.000000	0.000353	0.005809	23	324	0.000000	0.005456	0.521105	0.429095	Hematopoietic
metric_hpc_cell_scale-8	hpc	8	0.990709	0.469833	0.470366	0.043068	0.000000	0.000353	0.023709	23	416	0.000000	0.023357	0.520877	0.427298	Hematopoietic
metric_hpc_cell_scale-10	hpc	10	0.991871	0.453656	0.470932	0.043742	0.018318	0.000353	0.024470	32	490	0.018318	0.024118	0.538215	0.427190	Hematopoietic

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import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Cor_unique", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.green_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Pearsonr",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3.5, 3)
g.set_xticklabels(fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
plt.title('Interpolation Scale Size\nExpression Correlation\n(Unique)',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_cor_scale.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_cor_scale.pdf',dpi=300,bbox_inches='tight')
import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Cor_unique", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.green_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Pearsonr",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3.5, 3)
g.set_xticklabels(fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
plt.title('Interpolation Scale Size\nExpression Correlation\n(Unique)',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_cor_scale.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_cor_scale.pdf',dpi=300,bbox_inches='tight')

In [121]:

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import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Cos_unique", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.green_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Cosine Similarity",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3.5, 3)
g.set_xticklabels(fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
plt.title('Interpolation Scale Size\nMarker Similarity\n(Unique)',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_cos_scale.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_cos_scale.pdf',dpi=300,bbox_inches='tight')
import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Cos_unique", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.green_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Cosine Similarity",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3.5, 3)
g.set_xticklabels(fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
plt.title('Interpolation Scale Size\nMarker Similarity\n(Unique)',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_cos_scale.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_cos_scale.pdf',dpi=300,bbox_inches='tight')

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import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="noisy", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.green_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Noisy Clusters Size",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3.5, 3)
g.set_xticklabels(fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
plt.title('Interpolation Scale Size\nNoisy clusters',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_noisy_scale.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_noisy_scale.pdf',dpi=300,bbox_inches='tight')
import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="noisy", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.green_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Noisy Clusters Size",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3.5, 3)
g.set_xticklabels(fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
plt.title('Interpolation Scale Size\nNoisy clusters',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_noisy_scale.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_noisy_scale.pdf',dpi=300,bbox_inches='tight')

In [123]:

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import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Trans_dif", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.green_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Transitions Confidence",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3, 3)
g.set_xticklabels(['Basophil','OPC'],fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
#plt.ylim(-0.01,0.05)
plt.title('Interpolation Scale Size\nInterpolation\nTransformation',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_trans_scale.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_trans_scale.pdf',dpi=300,bbox_inches='tight')
import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Trans_dif", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.green_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Transitions Confidence",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3, 3)
g.set_xticklabels(['Basophil','OPC'],fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
#plt.ylim(-0.01,0.05)
plt.title('Interpolation Scale Size\nInterpolation\nTransformation',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_trans_scale.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_trans_scale.pdf',dpi=300,bbox_inches='tight')

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import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Inter_cells", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.green_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "The number of Inter-cells",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3, 3)
g.set_xticklabels(['Basophil','OPC'],fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
#plt.ylim(-0.01,0.05)
plt.title('Interpolation Scale Size\nInterpolation',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_ic_scale.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_ic_scale.pdf',dpi=300,bbox_inches='tight')
import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Inter_cells", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.green_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "The number of Inter-cells",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3, 3)
g.set_xticklabels(['Basophil','OPC'],fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
#plt.ylim(-0.01,0.05)
plt.title('Interpolation Scale Size\nInterpolation',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_ic_scale.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_ic_scale.pdf',dpi=300,bbox_inches='tight')

hidden size¶

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import os
metric_li=[i for i in os.listdir('result') if 'hidden' in i]
metric_li
import os
metric_li=[i for i in os.listdir('result') if 'hidden' in i]
metric_li

Out[125]:

['metric_dg_hidden.pkl', 'metric_hpc_hidden.pkl']

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import pickle
metric_dict={}
for i in metric_li:
    with open(f'result/{i}','rb') as f:
        metric_dict[i.split('.')[0]]=pickle.load(f)
import pickle
metric_dict={}
for i in metric_li:
    with open(f'result/{i}','rb') as f:
        metric_dict[i.split('.')[0]]=pickle.load(f)

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plot_data=pd.DataFrame(columns=['dataset','model','Cor_mean','non_Cor_mean',
                               'Cos_mean','non_Cos_mean','Trans_raw','Trans_after',
                               'Var_raw','Var_after','noisy','Inter_cells'])
for i in metric_dict.keys():
    for j in metric_dict[i].keys():
        test_li=[i.split('_')[-2],j]+list(metric_dict[i][j].values())
        plot_data.loc[str(i)+'-'+str(j)]=test_li
plot_data=pd.DataFrame(columns=['dataset','model','Cor_mean','non_Cor_mean',
                               'Cos_mean','non_Cos_mean','Trans_raw','Trans_after',
                               'Var_raw','Var_after','noisy','Inter_cells'])
for i in metric_dict.keys():
    for j in metric_dict[i].keys():
        test_li=[i.split('_')[-2],j]+list(metric_dict[i][j].values())
        plot_data.loc[str(i)+'-'+str(j)]=test_li

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plot_data['Trans_dif']=plot_data['Trans_after'].values-plot_data['Trans_raw'].values
plot_data['Var_dif']=plot_data['Var_after'].values-plot_data['Var_raw'].values
plot_data['Trans_dif']=plot_data['Trans_after'].values-plot_data['Trans_raw'].values
plot_data['Var_dif']=plot_data['Var_after'].values-plot_data['Var_raw'].values

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plot_data['Cor_unique']=plot_data['Cor_mean'].values-plot_data['non_Cor_mean'].values
plot_data['Cos_unique']=plot_data['Cos_mean'].values-plot_data['non_Cos_mean'].values
plot_data['Cor_unique']=plot_data['Cor_mean'].values-plot_data['non_Cor_mean'].values
plot_data['Cos_unique']=plot_data['Cos_mean'].values-plot_data['non_Cos_mean'].values

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plot_data['dataset_full']=plot_data['dataset'].map(
    {'dg':'Dentategyrus','hpc':'Hematopoietic'}
)
plot_data['dataset_full']=plot_data['dataset'].map(
    {'dg':'Dentategyrus','hpc':'Hematopoietic'}
)

In [131]:

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plot_data
plot_data

Out[131]:

	dataset	model	Cor_mean	non_Cor_mean	Cos_mean	non_Cos_mean	Trans_after	Var_raw	Var_after	noisy	Inter_cells	Trans_dif	Var_dif	Cor_unique	Cos_unique	dataset_full
metric_dg_hidden-64	dg	64	0.992447	0.259584	0.555072	0.039374	0.031360	0.000524	0.000104	14	84	0.031360	-0.000420	0.732863	0.515698	Dentategyrus
metric_dg_hidden-128	dg	128	0.991807	0.281389	0.544913	0.038932	0.000000	0.000524	0.003984	17	106	0.000000	0.003460	0.710418	0.505981	Dentategyrus
metric_dg_hidden-256	dg	256	0.988232	0.287221	0.548476	0.038911	0.022114	0.000524	0.001214	21	90	0.022114	0.000690	0.701011	0.509565	Dentategyrus
metric_dg_hidden-512	dg	512	0.992349	0.272541	0.552497	0.039007	0.029417	0.000524	0.001403	22	90	0.029417	0.000880	0.719808	0.513490	Dentategyrus
metric_dg_hidden-1024	dg	1024	0.991660	0.276692	0.540695	0.039201	0.028747	0.000524	0.000016	17	84	0.028747	-0.000507	0.714969	0.501494	Dentategyrus
metric_hpc_hidden-64	hpc	64	0.993006	0.483260	0.525996	0.046454	0.000000	0.000353	0.013150	0	126	0.000000	0.012797	0.509747	0.479542	Hematopoietic
metric_hpc_hidden-128	hpc	128	0.993926	0.457483	0.527889	0.045535	0.000000	0.000353	0.004588	0	126	0.000000	0.004235	0.536443	0.482354	Hematopoietic
metric_hpc_hidden-256	hpc	256	0.991681	0.481413	0.506855	0.044611	0.017437	0.000353	0.009904	0	126	0.017437	0.009551	0.510269	0.462244	Hematopoietic
metric_hpc_hidden-512	hpc	512	0.994028	0.454335	0.532685	0.046464	0.000000	0.000353	0.008248	1	108	0.000000	0.007895	0.539693	0.486222	Hematopoietic
metric_hpc_hidden-1024	hpc	1024	0.993393	0.482132	0.522975	0.045669	0.014599	0.000353	0.014261	0	126	0.014599	0.013908	0.511261	0.477307	Hematopoietic

In [132]:

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import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Cor_unique", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.blue_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Pearsonr",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3.5, 3)
g.set_xticklabels(fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
plt.title('Hidden Layer Size\nExpression Correlation\n(Unique)',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_cor_hidden.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_cor_hidden.pdf',dpi=300,bbox_inches='tight')
import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Cor_unique", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.blue_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Pearsonr",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3.5, 3)
g.set_xticklabels(fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
plt.title('Hidden Layer Size\nExpression Correlation\n(Unique)',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_cor_hidden.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_cor_hidden.pdf',dpi=300,bbox_inches='tight')

In [133]:

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import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Cos_unique", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.blue_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Cosine Similarity",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3.5, 3)
g.set_xticklabels(fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
plt.title('Hidden Layer Size\nMarker Similarity\n(Unique)',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_cos_hidden.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_cos_hidden.pdf',dpi=300,bbox_inches='tight')
import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Cos_unique", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.blue_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Cosine Similarity",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3.5, 3)
g.set_xticklabels(fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
plt.title('Hidden Layer Size\nMarker Similarity\n(Unique)',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_cos_hidden.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_cos_hidden.pdf',dpi=300,bbox_inches='tight')

In [134]:

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import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="noisy", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.blue_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Noisy Clusters Size",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3.5, 3)
g.set_xticklabels(fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
plt.title('Hidden Layer Size\nNoisy clusters',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_noisy_hidden.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_noisy_hidden.pdf',dpi=300,bbox_inches='tight')
import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="noisy", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.blue_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Noisy Clusters Size",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3.5, 3)
g.set_xticklabels(fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
plt.title('Hidden Layer Size\nNoisy clusters',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_noisy_hidden.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_noisy_hidden.pdf',dpi=300,bbox_inches='tight')

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import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Trans_dif", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.blue_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Transitions Confidence",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3, 3)
g.set_xticklabels(['OPC','Basophil'],fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
#plt.ylim(-0.01,0.05)
plt.title('Hidden Layer Size\nInterpolation\nTransformation',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_trans_hidden.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_trans_hidden.pdf',dpi=300,bbox_inches='tight')
import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Trans_dif", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.blue_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Transitions Confidence",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3, 3)
g.set_xticklabels(['OPC','Basophil'],fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
#plt.ylim(-0.01,0.05)
plt.title('Hidden Layer Size\nInterpolation\nTransformation',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_trans_hidden.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_trans_hidden.pdf',dpi=300,bbox_inches='tight')

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import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Inter_cells", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.blue_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "The number of Inter-cells",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3, 3)
g.set_xticklabels(['OPC','Basophil'],fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
#plt.ylim(-0.01,0.05)
plt.title('Hidden Layer Size\nInterpolation',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_ic_hidden.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_ic_hidden.pdf',dpi=300,bbox_inches='tight')
import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data, kind="bar",
    x="dataset_full", y="Inter_cells", hue="model",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.blue_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "The number of Inter-cells",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3, 3)
g.set_xticklabels(['OPC','Basophil'],fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
#plt.ylim(-0.01,0.05)
plt.title('Hidden Layer Size\nInterpolation',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_ic_hidden.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_ic_hidden.pdf',dpi=300,bbox_inches='tight')

Calculate time¶

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hpc_time={
    '1000':'04:56',
    '2000':'16:57',
    '5000':'41:18+02:33',
    '10000':'39:38+16:53',
    '20000':'70:51+43:09',
}

dg_time={
    '1000':'09:12',
    '2000':'18:24',
    '5000':'22:54+04:57',
}

def time_to_minutes(time_str):
    parts = time_str.split('+')
    total_minutes = 0
    for part in parts:
        time_parts = part.split(':')
        if len(time_parts) == 2:
            hours, minutes = map(int, time_parts)
            total_minutes += hours * 60 + minutes
        elif len(time_parts) == 3:
            hours, minutes, seconds = map(int, time_parts)
            total_minutes += hours * 60 + minutes + seconds / 60
    return total_minutes

result1 = {}
for key, value in hpc_time.items():
    result1[key] = time_to_minutes(value)

result2 = {}
for key, value in dg_time.items():
    result2[key] = time_to_minutes(value)
hpc_time={
    '1000':'04:56',
    '2000':'16:57',
    '5000':'41:18+02:33',
    '10000':'39:38+16:53',
    '20000':'70:51+43:09',
}

dg_time={
    '1000':'09:12',
    '2000':'18:24',
    '5000':'22:54+04:57',
}

def time_to_minutes(time_str):
    parts = time_str.split('+')
    total_minutes = 0
    for part in parts:
        time_parts = part.split(':')
        if len(time_parts) == 2:
            hours, minutes = map(int, time_parts)
            total_minutes += hours * 60 + minutes
        elif len(time_parts) == 3:
            hours, minutes, seconds = map(int, time_parts)
            total_minutes += hours * 60 + minutes + seconds / 60
    return total_minutes

result1 = {}
for key, value in hpc_time.items():
    result1[key] = time_to_minutes(value)

result2 = {}
for key, value in dg_time.items():
    result2[key] = time_to_minutes(value)

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result2
result2

Out[143]:

{'1000': 552, '2000': 1104, '5000': 1671}

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plot_data1=pd.DataFrame(columns=['model','cellsize','time'])
for key, value in result1.items():
    plot_data1.loc['hpc'+key]=['hpc',key,value]
for key, value in result2.items():
    plot_data1.loc['dg'+key]=['dg',key,value]
plot_data1['dataset_full']=plot_data1['model'].map(
    {'dg':'Dentategyrus','hpc':'Hematopoietic'}
)
plot_data1=pd.DataFrame(columns=['model','cellsize','time'])
for key, value in result1.items():
    plot_data1.loc['hpc'+key]=['hpc',key,value]
for key, value in result2.items():
    plot_data1.loc['dg'+key]=['dg',key,value]
plot_data1['dataset_full']=plot_data1['model'].map(
    {'dg':'Dentategyrus','hpc':'Hematopoietic'}
)

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plot_data1
plot_data1

Out[150]:

	model	cellsize	time	dataset_full
hpc1000	hpc	1000	296	Hematopoietic
hpc2000	hpc	2000	1017	Hematopoietic
hpc5000	hpc	5000	2631	Hematopoietic
hpc10000	hpc	10000	3391	Hematopoietic
hpc20000	hpc	20000	6840	Hematopoietic
dg1000	dg	1000	552	Dentategyrus
dg2000	dg	2000	1104	Dentategyrus
dg5000	dg	5000	1671	Dentategyrus

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import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data1, kind="bar",
    x="dataset_full", y="time", hue="cellsize",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.orange_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Time (Minutes)",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3.5, 3)
g.set_xticklabels(fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
#plt.ylim(-0.01,0.05)
plt.title('The size of scRNA-seq',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_caltime.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_caltime.pdf',dpi=300,bbox_inches='tight')
import seaborn as sns
#sns.set_theme(style="whitegrid")

#penguins = pd.read_csv('penguins.csv')
#fig, ax = plt.subplots(figsize=(4,3))
# Draw a nested barplot by species and sex
g = sns.catplot(
    data=plot_data1, kind="bar",
    x="dataset_full", y="time", hue="cellsize",
    errorbar="sd", #palette="dark", 
    alpha=.6, height=4, 
    palette=ov.utils.orange_color[:]
    
)
g.despine(left=True)
g.set_axis_labels("", "Time (Minutes)",fontsize=13,fontweight='bold')
g.legend.set_title("")
g.legend.set_bbox_to_anchor((1.2,0.5))
g.fig.set_size_inches(3.5, 3)
g.set_xticklabels(fontsize=12,fontweight='bold')
g.set_yticklabels(fontsize=12,fontweight='bold')
plt.tight_layout()
#plt.ylim(-0.01,0.05)
plt.title('The size of scRNA-seq',fontsize=13,fontweight='bold')

plt.savefig('figures/metric/arg_bar_caltime.png',dpi=300,bbox_inches='tight')
plt.savefig('pdf/metric/arg_bar_caltime.pdf',dpi=300,bbox_inches='tight')

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