Timing-associated genes analysis with TimeFateKernel¶
In our single-cell analysis, we analyse the underlying temporal state in the cell, which we call pseudotime. and identifying the genes associated with pseudotime becomes the key to unravelling models of gene dynamic regulation. In traditional analysis, we would use correlation coefficients, or gene dynamics model fitting. The correlation coefficient approach will have a preference for genes at the beginning and end of the time series, and the gene dynamics model requires RNA velocity information. Unbiased identification of chronosequence-related genes, as well as the need for no additional dependency information, has become a challenge in current chronosequence analyses.
Here, we developed TimeFateKernel, which first removes potential noise from the data through metacells, and then constructs an adaptive ridge regression model to find the minimum set of genes needed to satisfy the timing fit.CellFateGenie has similar accuracy to gene dynamics models while eliminating preferences for the start and end of the time series.
Colab_Reproducibility：https://colab.research.google.com/drive/1Q1Sk5lGCBGBWS5Bs2kncAq9ZbjaDzSR4?usp=sharing
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import omicverse as ov
import scvelo as scv
import matplotlib.pyplot as plt
ov.ov_plot_set()
import omicverse as ov
import scvelo as scv
import matplotlib.pyplot as plt
ov.ov_plot_set()
2024-12-18 03:17:29.104875: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-12-18 03:17:29.117954: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-12-18 03:17:29.121936: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered 2024-12-18 03:17:29.132033: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2024-12-18 03:17:29.929419: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
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Version: 1.6.9, Tutorials: https://omicverse.readthedocs.io/
Dependency error: (pydeseq2 0.4.11 (/mnt/home/zehuazeng/software/rsc/lib/python3.10/site-packages), Requirement.parse('pydeseq2<=0.4.0,>=0.3'))
Data preprocessed¶
We using dataset of dentategyrus in scvelo to demonstrate the timing-associated genes analysis. Firstly, We use ov.pp.qc and ov.pp.preprocess to preprocess the dataset.
Then we use ov.pp.scale and ov.pp.pca to analysis the principal component of the data
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import cellrank as cr
ad_url = "https://fh-pi-setty-m-eco-public.s3.amazonaws.com/mellon-tutorial/preprocessed_t-cell-depleted-bm-rna.h5ad"
adata = ov.read("data/preprocessed_t-cell-depleted-bm-rna.h5ad", backup_url=ad_url)
adata
import cellrank as cr
ad_url = "https://fh-pi-setty-m-eco-public.s3.amazonaws.com/mellon-tutorial/preprocessed_t-cell-depleted-bm-rna.h5ad"
adata = ov.read("data/preprocessed_t-cell-depleted-bm-rna.h5ad", backup_url=ad_url)
adata
We need to check if the data has been normalized and logarithmized, we find that the maximum value is 13, then the data has been logarithmized.
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adata.X.max()
adata.X.max()
Out[58]:
12.226059
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ov.pl.embedding(adata,
basis="X_umap",
color=['celltype','palantir_pseudotime'],
title=['','Pseudotime'],
size=15,
show=False, #legend_loc=None, add_outline=False,
frameon='small',legend_fontoutline=2,#ax=ax
)
ov.pl.embedding(adata,
basis="X_umap",
color=['celltype','palantir_pseudotime'],
title=['','Pseudotime'],
size=15,
show=False, #legend_loc=None, add_outline=False,
frameon='small',legend_fontoutline=2,#ax=ax
)
/mnt/home/zehuazeng/software/rsc/lib/python3.10/site-packages/omicverse/utils/_scatterplot.py:430: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored cax = scatter(
Out[57]:
[<AxesSubplot: xlabel='X_umap1', ylabel='X_umap2'>,
<AxesSubplot: title={'center': 'Pseudotime'}, xlabel='X_umap1', ylabel='X_umap2'>]
Initialize the timing model¶
In TimeFateKernel, we only need to specify the pseudotime parameter to automatically fit genes that contribute to pseudotime.
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cfg_obj=ov.single.Fate(adata,pseudotime='palantir_pseudotime')
cfg_obj.model_init()
cfg_obj.ATR(stop=500)
cfg_obj=ov.single.Fate(adata,pseudotime='palantir_pseudotime')
cfg_obj.model_init()
cfg_obj.ATR(stop=500)
/mnt/home/zehuazeng/software/rsc/lib/python3.10/site-packages/sklearn/linear_model/_ridge.py:243: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead. warnings.warn(
$MSE|RMSE|MAE|R^2$:0.0024|0.049|0.037|0.95
3%|▎ | 499/17225 [01:53<1:03:35, 4.38it/s]
coef_threshold:0.0009673292515799403, r2:0.9458897232697306
Out[59]:
| coef_threshold | r2 | |
|---|---|---|
| 0 | 0.002444 | 0.661107 |
| 1 | 0.002407 | 0.728704 |
| 2 | 0.002259 | 0.752481 |
| 3 | 0.002137 | 0.783143 |
| 4 | 0.002099 | 0.786696 |
| ... | ... | ... |
| 495 | 0.000610 | 0.964707 |
| 496 | 0.000610 | 0.964685 |
| 497 | 0.000610 | 0.964671 |
| 498 | 0.000609 | 0.964718 |
| 499 | 0.000608 | 0.964769 |
500 rows × 2 columns
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#If the function `plot_filtering` reports an error, specify the threshold manually.
#cfg_obj.coef_threshold=0.001
#If the function `plot_filtering` reports an error, specify the threshold manually.
#cfg_obj.coef_threshold=0.001
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fig,ax=cfg_obj.plot_filtering(color='#5ca8dc')
ax.set_title('HSC\nTimeFateKernel')
fig,ax=cfg_obj.plot_filtering(color='#5ca8dc')
ax.set_title('HSC\nTimeFateKernel')
Out[60]:
Text(0.5, 1.0, 'HSC\nTimeFateKernel')
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res=cfg_obj.model_fit()
res=cfg_obj.model_fit()
$MSE|RMSE|MAE|R^2$:0.003|0.055|0.039|0.94
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cfg_obj.plot_fitting(type='raw')
cfg_obj.plot_fitting(type='raw')
Out[62]:
(<Figure size 240x240 with 1 Axes>,
<AxesSubplot: title={'center': 'Dimension: 17226'}, xlabel='Raw', ylabel='Predicted'>)
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fig,ax=cfg_obj.plot_fitting(type='filter',
color='#e25d5d')
fig,ax=cfg_obj.plot_fitting(type='filter',
color='#e25d5d')
We can find that after filtering by an automatic threshold gate, only 76 genes are considered to be associated with pseudotime
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cfg_obj.filter_coef.head()
cfg_obj.filter_coef.head()
Out[64]:
| coef | abs(coef) | values | |
|---|---|---|---|
| ARHGAP42 | 0.009992 | 0.009992 | 0.329918 |
| GNAQ | -0.009275 | 0.009275 | 4.815562 |
| FCRL1 | 0.009218 | 0.009218 | 0.754004 |
| MSRB3 | -0.008434 | 0.008434 | 0.593459 |
| DANT2 | -0.008101 | 0.008101 | 0.316569 |
Time-Series Gene Heatmap Visualization¶
Here, we presented plot_heatmap to visualize the Time-Series Gene Heatmap Visualization
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var_name=cfg_obj.filter_coef.index.tolist()
g=ov.utils.plot_heatmap(adata,var_names=var_name,
sortby='palantir_pseudotime',
col_color='celltype',
n_convolve=1000,figsize=(1,6),show=False,)
g.fig.set_size_inches(2, 8)
g.fig.suptitle('TimeFateKernel',x=0.25,y=0.83,
horizontalalignment='left',
fontsize=12,fontweight='bold')
g.ax_heatmap.set_yticklabels(g.ax_heatmap.get_yticklabels(),
fontsize=12)
plt.show()
var_name=cfg_obj.filter_coef.index.tolist()
g=ov.utils.plot_heatmap(adata,var_names=var_name,
sortby='palantir_pseudotime',
col_color='celltype',
n_convolve=1000,figsize=(1,6),show=False,)
g.fig.set_size_inches(2, 8)
g.fig.suptitle('TimeFateKernel',x=0.25,y=0.83,
horizontalalignment='left',
fontsize=12,fontweight='bold')
g.ax_heatmap.set_yticklabels(g.ax_heatmap.get_yticklabels(),
fontsize=12)
plt.show()
/mnt/home/zehuazeng/software/rsc/lib/python3.10/site-packages/seaborn/matrix.py:1107: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. self._figure.tight_layout(**tight_params)
Density of Cells calculated¶
In this step, we will compute cell-state density using Mellon’s DensityEstimator class. Diffusion components computed above serve as inputs.
The compute densities, log_density can be visualized using UMAPs. We recommend the visualization of clipped log density. This procedure, which trims the very low density of outlier cells to the lower 5% percentile, provides richer visualization in 2D embeddings such as UMAPs.
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cfg_obj.low_density(pca_key='X_pca')
cfg_obj.low_density(pca_key='X_pca')
computing neighbors
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:22)
[2024-12-18 03:46:57,981] [INFO ] Using sparse Gaussian Process since n_landmarks (5,000) < n_samples (8,627) and rank = 1.0.
[2024-12-18 03:46:57,983] [INFO ] Computing nearest neighbor distances.
[2024-12-18 03:46:58,685] [INFO ] Using d=1.7352303437057968.
[2024-12-18 03:46:58,801] [INFO ] Using covariance function Matern52(ls=0.0010437683199839192).
[2024-12-18 03:46:58,802] [INFO ] Computing 5,000 landmarks with k-means clustering.
[2024-12-18 03:47:11,537] [INFO ] Using rank 5,000 covariance representation.
[2024-12-18 03:47:14,139] [INFO ] Running inference using L-BFGS-B.
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ov.pl.embedding(adata,
basis='X_umap',
color=['mellon_log_density_lowd','celltype'],
frameon='small',
cmap='RdBu_r')
ov.pl.embedding(adata,
basis='X_umap',
color=['mellon_log_density_lowd','celltype'],
frameon='small',
cmap='RdBu_r')
/mnt/home/zehuazeng/software/rsc/lib/python3.10/site-packages/omicverse/utils/_scatterplot.py:430: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored cax = scatter(
Fate Gene of B lineages¶
We selected PreB as the object of study and first used leiden clustering to obtain the differentiation categories of potential PreB
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#ov.pp.neighbors(adata,use_rep='X_pca',
# n_neighbors=15,n_pcs=30)
ov.pp.leiden(adata,resolution=2)
#ov.pp.neighbors(adata,use_rep='X_pca',
# n_neighbors=15,n_pcs=30)
ov.pp.leiden(adata,resolution=2)
running Leiden clustering
finished: found 24 clusters and added
'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
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ov.pl.embedding(adata,
basis="X_umap",
color=['leiden'],title='',#size=15,
show=False, #legend_loc=None, add_outline=False,
frameon='small',
legend_fontoutline=2,#ax=ax
)
ov.pl.embedding(adata,
basis="X_umap",
color=['leiden'],title='',#size=15,
show=False, #legend_loc=None, add_outline=False,
frameon='small',
legend_fontoutline=2,#ax=ax
)
/mnt/home/zehuazeng/software/rsc/lib/python3.10/site-packages/omicverse/utils/_scatterplot.py:430: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored cax = scatter(
Out[69]:
<AxesSubplot: xlabel='X_umap1', ylabel='X_umap2'>
Local variability or local change in expression¶
Local variability provides a measure of gene expression change for each cell-state. This is determined by comparison of a gene in a cell to its neighbor cell-states and can be computed using palantir.utils.run_local_variability
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cfg_obj.lineage_score(cluster_key='leiden',lineage=['20','17'],
expression_key='MAGIC_imputed_data')
#palantir,mellon
cfg_obj.lineage_score(cluster_key='leiden',lineage=['20','17'],
expression_key='MAGIC_imputed_data')
#palantir,mellon
Run low_density first
100%|██████████| 8627/8627 [00:08<00:00, 1017.48it/s]
Calculating lineage score The lineage score stored in adata.var['change_scores_lineage']
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scores = adata.var["change_scores_lineage"]
scores.sort_values(ascending=False)
scores = adata.var["change_scores_lineage"]
scores.sort_values(ascending=False)
Out[74]:
EBF1 0.001614
DIAPH3 0.001494
MIR924HG 0.001401
AL589693.1 0.001341
ATP8B4 0.001338
...
AP001528.1 0.000000
AC110741.1 0.000000
KCNE1B 0.000000
CXCL1 0.000000
AC104809.2 0.000000
Name: change_scores_lineage, Length: 17226, dtype: float64
Fate Genes of B linages¶
We take the intersection of temporally related genes with locally variant genes to obtain the genes that drive PreB differentiation.
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preb_genes=scores.loc[cfg_obj.filter_coef.index].sort_values(ascending=False)[:20]
preb_genes
preb_genes=scores.loc[cfg_obj.filter_coef.index].sort_values(ascending=False)[:20]
preb_genes
Out[76]:
EBF1 0.001614 ATP8B4 0.001338 ERG 0.001142 LRMDA 0.000985 SLC8A1 0.000791 NKAIN2 0.000781 PAX5 0.000736 RNF220 0.000677 PLXDC2 0.000672 CASC15 0.000640 SEL1L3 0.000539 SETBP1 0.000530 EZH2 0.000516 BLNK 0.000439 MSRB3 0.000429 NIBAN3 0.000416 NUSAP1 0.000415 FRY 0.000393 AFF3 0.000378 MEIS1 0.000369 Name: change_scores_lineage, dtype: float64
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del adata.raw
del adata.raw
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import matplotlib.pyplot as plt
from matplotlib import patheffects
#fig, ax = plt.subplots(figsize=(3,3))
ov.pl.embedding(adata,
basis='X_umap',
color=['EBF1','ERG','PAX5'],
frameon='small',
size=15,
cmap='RdBu_r',)
import matplotlib.pyplot as plt
from matplotlib import patheffects
#fig, ax = plt.subplots(figsize=(3,3))
ov.pl.embedding(adata,
basis='X_umap',
color=['EBF1','ERG','PAX5'],
frameon='small',
size=15,
cmap='RdBu_r',)
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import matplotlib.pyplot as plt
from matplotlib import patheffects
fig, ax = plt.subplots(figsize=(3,3))
ad=adata
visual_cluster=['20','17']
ad.obs['visual']=ad.obs['leiden'].copy()
ad.obs.loc[~ad.obs['leiden'].isin(visual_cluster),'visual']=None
ov.utils.embedding(ad,
basis='X_umap',frameon='small',
color=['visual'],
legend_loc=None,
#palette=ov.utils.blue_color+ov.utils.orange_color+ov.utils.red_color+ov.utils.green_color,
show=False,
ax=ax)
ov.pl.embedding_adjust(
ad,
basis="X_umap",
groupby='celltype',
exclude=tuple(set(ad.obs['celltype'].cat.categories)-set(['CLP','proB', 'preproB', 'preB', 'NaiveB'])),
ax=ax,
adjust_kwargs=dict(arrowprops=dict(arrowstyle='-', color='black')),
text_kwargs=dict(fontsize=12 ,weight='bold',
path_effects=[patheffects.withStroke(linewidth=2, foreground='w')] ),
)
ov.pl.contour(ax=ax,adata=ad,
basis="X_umap",
groupby='leiden',clusters=visual_cluster,
contour_threshold=0.02,colors=ov.pl.red_color[2],linestyles='dashed')
plt.title('B Cells\nscRNA-seq', fontsize=14)
#plt.savefig(f'figures/hsc/umap-lineage-B-33.png',dpi=300,bbox_inches='tight')
#plt.savefig(f'pdf/hsc/umap-lineage-B-33.pdf',dpi=300,bbox_inches='tight')
import matplotlib.pyplot as plt
from matplotlib import patheffects
fig, ax = plt.subplots(figsize=(3,3))
ad=adata
visual_cluster=['20','17']
ad.obs['visual']=ad.obs['leiden'].copy()
ad.obs.loc[~ad.obs['leiden'].isin(visual_cluster),'visual']=None
ov.utils.embedding(ad,
basis='X_umap',frameon='small',
color=['visual'],
legend_loc=None,
#palette=ov.utils.blue_color+ov.utils.orange_color+ov.utils.red_color+ov.utils.green_color,
show=False,
ax=ax)
ov.pl.embedding_adjust(
ad,
basis="X_umap",
groupby='celltype',
exclude=tuple(set(ad.obs['celltype'].cat.categories)-set(['CLP','proB', 'preproB', 'preB', 'NaiveB'])),
ax=ax,
adjust_kwargs=dict(arrowprops=dict(arrowstyle='-', color='black')),
text_kwargs=dict(fontsize=12 ,weight='bold',
path_effects=[patheffects.withStroke(linewidth=2, foreground='w')] ),
)
ov.pl.contour(ax=ax,adata=ad,
basis="X_umap",
groupby='leiden',clusters=visual_cluster,
contour_threshold=0.02,colors=ov.pl.red_color[2],linestyles='dashed')
plt.title('B Cells\nscRNA-seq', fontsize=14)
#plt.savefig(f'figures/hsc/umap-lineage-B-33.png',dpi=300,bbox_inches='tight')
#plt.savefig(f'pdf/hsc/umap-lineage-B-33.pdf',dpi=300,bbox_inches='tight')
/mnt/home/zehuazeng/software/rsc/lib/python3.10/site-packages/omicverse/utils/_scatterplot.py:430: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored cax = scatter(
Out[82]:
Text(0.5, 1.0, 'B Cells\nscRNA-seq')
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import matplotlib.pyplot as plt
g=ov.utils.plot_heatmap(ad[ad.obs['leiden'].isin(visual_cluster)],
var_names=scores.loc[cfg_obj.filter_coef.index].sort_values(ascending=False)[:10].index.tolist(),
sortby='palantir_pseudotime',col_color='leiden',yticklabels=True,
n_convolve=100,figsize=(1,6),show=False)
g.fig.set_size_inches(1, 4)
g.ax_heatmap.set_yticklabels(g.ax_heatmap.get_yticklabels(),fontsize=10)
#plt.savefig(f'figures/hsc/heatmap-lineage-B-leiden-24.png',dpi=300,bbox_inches='tight')
import matplotlib.pyplot as plt
g=ov.utils.plot_heatmap(ad[ad.obs['leiden'].isin(visual_cluster)],
var_names=scores.loc[cfg_obj.filter_coef.index].sort_values(ascending=False)[:10].index.tolist(),
sortby='palantir_pseudotime',col_color='leiden',yticklabels=True,
n_convolve=100,figsize=(1,6),show=False)
g.fig.set_size_inches(1, 4)
g.ax_heatmap.set_yticklabels(g.ax_heatmap.get_yticklabels(),fontsize=10)
#plt.savefig(f'figures/hsc/heatmap-lineage-B-leiden-24.png',dpi=300,bbox_inches='tight')
/mnt/home/zehuazeng/software/rsc/lib/python3.10/site-packages/omicverse/utils/_heatmap.py:335: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /mnt/home/zehuazeng/software/rsc/lib/python3.10/site-packages/seaborn/matrix.py:1107: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. self._figure.tight_layout(**tight_params)
Out[84]:
[Text(1, 0.5, 'NKAIN2'), Text(1, 1.5, 'RNF220'), Text(1, 2.5, 'CASC15'), Text(1, 3.5, 'LRMDA'), Text(1, 4.5, 'ATP8B4'), Text(1, 5.5, 'ERG'), Text(1, 6.5, 'PLXDC2'), Text(1, 7.5, 'SLC8A1'), Text(1, 8.5, 'EBF1'), Text(1, 9.5, 'PAX5')]
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import matplotlib.pyplot as plt
g=ov.utils.plot_heatmap(ad[ad.obs['celltype'].isin(['CLP','proB','preproB','preB','NaiveB'])],
var_names=scores.loc[cfg_obj.filter_coef.index].sort_values(ascending=False)[:10].index.tolist(),
sortby='palantir_pseudotime',col_color='celltype',yticklabels=True,
n_convolve=100,figsize=(1,6),show=False)
g.fig.set_size_inches(1, 4)
g.ax_heatmap.set_yticklabels(g.ax_heatmap.get_yticklabels(),fontsize=10)
#plt.savefig(f'figures/hsc/heatmap-lineage-B-ct-24.png',dpi=300,bbox_inches='tight')
import matplotlib.pyplot as plt
g=ov.utils.plot_heatmap(ad[ad.obs['celltype'].isin(['CLP','proB','preproB','preB','NaiveB'])],
var_names=scores.loc[cfg_obj.filter_coef.index].sort_values(ascending=False)[:10].index.tolist(),
sortby='palantir_pseudotime',col_color='celltype',yticklabels=True,
n_convolve=100,figsize=(1,6),show=False)
g.fig.set_size_inches(1, 4)
g.ax_heatmap.set_yticklabels(g.ax_heatmap.get_yticklabels(),fontsize=10)
#plt.savefig(f'figures/hsc/heatmap-lineage-B-ct-24.png',dpi=300,bbox_inches='tight')
/mnt/home/zehuazeng/software/rsc/lib/python3.10/site-packages/omicverse/utils/_heatmap.py:335: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /mnt/home/zehuazeng/software/rsc/lib/python3.10/site-packages/seaborn/matrix.py:1107: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. self._figure.tight_layout(**tight_params)
Out[85]:
[Text(1, 0.5, 'NKAIN2'), Text(1, 1.5, 'ATP8B4'), Text(1, 2.5, 'LRMDA'), Text(1, 3.5, 'RNF220'), Text(1, 4.5, 'EBF1'), Text(1, 5.5, 'ERG'), Text(1, 6.5, 'CASC15'), Text(1, 7.5, 'SLC8A1'), Text(1, 8.5, 'PLXDC2'), Text(1, 9.5, 'PAX5')]
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# 创建横向柱状图
import matplotlib.cm as cm
fig, ax = plt.subplots(figsize=(0.5, 3))
od_genes=scores.loc[cfg_obj.filter_coef.index].sort_values(ascending=False)[:10]
norm = plt.Normalize(min(od_genes.values), max(od_genes.values))
colors = cm.Blues(norm(od_genes.values))
plt.barh(od_genes.index, od_genes.values, color=colors)
ax.spines['left'].set_position(('outward', 10))
ax.spines['bottom'].set_position(('outward', 10))
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(True)
ax.spines['left'].set_visible(True)
ax.grid(False)
# 设置标签和标题
ax.set_xlabel('')
ax.set_ylabel('$R^2$', fontsize=13)
ax.set_title('', fontsize=13)
ax.set_xlim(0,0.001)
#ax.set_xticks(x + width)
ax.set_xticklabels(ax.get_xticklabels(), fontsize=11,rotation=0)
ax.set_yticklabels(ax.get_yticklabels(), fontsize=11)
plt.xlabel('Scores',fontsize=12)
plt.ylabel('Genes',fontsize=12)
plt.title('Fate Rank',fontsize=12)
plt.gca().invert_yaxis() # 反转y轴使得最高分数在顶部
#plt.savefig(f'figures/hsc/fr-lineage-B-33.png',dpi=300,bbox_inches='tight')
#plt.savefig(f'pdf/hsc/fr-lineage-B-33.pdf',dpi=300,bbox_inches='tight')
# 创建横向柱状图
import matplotlib.cm as cm
fig, ax = plt.subplots(figsize=(0.5, 3))
od_genes=scores.loc[cfg_obj.filter_coef.index].sort_values(ascending=False)[:10]
norm = plt.Normalize(min(od_genes.values), max(od_genes.values))
colors = cm.Blues(norm(od_genes.values))
plt.barh(od_genes.index, od_genes.values, color=colors)
ax.spines['left'].set_position(('outward', 10))
ax.spines['bottom'].set_position(('outward', 10))
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(True)
ax.spines['left'].set_visible(True)
ax.grid(False)
# 设置标签和标题
ax.set_xlabel('')
ax.set_ylabel('$R^2$', fontsize=13)
ax.set_title('', fontsize=13)
ax.set_xlim(0,0.001)
#ax.set_xticks(x + width)
ax.set_xticklabels(ax.get_xticklabels(), fontsize=11,rotation=0)
ax.set_yticklabels(ax.get_yticklabels(), fontsize=11)
plt.xlabel('Scores',fontsize=12)
plt.ylabel('Genes',fontsize=12)
plt.title('Fate Rank',fontsize=12)
plt.gca().invert_yaxis() # 反转y轴使得最高分数在顶部
#plt.savefig(f'figures/hsc/fr-lineage-B-33.png',dpi=300,bbox_inches='tight')
#plt.savefig(f'pdf/hsc/fr-lineage-B-33.pdf',dpi=300,bbox_inches='tight')
/tmp/ipykernel_2892064/1191334045.py:28: UserWarning: FixedFormatter should only be used together with FixedLocator ax.set_xticklabels(ax.get_xticklabels(), fontsize=11,rotation=0) /tmp/ipykernel_2892064/1191334045.py:29: UserWarning: FixedFormatter should only be used together with FixedLocator ax.set_yticklabels(ax.get_yticklabels(), fontsize=11)
In [88]:
Copied!
import seaborn as sns
fig, ax = plt.subplots(figsize=(3,3))
gene='ERG'
visual_cluster=['20','17']
x=ad[ad.obs['leiden'].isin(visual_cluster),gene].to_df().values.reshape(-1)
y=ad.obs.loc[ad.obs['leiden'].isin(visual_cluster),'palantir_pseudotime'].values.reshape(-1)
z=ad.obs.loc[ad.obs['leiden'].isin(visual_cluster),'mellon_log_density_lowd'].values.reshape(-1)
sns.kdeplot(
x=x, y=y,
fill=True,
cmap='Blues',
#clip=(-5, 5), cut=10,
thresh=0.1, levels=15,
ax=ax,#cbar=True,
)
scatter=ax.scatter(x,y,
c=z, s=x*10,
cmap='RdBu_r',
)
ax.spines['left'].set_position(('outward', 10))
ax.spines['bottom'].set_position(('outward', 10))
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(True)
ax.spines['left'].set_visible(True)
ax.grid(False)
plt.xlabel('Expression',fontsize=13)
plt.ylabel('Pseudotime',fontsize=13)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.title(gene,fontsize=13)
cbar = plt.colorbar(scatter, ax=ax,shrink=0.5)
cbar.set_label('Density', fontsize=13)
cbar.ax.tick_params(labelsize=12)
#plt.savefig(f'figures/hsc/density-lineage-B-{gene}.png',dpi=300,bbox_inches='tight')
#plt.savefig(f'pdf/hsc/density-lineage-B-{gene}.pdf',dpi=300,bbox_inches='tight')
#cbar.set_ticklabels(cbar.get_ticklabels(),fontsize=12)
import seaborn as sns
fig, ax = plt.subplots(figsize=(3,3))
gene='ERG'
visual_cluster=['20','17']
x=ad[ad.obs['leiden'].isin(visual_cluster),gene].to_df().values.reshape(-1)
y=ad.obs.loc[ad.obs['leiden'].isin(visual_cluster),'palantir_pseudotime'].values.reshape(-1)
z=ad.obs.loc[ad.obs['leiden'].isin(visual_cluster),'mellon_log_density_lowd'].values.reshape(-1)
sns.kdeplot(
x=x, y=y,
fill=True,
cmap='Blues',
#clip=(-5, 5), cut=10,
thresh=0.1, levels=15,
ax=ax,#cbar=True,
)
scatter=ax.scatter(x,y,
c=z, s=x*10,
cmap='RdBu_r',
)
ax.spines['left'].set_position(('outward', 10))
ax.spines['bottom'].set_position(('outward', 10))
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(True)
ax.spines['left'].set_visible(True)
ax.grid(False)
plt.xlabel('Expression',fontsize=13)
plt.ylabel('Pseudotime',fontsize=13)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.title(gene,fontsize=13)
cbar = plt.colorbar(scatter, ax=ax,shrink=0.5)
cbar.set_label('Density', fontsize=13)
cbar.ax.tick_params(labelsize=12)
#plt.savefig(f'figures/hsc/density-lineage-B-{gene}.png',dpi=300,bbox_inches='tight')
#plt.savefig(f'pdf/hsc/density-lineage-B-{gene}.pdf',dpi=300,bbox_inches='tight')
#cbar.set_ticklabels(cbar.get_ticklabels(),fontsize=12)
In [89]:
Copied!
fig, ax = plt.subplots(figsize=(3,3))
#visual_cluster=['15','12']
#ad.obs['visual']=ad.obs['leiden'].copy()
#ad.obs.loc[~ad.obs['leiden'].isin(visual_cluster),'visual']=None
ov.utils.embedding(ad,
basis='X_umap',frameon='small',
color=[gene],
legend_loc=None,
#palette=ov.utils.blue_color+ov.utils.orange_color+ov.utils.red_color+ov.utils.green_color,
show=False,
ax=ax)
#plt.savefig(f'figures/hsc/umap-lineage-B-{gene}.png',dpi=300,bbox_inches='tight')
#plt.savefig(f'pdf/hsc/umap-lineage-B-{gene}.pdf',dpi=300,bbox_inches='tight')
fig, ax = plt.subplots(figsize=(3,3))
#visual_cluster=['15','12']
#ad.obs['visual']=ad.obs['leiden'].copy()
#ad.obs.loc[~ad.obs['leiden'].isin(visual_cluster),'visual']=None
ov.utils.embedding(ad,
basis='X_umap',frameon='small',
color=[gene],
legend_loc=None,
#palette=ov.utils.blue_color+ov.utils.orange_color+ov.utils.red_color+ov.utils.green_color,
show=False,
ax=ax)
#plt.savefig(f'figures/hsc/umap-lineage-B-{gene}.png',dpi=300,bbox_inches='tight')
#plt.savefig(f'pdf/hsc/umap-lineage-B-{gene}.pdf',dpi=300,bbox_inches='tight')
Out[89]:
<AxesSubplot: title={'center': 'ERG'}, xlabel='X_umap1', ylabel='X_umap2'>
