Application2 of scRL on HSCs dataset
[1]:
import scanpy as sc
from anndata import AnnData as ad
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib as mpl
from matplotlib import ticker
from matplotlib import font_manager
font_manager.fontManager.addfont('../../fonts/Arial Bold Italic.ttf')
font_manager.fontManager.addfont('../../fonts/Arial Bold.ttf')
font_manager.fontManager.addfont('../../fonts/Arial Italic.ttf')
font_manager.fontManager.addfont('../../fonts/Arial.ttf')
plt.rcParams['font.sans-serif'] = ['Arial']
cm = 1/2.54
sc.set_figure_params(dpi=150,figsize=(5,5),fontsize=14,frameon=False)
from scRL.GridCore import grids_from_embedding, project_cluster, align_pseudotime, project_back, project_expression
from scRL.EnvironmentCore import lineage_rewards
from scRL.Trainer import trainer
from scRL.utils import get_traj, moving_average
import torch
device = torch.device('cuda')
[2]:
adata = sc.read_h5ad('data/setty_bone_marrow.h5ad')
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, n_top_genes=2000)
sc.pp.pca(adata, use_highly_variable=True)
sc.pp.neighbors(adata)
sc.tl.umap(adata)
sc.tl.leiden(adata)
sc.pl.umap(adata, color='leiden',legend_loc='on data')
/home/lilizi/miniconda3/envs/blood/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
cax = scatter(
[3]:
adata.obs['clusters'] = pd.Categorical(adata.obs['clusters']
,['HSC_1','HSC_2','Precursors','Mega','Ery_1'
,'Ery_2','Mono_1','Mono_2','DCs','CLP']
)
clusters = adata.obs['clusters']
pal = sorted(sns.color_palette('tab10',n_colors=10),key=lambda x: x[2])
adata.uns['clusters_colors'] = pal
sc.pl.umap(adata, color='clusters', palette=pal)
/home/lilizi/miniconda3/envs/blood/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
cax = scatter(
[13]:
sc.pl.umap(adata, color='clusters', s=50,show=False,title='',legend_loc=None)
plt.savefig('F1.png',dpi=600,bbox_inches='tight')
/home/lilizi/miniconda3/envs/blood/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
cax = scatter(
[4]:
hsc = pd.read_table('data/GSE117498_RAW/GSM3305359_HSC.raw_counts.tsv.gz',header=0,index_col=0).iloc[1:,:].T
mpp = pd.read_table('data/GSE117498_RAW/GSM3305360_MPP.raw_counts.tsv.gz',header=0,index_col=0).iloc[1:,:].T
mlp = pd.read_table('data/GSE117498_RAW/GSM3305361_MLP.raw_counts.tsv.gz',header=0,index_col=0).iloc[1:,:].T
prebnk = pd.read_table('data/GSE117498_RAW/GSM3305362_PreBNK.raw_counts.tsv.gz',header=0,index_col=0).iloc[1:,:].T
mep = pd.read_table('data/GSE117498_RAW/GSM3305363_MEP.raw_counts.tsv.gz',header=0,index_col=0).iloc[1:,:].T
cmp = pd.read_table('data/GSE117498_RAW/GSM3305364_CMP.raw_counts.tsv.gz',header=0,index_col=0).iloc[1:,:].T
gmp = pd.read_table('data/GSE117498_RAW/GSM3305365_GMP.raw_counts.tsv.gz',header=0,index_col=0).iloc[1:,:].T
[5]:
adata1 = ad(hsc).concatenate([ad(mpp),ad(mlp),ad(prebnk),ad(mep),ad(cmp),ad(gmp)], batch_categories=['HSC','MPP','MLP','PreBNK','MEP','CMP','GMP'])
sc.pp.normalize_total(adata1, target_sum=1e4)
sc.pp.log1p(adata1)
sc.pp.highly_variable_genes(adata1)
sc.pp.pca(adata1, use_highly_variable=True)
sc.pp.neighbors(adata1)
sc.tl.leiden(adata1)
sc.tl.umap(adata1)
sc.pl.umap(adata1, color='batch')
/tmp/ipykernel_40175/2425512052.py:1: FutureWarning: X.dtype being converted to np.float32 from int64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.
adata1 = ad(hsc).concatenate([ad(mpp),ad(mlp),ad(prebnk),ad(mep),ad(cmp),ad(gmp)], batch_categories=['HSC','MPP','MLP','PreBNK','MEP','CMP','GMP'])
/home/lilizi/miniconda3/envs/blood/lib/python3.9/site-packages/anndata/_core/anndata.py:1785: FutureWarning: X.dtype being converted to np.float32 from float64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.
[AnnData(sparse.csr_matrix(a.shape), obs=a.obs) for a in all_adatas],
/home/lilizi/miniconda3/envs/blood/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
cax = scatter(
[10]:
pal1 = sorted([mpl.colors.hex2color(c) for c in adata1.uns['batch_colors']],key=lambda x:x[2])
ax = sc.pl.umap(adata1, color='batch', palette=pal1,s=50,title='',frameon=False,legend_loc=None,show=False)
sc.pl.umap(adata1[adata1.obs['batch'].isin(['HSC','MPP'])], color='batch',s=50,title='',frameon=False,legend_loc=None,show=False,ax=ax)
plt.savefig('F2.png',dpi=600, bbox_inches='tight')
/home/lilizi/miniconda3/envs/blood/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
cax = scatter(
/home/lilizi/miniconda3/envs/blood/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
cax = scatter(
[6]:
def procedure(adata, key, start):
X = adata.obsm['X_umap']
X_pca = adata.obsm['X_pca']
clusters = adata.obs[key]
gres = grids_from_embedding(X)
gres = project_cluster(gres, clusters, cluster_colors=adata.uns[f'{key}_colors'])
gres = align_pseudotime(gres, start)
gres = project_back(gres, 'pseudotime')
adata.obs['time'] = gres.embedding['pseudotime']
return gres
[7]:
gres = procedure(adata, 'clusters', 'HSC_1')
Boundary generating: 100%|███████████████████| 196/196 [00:00<00:00, 212.96it/s]
Mask testing: 100%|███████████████████████████████| 8/8 [00:00<00:00, 24.78it/s]
Adjacent generating: 100%|████████████████| 1071/1071 [00:00<00:00, 2415.64it/s]
Boundary pruning: 100%|████████████████████| 240/240 [00:00<00:00, 10790.13it/s]
Adjacent generating: 100%|████████████████| 1163/1163 [00:00<00:00, 2303.05it/s]
Time used for mapping grids: 5.74 seconds
Time used for projecting annotation : 1.14 seconds
Time used for aligning pseudo-time : 0.67 seconds
[22]:
sc.pl.umap(adata, color='time',s=50,title='',show=False)
plt.savefig('F3.png',dpi=600,bbox_inches='tight')
[8]:
gres1 = procedure(adata1, 'batch', 'HSC')
Boundary generating: 100%|███████████████████| 196/196 [00:00<00:00, 197.91it/s]
Mask testing: 100%|███████████████████████████████| 8/8 [00:00<00:00, 26.99it/s]
Adjacent generating: 100%|████████████████| 1653/1653 [00:00<00:00, 1887.07it/s]
Boundary pruning: 100%|████████████████████| 227/227 [00:00<00:00, 10970.37it/s]
Adjacent generating: 100%|████████████████| 1699/1699 [00:00<00:00, 2144.77it/s]
Time used for mapping grids: 6.06 seconds
Time used for projecting annotation : 1.02 seconds
Time used for aligning pseudo-time : 0.61 seconds
[23]:
sc.pl.umap(adata1, color='time',s=50,title='',show=False)
plt.savefig('F4.png',dpi=600,bbox_inches='tight')
[ ]:
[ ]:
[58]:
start = 'Run4_195606431320494'
sc.tl.diffmap(adata)
adata.uns['iroot'] = np.where(adata.obs_names == start)[0].item()
sc.tl.dpt(adata)
start1 = adata1.obs_names[np.argmax(adata1.obsm['X_umap'][:,1])]
sc.tl.diffmap(adata1)
adata1.uns['iroot'] = np.argmax(adata1.obsm['X_umap'][:,1])
sc.tl.dpt(adata1)
[31]:
from wishbone import wb
res = wb.c_wishbone(adata.obsm['X_diffmap'][:, [1,2]],
s=np.where(adata.obs_names == start)[0]
)
res1 = wb.c_wishbone(adata1.obsm['X_diffmap'][:, [1,2]],
s=np.where(adata1.obs_names == start1)[0]
)
adata.obs['wishbone_pseudotime'] = res['Trajectory']
adata1.obs['wishbone_pseudotime'] = res1['Trajectory']
[196]:
import scanpy.external as sce
[64]:
sce.tl.palantir(adata)
pr_res = sce.tl.palantir_results(adata, early_cell=start)
[198]:
sns.set_style('ticks')
[64]:
adata.obs['palantir_pseudotime'] = pr_res.pseudotime
[197]:
sce.tl.palantir(adata1)
pr_res1 = sce.tl.palantir_results(adata1, early_cell=start1)
adata1.obs['palantir_pseudotime'] = pr_res1.pseudotime
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Determing nearest neighbor graph...
Sampling and flocking waypoints...
Time for determining waypoints: 0.008221256732940673 minutes
Determining pseudotime...
Shortest path distances using 30-nearest neighbor graph...
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Time for shortest paths: 0.12091676791508993 minutes
Iteratively refining the pseudotime...
Correlation at iteration 1: 0.9996
Correlation at iteration 2: 1.0000
Entropy and branch probabilities...
Markov chain construction...
Identification of terminal states...
Computing fundamental matrix and absorption probabilities...
Project results to all cells...
[40]:
adata[:,adata.var['highly_variable']].to_df().T.to_csv('cd34exp.csv')
g = adata[:,adata.var['highly_variable']].var_names
pd.DataFrame({'gene':g,'gene_short_names':g}).to_csv('cd34fdata.csv')
adata.obs.to_csv('cd34pdata.csv')
adata1[:,adata1.var['highly_variable']].to_df().T.to_csv('phenoexp.csv')
g = adata1[:,adata1.var['highly_variable']].var_names
pd.DataFrame({'gene':g,'gene_short_names':g}).to_csv('phenofdata.csv')
adata1.obs.to_csv('phenopdata.csv')
[43]:
adata.obs['monocle_time'] = pd.read_csv('mpt.csv',header=0,index_col=0).values
adata.obs['monocle3_time'] = pd.read_csv('mpt3.csv',header=0,index_col=0).values
adata1.obs['monocle_time'] = pd.read_csv('../../../phenompt.csv',header=0,index_col=0).values
adata1.obs['monocle3_time'] = pd.read_csv('../../../phenompt3.csv',header=0,index_col=0).values
[50]:
adata.obs['umap_dis'] = np.linalg.norm(adata.obsm['X_umap'][adata.uns['iroot'],:]-adata.obsm['X_umap'], axis=1)
adata1.obs['umap_dis'] = np.linalg.norm(adata1.obsm['X_umap'][adata1.uns['iroot'],:]-adata1.obsm['X_umap'], axis=1)
[16]:
for i, t in enumerate(['time','palantir_pseudotime','wishbone_pseudotime','dpt_pseudotime','monocle_time','monocle3_time']):
adata.obs[t] = times[t]
sc.pl.umap(adata, color=t,s=50,show=False,colorbar_loc=None,title='')
plt.savefig(f'F5_{i}.png',dpi=600,bbox_inches='tight')
[19]:
for i, t in enumerate(['time','palantir_pseudotime','wishbone_pseudotime','dpt_pseudotime','monocle_time','monocle3_time']):
adata1.obs[t] = times1[t]
sc.pl.umap(adata1, color=t,s=50,show=False,colorbar_loc=None,title='')
plt.savefig(f'F6_{i}.png',dpi=600,bbox_inches='tight')
[73]:
adata.obs[['umap_dis','time','palantir_pseudotime','wishbone_pseudotime','dpt_pseudotime','monocle_time','monocle3_time']].to_csv('cd34_times.csv')
[74]:
adata1.obs[['umap_dis','time','palantir_pseudotime','wishbone_pseudotime','dpt_pseudotime','monocle_time','monocle3_time']].to_csv('pheno_times.csv')
[13]:
times = pd.read_csv('cd34_times.csv',header=0,index_col=0)
times1 = pd.read_csv('pheno_times.csv',header=0,index_col=0)
[ ]:
[ ]:
[56]:
adata.obs[['umap_dis','time','palantir_pseudotime','wishbone_pseudotime','dpt_pseudotime','monocle_time','monocle3_time']].corr()
[56]:
| umap_dis | time | palantir_pseudotime | wishbone_pseudotime | dpt_pseudotime | monocle_time | monocle3_time | |
|---|---|---|---|---|---|---|---|
| umap_dis | 1.000000 | 0.968365 | 0.893793 | 0.846124 | 0.897445 | -0.403734 | 0.862021 |
| time | 0.968365 | 1.000000 | 0.845803 | 0.876461 | 0.936078 | -0.526510 | 0.855501 |
| palantir_pseudotime | 0.893793 | 0.845803 | 1.000000 | 0.876030 | 0.874180 | -0.065465 | 0.691234 |
| wishbone_pseudotime | 0.846124 | 0.876461 | 0.876030 | 1.000000 | 0.975520 | -0.247391 | 0.583827 |
| dpt_pseudotime | 0.897445 | 0.936078 | 0.874180 | 0.975520 | 1.000000 | -0.371213 | 0.713829 |
| monocle_time | -0.403734 | -0.526510 | -0.065465 | -0.247391 | -0.371213 | 1.000000 | -0.223607 |
| monocle3_time | 0.862021 | 0.855501 | 0.691234 | 0.583827 | 0.713829 | -0.223607 | 1.000000 |
[68]:
adata.obs[['umap_dis','time','palantir_pseudotime','wishbone_pseudotime','dpt_pseudotime','monocle_time','monocle3_time']].corr(method='spearman')
[68]:
| umap_dis | time | palantir_pseudotime | wishbone_pseudotime | dpt_pseudotime | monocle_time | monocle3_time | |
|---|---|---|---|---|---|---|---|
| umap_dis | 1.000000 | 0.987028 | 0.910618 | 0.892078 | 0.933179 | -0.251680 | 0.881750 |
| time | 0.987028 | 1.000000 | 0.886383 | 0.861972 | 0.914707 | -0.287535 | 0.869918 |
| palantir_pseudotime | 0.910618 | 0.886383 | 1.000000 | 0.967446 | 0.980356 | 0.064501 | 0.764251 |
| wishbone_pseudotime | 0.892078 | 0.861972 | 0.967446 | 1.000000 | 0.977595 | 0.040673 | 0.740726 |
| dpt_pseudotime | 0.933179 | 0.914707 | 0.980356 | 0.977595 | 1.000000 | -0.010969 | 0.780016 |
| monocle_time | -0.251680 | -0.287535 | 0.064501 | 0.040673 | -0.010969 | 1.000000 | -0.065490 |
| monocle3_time | 0.881750 | 0.869918 | 0.764251 | 0.740726 | 0.780016 | -0.065490 | 1.000000 |
[69]:
adata.obs[['umap_dis','time','palantir_pseudotime','wishbone_pseudotime','dpt_pseudotime','monocle_time','monocle3_time']].corr(method='kendall')
[69]:
| umap_dis | time | palantir_pseudotime | wishbone_pseudotime | dpt_pseudotime | monocle_time | monocle3_time | |
|---|---|---|---|---|---|---|---|
| umap_dis | 1.000000 | 0.914122 | 0.759110 | 0.721722 | 0.785419 | -0.165547 | 0.702206 |
| time | 0.914122 | 1.000000 | 0.729621 | 0.687235 | 0.758143 | -0.189250 | 0.686804 |
| palantir_pseudotime | 0.759110 | 0.729621 | 1.000000 | 0.843980 | 0.879671 | 0.040376 | 0.595386 |
| wishbone_pseudotime | 0.721722 | 0.687235 | 0.843980 | 1.000000 | 0.885222 | 0.028135 | 0.541465 |
| dpt_pseudotime | 0.785419 | 0.758143 | 0.879671 | 0.885222 | 1.000000 | 0.000868 | 0.601444 |
| monocle_time | -0.165547 | -0.189250 | 0.040376 | 0.028135 | 0.000868 | 1.000000 | -0.033961 |
| monocle3_time | 0.702206 | 0.686804 | 0.595386 | 0.541465 | 0.601444 | -0.033961 | 1.000000 |
[67]:
adata1.obs[['umap_dis','time','palantir_pseudotime','wishbone_pseudotime','dpt_pseudotime','monocle_time','monocle3_time']].corr()
[67]:
| umap_dis | time | palantir_pseudotime | wishbone_pseudotime | dpt_pseudotime | monocle_time | monocle3_time | |
|---|---|---|---|---|---|---|---|
| umap_dis | 1.000000 | 0.976782 | 0.728338 | 0.743226 | 0.881044 | 0.212012 | 0.768849 |
| time | 0.976782 | 1.000000 | 0.807974 | 0.794167 | 0.914937 | 0.124536 | 0.814106 |
| palantir_pseudotime | 0.728338 | 0.807974 | 1.000000 | 0.908804 | 0.937603 | 0.060842 | 0.691790 |
| wishbone_pseudotime | 0.743226 | 0.794167 | 0.908804 | 1.000000 | 0.916202 | 0.025952 | 0.651030 |
| dpt_pseudotime | 0.881044 | 0.914937 | 0.937603 | 0.916202 | 1.000000 | 0.152000 | 0.730693 |
| monocle_time | 0.212012 | 0.124536 | 0.060842 | 0.025952 | 0.152000 | 1.000000 | -0.090880 |
| monocle3_time | 0.768849 | 0.814106 | 0.691790 | 0.651030 | 0.730693 | -0.090880 | 1.000000 |
[70]:
adata1.obs[['umap_dis','time','palantir_pseudotime','wishbone_pseudotime','dpt_pseudotime','monocle_time','monocle3_time']].corr(method='spearman')
[70]:
| umap_dis | time | palantir_pseudotime | wishbone_pseudotime | dpt_pseudotime | monocle_time | monocle3_time | |
|---|---|---|---|---|---|---|---|
| umap_dis | 1.000000 | 0.977493 | 0.587837 | 0.789439 | 0.951279 | 0.265922 | 0.756017 |
| time | 0.977493 | 1.000000 | 0.699308 | 0.864583 | 0.968940 | 0.182283 | 0.805172 |
| palantir_pseudotime | 0.587837 | 0.699308 | 1.000000 | 0.841612 | 0.705644 | 0.153153 | 0.673639 |
| wishbone_pseudotime | 0.789439 | 0.864583 | 0.841612 | 1.000000 | 0.890381 | 0.057648 | 0.787674 |
| dpt_pseudotime | 0.951279 | 0.968940 | 0.705644 | 0.890381 | 1.000000 | 0.144937 | 0.833106 |
| monocle_time | 0.265922 | 0.182283 | 0.153153 | 0.057648 | 0.144937 | 1.000000 | -0.003457 |
| monocle3_time | 0.756017 | 0.805172 | 0.673639 | 0.787674 | 0.833106 | -0.003457 | 1.000000 |
[71]:
adata1.obs[['umap_dis','time','palantir_pseudotime','wishbone_pseudotime','dpt_pseudotime','monocle_time','monocle3_time']].corr(method='kendall')
[71]:
| umap_dis | time | palantir_pseudotime | wishbone_pseudotime | dpt_pseudotime | monocle_time | monocle3_time | |
|---|---|---|---|---|---|---|---|
| umap_dis | 1.000000 | 0.875926 | 0.392881 | 0.594686 | 0.821572 | 0.150719 | 0.534956 |
| time | 0.875926 | 1.000000 | 0.491095 | 0.675882 | 0.842827 | 0.080135 | 0.580485 |
| palantir_pseudotime | 0.392881 | 0.491095 | 1.000000 | 0.648919 | 0.499077 | 0.107146 | 0.429151 |
| wishbone_pseudotime | 0.594686 | 0.675882 | 0.648919 | 1.000000 | 0.727705 | 0.007227 | 0.555583 |
| dpt_pseudotime | 0.821572 | 0.842827 | 0.499077 | 0.727705 | 1.000000 | 0.069726 | 0.611368 |
| monocle_time | 0.150719 | 0.080135 | 0.107146 | 0.007227 | 0.069726 | 1.000000 | 0.000199 |
| monocle3_time | 0.534956 | 0.580485 | 0.429151 | 0.555583 | 0.611368 | 0.000199 | 1.000000 |
[22]:
X_pca = adata.obsm['X_pca']
X_pca1 = adata1.obsm['X_pca']
[20]:
gres = lineage_rewards(gres, ['HSC_1','HSC_2'], ['Ery_1','Ery_2','Mega'], mode='Contribution')
[210]:
gres1 = lineage_rewards(gres1, ['HSC','MPP'], ['MEP'], mode='Contribution')
Reward generating: 100%|██████████████████| 1699/1699 [00:00<00:00, 2719.81it/s]
Time used for generating rewards : 0.65 seconds
[20]:
t_ery = trainer('ActorCritic', gres, reward_mode='Contribution', X_latent=X_pca, num_episodes=5e3)
t_ery.train()
[214]:
t_ery1 = trainer('ActorCritic', gres1, reward_mode='Contribution', X_latent=X_pca1, num_episodes=5e3)
t_ery1.train()
Iteration1: 100%|█████████████| 500/500 [00:03<00:00, 159.30it/s, E=500, R=1.80]
Iteration2: 100%|█████████████| 500/500 [00:05<00:00, 90.11it/s, E=1000, R=5.35]
Iteration3: 100%|█████████████| 500/500 [00:06<00:00, 80.40it/s, E=1500, R=7.41]
Iteration4: 100%|█████████████| 500/500 [00:07<00:00, 71.33it/s, E=2000, R=8.55]
Iteration5: 100%|█████████████| 500/500 [00:06<00:00, 75.92it/s, E=2500, R=9.07]
Iteration6: 100%|█████████████| 500/500 [00:07<00:00, 71.41it/s, E=3000, R=9.45]
Iteration7: 100%|█████████████| 500/500 [00:07<00:00, 70.01it/s, E=3500, R=9.41]
Iteration8: 100%|█████████████| 500/500 [00:07<00:00, 71.11it/s, E=4000, R=8.88]
Iteration9: 100%|█████████████| 500/500 [00:06<00:00, 72.82it/s, E=4500, R=8.50]
Iteration10: 100%|████████████| 500/500 [00:07<00:00, 70.62it/s, E=5000, R=8.95]
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[28]:
gres.grids['Ery'] = t_ery.agent.critic(torch.tensor(t_ery.env.state_space.mean(axis=1),device=device)).detach().cpu().numpy().ravel()
gres = project_back(gres, 'Ery')
adata.obs['Ery'] = gres.embedding['Ery']
[215]:
gres1.grids['Ery'] = t_ery1.agent.critic(torch.tensor(t_ery1.env.state_space.mean(axis=1),device=device)).detach().cpu().numpy().ravel()
gres1 = project_back(gres1, 'Ery')
adata1.obs['Ery'] = gres1.embedding['Ery']
[33]:
sc.pl.umap(adata, color='Ery',s=50,title='',cmap='plasma',show=False)
plt.savefig('F7.png',dpi=600,bbox_inches='tight')
[216]:
sc.pl.umap(adata1, color='Ery',s=50,title='',cmap='plasma',show=False)
plt.savefig('F8.png',dpi=600,bbox_inches='tight')
[172]:
gres = lineage_rewards(gres, ['HSC_1','HSC_2'], ['Mono_1','Mono_2','DCs'], mode='Contribution')
Reward generating: 100%|██████████████████| 1163/1163 [00:00<00:00, 2446.68it/s]
Time used for generating rewards : 0.50 seconds
[218]:
gres1 = lineage_rewards(gres1, ['HSC','MPP'], ['GMP'], mode='Contribution')
Reward generating: 100%|██████████████████| 1699/1699 [00:00<00:00, 2497.06it/s]
Time used for generating rewards : 0.71 seconds
[173]:
t_mye = trainer('ActorCritic', gres, reward_mode='Contribution', X_latent=X_pca, num_episodes=5e3)
t_mye.train()
Iteration1: 100%|█████████████| 500/500 [00:03<00:00, 148.67it/s, E=500, R=1.50]
Iteration2: 100%|████████████| 500/500 [00:04<00:00, 103.11it/s, E=1000, R=2.98]
Iteration3: 100%|█████████████| 500/500 [00:05<00:00, 97.73it/s, E=1500, R=3.57]
Iteration4: 100%|█████████████| 500/500 [00:05<00:00, 87.66it/s, E=2000, R=4.13]
Iteration5: 100%|█████████████| 500/500 [00:06<00:00, 82.20it/s, E=2500, R=5.26]
Iteration6: 100%|█████████████| 500/500 [00:06<00:00, 79.24it/s, E=3000, R=5.24]
Iteration7: 100%|█████████████| 500/500 [00:06<00:00, 72.65it/s, E=3500, R=6.23]
Iteration8: 100%|█████████████| 500/500 [00:07<00:00, 70.50it/s, E=4000, R=5.86]
Iteration9: 100%|█████████████| 500/500 [00:07<00:00, 70.30it/s, E=4500, R=5.84]
Iteration10: 100%|████████████| 500/500 [00:07<00:00, 68.98it/s, E=5000, R=6.77]
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...])
[219]:
t_mye1 = trainer('ActorCritic', gres1, reward_mode='Contribution', X_latent=X_pca1, num_episodes=5e3)
t_mye1.train()
Iteration1: 100%|████████████| 500/500 [00:03<00:00, 144.45it/s, E=500, R=-0.26]
Iteration2: 100%|████████████| 500/500 [00:05<00:00, 90.86it/s, E=1000, R=-0.19]
Iteration3: 100%|████████████| 500/500 [00:06<00:00, 78.22it/s, E=1500, R=-0.07]
Iteration4: 100%|█████████████| 500/500 [00:06<00:00, 79.26it/s, E=2000, R=0.03]
Iteration5: 100%|████████████| 500/500 [00:06<00:00, 72.41it/s, E=2500, R=-0.10]
Iteration6: 100%|█████████████| 500/500 [00:07<00:00, 68.54it/s, E=3000, R=3.29]
Iteration7: 100%|█████████████| 500/500 [00:07<00:00, 64.99it/s, E=3500, R=6.40]
Iteration8: 100%|█████████████| 500/500 [00:08<00:00, 59.52it/s, E=4000, R=7.54]
Iteration9: 100%|█████████████| 500/500 [00:08<00:00, 59.63it/s, E=4500, R=9.65]
Iteration10: 100%|████████████| 500/500 [00:08<00:00, 61.59it/s, E=5000, R=9.12]
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...])
[174]:
gres.grids['Mye'] = t_mye.agent.critic(torch.tensor(t_mye.env.state_space.mean(axis=1),device=device)).detach().cpu().numpy().ravel()
gres = project_back(gres, 'Mye')
adata.obs['Mye'] = gres.embedding['Mye']
[220]:
gres1.grids['Mye'] = t_mye1.agent.critic(torch.tensor(t_mye1.env.state_space.mean(axis=1),device=device)).detach().cpu().numpy().ravel()
gres1 = project_back(gres1, 'Mye')
adata1.obs['Mye'] = gres1.embedding['Mye']
[175]:
sc.pl.umap(adata, color='Mye',s=50,title='',cmap='plasma',show=False)
plt.savefig('F9.png',dpi=600,bbox_inches='tight')
[221]:
sc.pl.umap(adata1, color='Mye',s=50,title='',cmap='plasma',show=False)
plt.savefig('F10.png',dpi=600,bbox_inches='tight')
[40]:
FateID = pd.read_csv('FateIDprobs.csv',header=0,index_col=0)
[44]:
adata.obs['Ery_fateid'] = FateID[['tEry_1','tEry_2','tMega']].mean(axis=1)
adata.obs['Mye_fateid'] = FateID[['tMono_1','tMono_2','tDCs']].mean(axis=1)
[193]:
FateID1 = pd.read_csv('../../../pheno_FateIDprobs.csv',header=0,index_col=0)
[207]:
adata1.obs['Ery_fateid'] = FateID1['tMEP'].values
adata1.obs['Mye_fateid'] = FateID1['tGMP'].values
[201]:
adata1.obs['lin1'] = pr_res1.branch_probs.iloc[:,0]
adata1.obs['lin2'] = pr_res1.branch_probs.iloc[:,1]
adata1.obs['lin3'] = pr_res1.branch_probs.iloc[:,2]
adata1.obs['lin4'] = pr_res1.branch_probs.iloc[:,3]
adata1.obs['lin5'] = pr_res1.branch_probs.iloc[:,4]
[209]:
adata1.obs['palantir_Ery'] = pr_res1.branch_probs.iloc[:,0]
adata1.obs['palantir_Mye'] = pr_res1.branch_probs.iloc[:,[1,3]].mean(axis=1)
[222]:
sc.pl.umap(adata1, color=['palantir_Ery','palantir_Mye','Ery_fateid','Mye_fateid'])
[ ]:
[49]:
for i in range(adata.obsm['palantir_branch_probs'].shape[1]):
adata.obs[f'palantir_lin{i}'] = adata.obsm['palantir_branch_probs'][:,i]
[50]:
sc.pl.umap(adata, color=[f'palantir_lin{i}' for i in range(6)])
[51]:
adata.obs['palantir_Ery'] = adata.obsm['palantir_branch_probs'][:,[1,3]].mean(axis=1)
adata.obs['palantir_Mye'] = adata.obsm['palantir_branch_probs'][:,[0,2,5]].mean(axis=1)
[54]:
sc.pl.umap(adata, color=['palantir_Ery','Ery_fateid','palantir_Mye','Mye_fateid'])
[59]:
adata.obs['umap_dis'] = np.linalg.norm(adata.obsm['X_umap'][adata.uns['iroot'],:]-adata.obsm['X_umap'], axis=1)
adata1.obs['umap_dis'] = np.linalg.norm(adata1.obsm['X_umap'][adata1.uns['iroot'],:]-adata1.obsm['X_umap'], axis=1)
[61]:
adata.obs[['umap_dis','Ery','palantir_Ery','Ery_fateid']].corr()
[61]:
| umap_dis | Ery | palantir_Ery | Ery_fateid | |
|---|---|---|---|---|
| umap_dis | 1.000000 | 0.098318 | -0.228703 | -0.565418 |
| Ery | 0.098318 | 1.000000 | 0.903516 | 0.683332 |
| palantir_Ery | -0.228703 | 0.903516 | 1.000000 | 0.858363 |
| Ery_fateid | -0.565418 | 0.683332 | 0.858363 | 1.000000 |
[62]:
adata.obs[['umap_dis','Ery','palantir_Ery','Ery_fateid']].corr(method='spearman')
[62]:
| umap_dis | Ery | palantir_Ery | Ery_fateid | |
|---|---|---|---|---|
| umap_dis | 1.000000 | 0.217304 | -0.598203 | -0.599813 |
| Ery | 0.217304 | 1.000000 | 0.370568 | 0.423275 |
| palantir_Ery | -0.598203 | 0.370568 | 1.000000 | 0.940198 |
| Ery_fateid | -0.599813 | 0.423275 | 0.940198 | 1.000000 |
[63]:
adata.obs[['umap_dis','Ery','palantir_Ery','Ery_fateid']].corr(method='kendall')
[63]:
| umap_dis | Ery | palantir_Ery | Ery_fateid | |
|---|---|---|---|---|
| umap_dis | 1.000000 | 0.177310 | -0.450463 | -0.465661 |
| Ery | 0.177310 | 1.000000 | 0.242150 | 0.290237 |
| palantir_Ery | -0.450463 | 0.242150 | 1.000000 | 0.796544 |
| Ery_fateid | -0.465661 | 0.290237 | 0.796544 | 1.000000 |
[176]:
adata.obs[['umap_dis','Mye','palantir_Mye','Mye_fateid']].corr()
[176]:
| umap_dis | Mye | palantir_Mye | Mye_fateid | |
|---|---|---|---|---|
| umap_dis | 1.000000 | 0.489865 | -0.241363 | 0.168013 |
| Mye | 0.489865 | 1.000000 | 0.517934 | 0.792049 |
| palantir_Mye | -0.241363 | 0.517934 | 1.000000 | 0.837977 |
| Mye_fateid | 0.168013 | 0.792049 | 0.837977 | 1.000000 |
[177]:
adata.obs[['umap_dis','Mye','palantir_Mye','Mye_fateid']].corr(method='spearman')
[177]:
| umap_dis | Mye | palantir_Mye | Mye_fateid | |
|---|---|---|---|---|
| umap_dis | 1.000000 | 0.722406 | 0.148283 | 0.172018 |
| Mye | 0.722406 | 1.000000 | 0.617483 | 0.660454 |
| palantir_Mye | 0.148283 | 0.617483 | 1.000000 | 0.955802 |
| Mye_fateid | 0.172018 | 0.660454 | 0.955802 | 1.000000 |
[178]:
adata.obs[['umap_dis','Mye','palantir_Mye','Mye_fateid']].corr(method='kendall')
[178]:
| umap_dis | Mye | palantir_Mye | Mye_fateid | |
|---|---|---|---|---|
| umap_dis | 1.000000 | 0.569801 | 0.169206 | 0.188116 |
| Mye | 0.569801 | 1.000000 | 0.403333 | 0.472620 |
| palantir_Mye | 0.169206 | 0.403333 | 1.000000 | 0.829449 |
| Mye_fateid | 0.188116 | 0.472620 | 0.829449 | 1.000000 |
[ ]:
[ ]:
[223]:
adata1.obs[['umap_dis','Ery','palantir_Ery','Ery_fateid']].corr()
[223]:
| umap_dis | Ery | palantir_Ery | Ery_fateid | |
|---|---|---|---|---|
| umap_dis | 1.000000 | 0.277291 | -0.241954 | -0.606381 |
| Ery | 0.277291 | 1.000000 | 0.368041 | 0.428587 |
| palantir_Ery | -0.241954 | 0.368041 | 1.000000 | 0.567192 |
| Ery_fateid | -0.606381 | 0.428587 | 0.567192 | 1.000000 |
[224]:
adata1.obs[['umap_dis','Ery','palantir_Ery','Ery_fateid']].corr(method='spearman')
[224]:
| umap_dis | Ery | palantir_Ery | Ery_fateid | |
|---|---|---|---|---|
| umap_dis | 1.000000 | 0.317487 | 0.062320 | -0.436132 |
| Ery | 0.317487 | 1.000000 | 0.551669 | 0.409955 |
| palantir_Ery | 0.062320 | 0.551669 | 1.000000 | 0.526824 |
| Ery_fateid | -0.436132 | 0.409955 | 0.526824 | 1.000000 |
[225]:
adata1.obs[['umap_dis','Ery','palantir_Ery','Ery_fateid']].corr(method='kendall')
[225]:
| umap_dis | Ery | palantir_Ery | Ery_fateid | |
|---|---|---|---|---|
| umap_dis | 1.000000 | 0.246639 | 0.140031 | -0.330621 |
| Ery | 0.246639 | 1.000000 | 0.395373 | 0.281934 |
| palantir_Ery | 0.140031 | 0.395373 | 1.000000 | 0.375852 |
| Ery_fateid | -0.330621 | 0.281934 | 0.375852 | 1.000000 |
[226]:
adata1.obs[['umap_dis','Mye','palantir_Mye','Mye_fateid']].corr()
[226]:
| umap_dis | Mye | palantir_Mye | Mye_fateid | |
|---|---|---|---|---|
| umap_dis | 1.000000 | 0.621515 | 0.352129 | 0.606381 |
| Mye | 0.621515 | 1.000000 | 0.419072 | 0.882633 |
| palantir_Mye | 0.352129 | 0.419072 | 1.000000 | 0.333034 |
| Mye_fateid | 0.606381 | 0.882633 | 0.333034 | 1.000000 |
[227]:
adata1.obs[['umap_dis','Mye','palantir_Mye','Mye_fateid']].corr(method='spearman')
[227]:
| umap_dis | Mye | palantir_Mye | Mye_fateid | |
|---|---|---|---|---|
| umap_dis | 1.000000 | 0.690151 | -0.153834 | 0.436132 |
| Mye | 0.690151 | 1.000000 | 0.159101 | 0.801490 |
| palantir_Mye | -0.153834 | 0.159101 | 1.000000 | 0.259570 |
| Mye_fateid | 0.436132 | 0.801490 | 0.259570 | 1.000000 |
[228]:
adata1.obs[['umap_dis','Mye','palantir_Mye','Mye_fateid']].corr(method='kendall')
[228]:
| umap_dis | Mye | palantir_Mye | Mye_fateid | |
|---|---|---|---|---|
| umap_dis | 1.000000 | 0.500853 | -0.158823 | 0.330621 |
| Mye | 0.500853 | 1.000000 | 0.083739 | 0.608485 |
| palantir_Mye | -0.158823 | 0.083739 | 1.000000 | 0.226775 |
| Mye_fateid | 0.330621 | 0.608485 | 0.226775 | 1.000000 |
[ ]:
[67]:
gres = lineage_rewards(gres, ['HSC_1','HSC_2'], ['Ery_1','Ery_2','Mega'])
gres1 = lineage_rewards(gres1, ['HSC','MPP'], ['MEP'])
[69]:
t_ery = trainer('ActorCritic', gres, X_latent=X_pca, num_episodes=5e3, gamma=.9)
t_ery.train()
t_ery1 = trainer('ActorCritic', gres1, X_latent=X_pca1, num_episodes=5e3, gamma=.9)
t_ery1.train()
Iteration1: 100%|█████████████| 500/500 [00:03<00:00, 153.48it/s, E=500, R=4.76]
Iteration2: 100%|████████████| 500/500 [00:05<00:00, 86.53it/s, E=1000, R=11.18]
Iteration3: 100%|████████████| 500/500 [00:06<00:00, 72.41it/s, E=1500, R=12.58]
Iteration4: 100%|████████████| 500/500 [00:07<00:00, 68.07it/s, E=2000, R=13.28]
Iteration5: 100%|████████████| 500/500 [00:07<00:00, 65.16it/s, E=2500, R=14.67]
Iteration6: 100%|████████████| 500/500 [00:07<00:00, 66.18it/s, E=3000, R=13.91]
Iteration7: 100%|████████████| 500/500 [00:07<00:00, 65.32it/s, E=3500, R=15.17]
Iteration8: 100%|████████████| 500/500 [00:07<00:00, 65.62it/s, E=4000, R=12.90]
Iteration9: 100%|████████████| 500/500 [00:08<00:00, 62.25it/s, E=4500, R=14.74]
Iteration10: 100%|███████████| 500/500 [00:07<00:00, 64.17it/s, E=5000, R=14.94]
Iteration1: 100%|█████████████| 500/500 [00:03<00:00, 162.78it/s, E=500, R=5.32]
Iteration2: 100%|█████████████| 500/500 [00:06<00:00, 80.72it/s, E=1000, R=8.59]
Iteration3: 100%|█████████████| 500/500 [00:06<00:00, 71.90it/s, E=1500, R=9.66]
Iteration4: 100%|█████████████| 500/500 [00:06<00:00, 73.35it/s, E=2000, R=9.99]
Iteration5: 100%|████████████| 500/500 [00:06<00:00, 75.14it/s, E=2500, R=10.14]
Iteration6: 100%|████████████| 500/500 [00:06<00:00, 76.24it/s, E=3000, R=10.35]
Iteration7: 100%|█████████████| 500/500 [00:06<00:00, 77.46it/s, E=3500, R=9.87]
Iteration8: 100%|█████████████| 500/500 [00:06<00:00, 75.94it/s, E=4000, R=9.66]
Iteration9: 100%|█████████████| 500/500 [00:06<00:00, 76.48it/s, E=4500, R=9.61]
Iteration10: 100%|███████████| 500/500 [00:06<00:00, 78.70it/s, E=5000, R=10.25]
[69]:
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[70]:
gres.grids['Ery'] = t_ery.agent.critic(torch.tensor(t_ery.env.state_space.mean(axis=1),device=device)).detach().cpu().numpy().ravel()
gres = project_back(gres, 'Ery')
adata.obs['Ery_decision'] = gres.embedding['Ery']
gres1.grids['Ery'] = t_ery1.agent.critic(torch.tensor(t_ery1.env.state_space.mean(axis=1),device=device)).detach().cpu().numpy().ravel()
gres1 = project_back(gres1, 'Ery')
adata1.obs['Ery_decision'] = gres1.embedding['Ery']
[71]:
sc.pl.umap(adata, color='Ery_decision',s=50,title='',cmap='plasma',show=False)
plt.savefig('F11.png',dpi=600,bbox_inches='tight')
[72]:
sc.pl.umap(adata1, color='Ery_decision',s=50,title='',cmap='plasma',show=False)
plt.savefig('F12.png',dpi=600,bbox_inches='tight')
[73]:
gres = lineage_rewards(gres, ['HSC_1','HSC_2'], ['Mono_1','Mono_2','DCs'])
gres1 = lineage_rewards(gres1, ['HSC','MPP'], ['GMP'])
t_mye = trainer('ActorCritic', gres, X_latent=X_pca, num_episodes=5e3, gamma=.9)
t_mye.train()
t_mye1 = trainer('ActorCritic', gres1, X_latent=X_pca1, num_episodes=5e3, gamma=.9)
t_mye1.train()
Reward generating: 100%|██████████████████| 1163/1163 [00:00<00:00, 2470.96it/s]
Time used for generating rewards : 0.49 seconds
Reward generating: 100%|██████████████████| 1699/1699 [00:00<00:00, 2625.32it/s]
Time used for generating rewards : 0.67 seconds
Iteration1: 100%|█████████████| 500/500 [00:03<00:00, 159.38it/s, E=500, R=3.23]
Iteration2: 100%|█████████████| 500/500 [00:06<00:00, 76.31it/s, E=1000, R=8.64]
Iteration3: 100%|████████████| 500/500 [00:07<00:00, 68.28it/s, E=1500, R=10.28]
Iteration4: 100%|████████████| 500/500 [00:06<00:00, 71.86it/s, E=2000, R=11.08]
Iteration5: 100%|████████████| 500/500 [00:07<00:00, 70.56it/s, E=2500, R=11.60]
Iteration6: 100%|████████████| 500/500 [00:07<00:00, 69.27it/s, E=3000, R=12.57]
Iteration7: 100%|████████████| 500/500 [00:07<00:00, 66.82it/s, E=3500, R=11.58]
Iteration8: 100%|████████████| 500/500 [00:07<00:00, 68.53it/s, E=4000, R=11.39]
Iteration9: 100%|████████████| 500/500 [00:07<00:00, 66.04it/s, E=4500, R=13.36]
Iteration10: 100%|███████████| 500/500 [00:07<00:00, 67.98it/s, E=5000, R=11.50]
Iteration1: 100%|████████████| 500/500 [00:02<00:00, 178.17it/s, E=500, R=-0.10]
Iteration2: 100%|█████████████| 500/500 [00:06<00:00, 72.42it/s, E=1000, R=6.24]
Iteration3: 100%|█████████████| 500/500 [00:09<00:00, 52.67it/s, E=1500, R=9.14]
Iteration4: 100%|█████████████| 500/500 [00:09<00:00, 51.60it/s, E=2000, R=9.37]
Iteration5: 100%|████████████| 500/500 [00:09<00:00, 55.07it/s, E=2500, R=10.00]
Iteration6: 100%|█████████████| 500/500 [00:09<00:00, 55.47it/s, E=3000, R=9.67]
Iteration7: 100%|████████████| 500/500 [00:09<00:00, 54.89it/s, E=3500, R=10.26]
Iteration8: 100%|████████████| 500/500 [00:09<00:00, 51.99it/s, E=4000, R=10.16]
Iteration9: 100%|████████████| 500/500 [00:09<00:00, 50.83it/s, E=4500, R=10.89]
Iteration10: 100%|███████████| 500/500 [00:09<00:00, 52.04it/s, E=5000, R=10.25]
[73]:
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[78]:
gres.grids['Mye'] = t_mye.agent.critic(torch.tensor(t_mye.env.state_space.mean(axis=1),device=device)).detach().cpu().numpy().ravel()
gres = project_back(gres, 'Mye')
adata.obs['Mye_decision'] = gres.embedding['Mye']
gres1.grids['Mye'] = t_mye1.agent.critic(torch.tensor(t_mye1.env.state_space.mean(axis=1),device=device)).detach().cpu().numpy().ravel()
gres1 = project_back(gres1, 'Mye')
adata1.obs['Mye_decision'] = gres1.embedding['Mye']
[79]:
sc.pl.umap(adata, color='Mye_decision',s=50,title='',cmap='plasma',show=False)
plt.savefig('F13.png',dpi=600,bbox_inches='tight')
[80]:
sc.pl.umap(adata1, color='Mye_decision',s=50,title='',cmap='plasma',show=False)
plt.savefig('F14.png',dpi=600,bbox_inches='tight')
[98]:
from sklearn.preprocessing import KBinsDiscretizer, minmax_scale
[164]:
ery_df = adata.obs.loc[adata.obs['clusters'].isin(['HSC_1','HSC_2','Ery_1','Ery_2','Mega']),['Ery','Ery_decision','time']]
ery_df['time_bins'] = KBinsDiscretizer(20, encode='ordinal', strategy='quantile').fit_transform(ery_df['time'].values.reshape(-1,1))
[165]:
with sns.axes_style('darkgrid'):
sns.boxplot(data=ery_df,x='time_bins',y='Ery_decision',palette='viridis',fliersize=0,whis=(10,90))
sns.stripplot(data=ery_df, x='time_bins', y='Ery_decision',color='k',s=1,edgecolor='w')
ax = plt.gca()
ax.tick_params(labelbottom=False)
ax.set_xlabel('')
ax.set_ylabel('')
plt.savefig('F15.png',dpi=600,bbox_inches='tight')
[166]:
with sns.axes_style('darkgrid'):
sns.boxplot(data=ery_df,x='time_bins',y='Ery',palette='viridis',fliersize=0,whis=(10,90))
sns.stripplot(data=ery_df, x='time_bins', y='Ery',color='k',s=1,edgecolor='w')
ax = plt.gca()
ax.tick_params(labelbottom=False)
ax.set_xlabel('')
ax.set_ylabel('')
plt.savefig('F16.png',dpi=600,bbox_inches='tight')
[179]:
mye_df = adata.obs.loc[adata.obs['clusters'].isin(['HSC_1','HSC_2','Mono_1','Mono_2','DCs']),['Mye','Mye_decision','time']]
mye_df['time_bins'] = KBinsDiscretizer(20, encode='ordinal', strategy='quantile').fit_transform(mye_df['time'].values.reshape(-1,1))
[180]:
with sns.axes_style('darkgrid'):
sns.boxplot(data=mye_df,x='time_bins',y='Mye_decision',palette='viridis',fliersize=0,whis=(10,90))
sns.stripplot(data=mye_df, x='time_bins', y='Mye_decision',color='k',s=1,edgecolor='w')
ax = plt.gca()
ax.tick_params(labelbottom=False)
ax.set_xlabel('')
ax.set_ylabel('')
plt.savefig('F17.png',dpi=600,bbox_inches='tight')
[188]:
with sns.axes_style('darkgrid'):
sns.boxplot(data=mye_df,x='time_bins',y='Mye',palette='viridis',fliersize=0,whis=(10,90))
sns.stripplot(data=mye_df, x='time_bins', y='Mye',color='k',s=1,edgecolor='w')
ax = plt.gca()
ax.tick_params(labelbottom=False)
ax.set_xlabel('')
ax.set_ylabel('')
plt.savefig('F18.png',dpi=600,bbox_inches='tight')
[191]:
ery_weighted = sum((ery_df['Ery'] / ery_df['Ery'].sum())*ery_df['time'])
eryD_weighted = sum((ery_df['Ery_decision'] / ery_df['Ery_decision'].sum())*ery_df['time'])
with sns.axes_style('darkgrid'):
fig = plt.figure(figsize=(3,5))
sns.barplot(x=[0,1],y=[eryD_weighted, ery_weighted],palette='viridis')
ax = plt.gca()
ax.set_xticklabels(['Desion','Contribution'])
plt.savefig('F19.png',dpi=600,bbox_inches='tight')
[192]:
mye_weighted = sum((mye_df['Mye'] / mye_df['Mye'].sum())*mye_df['time'])
myeD_weighted = sum((mye_df['Mye_decision'] / mye_df['Mye_decision'].sum())*mye_df['time'])
with sns.axes_style('darkgrid'):
fig = plt.figure(figsize=(3,5))
sns.barplot(x=[0,1],y=[myeD_weighted, mye_weighted],palette='viridis')
ax = plt.gca()
ax.set_xticklabels(['Desion','Contribution'])
plt.savefig('F20.png',dpi=600,bbox_inches='tight')
[ ]:
[231]:
ery_df1 = adata1.obs.loc[adata1.obs['batch'].isin(['HSC','MPP','MEP']),['Ery','Ery_decision','time']]
ery_df1['time_bins'] = KBinsDiscretizer(20, encode='ordinal', strategy='quantile').fit_transform(ery_df1['time'].values.reshape(-1,1))
[235]:
with sns.axes_style('darkgrid'):
sns.boxplot(data=ery_df1,x='time_bins',y='Ery_decision',palette='viridis',fliersize=0,whis=(10,90))
sns.stripplot(data=ery_df1, x='time_bins', y='Ery_decision',color='k',s=1,edgecolor='w')
ax = plt.gca()
ax.tick_params(labelbottom=False)
ax.set_xlabel('')
ax.set_ylabel('')
plt.savefig('F21.png',dpi=600,bbox_inches='tight')
[236]:
with sns.axes_style('darkgrid'):
sns.boxplot(data=ery_df1,x='time_bins',y='Ery',palette='viridis',fliersize=0,whis=(10,90))
sns.stripplot(data=ery_df1, x='time_bins', y='Ery',color='k',s=1,edgecolor='w')
ax = plt.gca()
ax.tick_params(labelbottom=False)
ax.set_xlabel('')
ax.set_ylabel('')
plt.savefig('F22.png',dpi=600,bbox_inches='tight')
[237]:
ery_weighted1 = sum((ery_df1['Ery'] / ery_df1['Ery'].sum())*ery_df1['time'])
eryD_weighted1 = sum((ery_df1['Ery_decision'] / ery_df1['Ery_decision'].sum())*ery_df1['time'])
with sns.axes_style('darkgrid'):
fig = plt.figure(figsize=(3,5))
sns.barplot(x=[0,1],y=[eryD_weighted1, ery_weighted1],palette='viridis')
ax = plt.gca()
ax.set_xticklabels(['Desion','Contribution'])
plt.savefig('F23.png',dpi=600,bbox_inches='tight')
[238]:
mye_df1 = adata1.obs.loc[adata1.obs['batch'].isin(['HSC','MPP','GMP']),['Mye','Mye_decision','time']]
mye_df1['time_bins'] = KBinsDiscretizer(20, encode='ordinal', strategy='quantile').fit_transform(mye_df1['time'].values.reshape(-1,1))
[239]:
with sns.axes_style('darkgrid'):
sns.boxplot(data=mye_df1,x='time_bins',y='Mye_decision',palette='viridis',fliersize=0,whis=(10,90))
sns.stripplot(data=mye_df1, x='time_bins', y='Mye_decision',color='k',s=1,edgecolor='w')
ax = plt.gca()
ax.tick_params(labelbottom=False)
ax.set_xlabel('')
ax.set_ylabel('')
plt.savefig('F24.png',dpi=600,bbox_inches='tight')
[240]:
with sns.axes_style('darkgrid'):
sns.boxplot(data=mye_df1,x='time_bins',y='Mye',palette='viridis',fliersize=0,whis=(10,90))
sns.stripplot(data=mye_df1, x='time_bins', y='Mye',color='k',s=1,edgecolor='w')
ax = plt.gca()
ax.tick_params(labelbottom=False)
ax.set_xlabel('')
ax.set_ylabel('')
plt.savefig('F25.png',dpi=600,bbox_inches='tight')
[241]:
mye_weighted1 = sum((mye_df1['Mye'] / mye_df1['Mye'].sum())*mye_df1['time'])
myeD_weighted1 = sum((mye_df1['Mye_decision'] / mye_df1['Mye_decision'].sum())*mye_df1['time'])
with sns.axes_style('darkgrid'):
fig = plt.figure(figsize=(3,5))
sns.barplot(x=[0,1],y=[myeD_weighted1, mye_weighted1],palette='viridis')
ax = plt.gca()
ax.set_xticklabels(['Desion','Contribution'])
plt.savefig('F26.png',dpi=600,bbox_inches='tight')
[242]:
adata.obs.to_csv('0326_cd34.csv')
[243]:
adata1.obs.to_csv('0326_pheno.csv')
[ ]: