08单细胞分析2025-拟时间scVelo分析

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已于 2025-03-21 00:26:52 修改

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分类专栏：单细胞分析文章标签： python linux scvelo paga velocity 拟时间

于 2025-03-21 00:24:07 首次发布

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测成熟和未成熟的RNA动力学 velocyto发育的起点和终点

1.建立新的环境scvelo分析的环境来分析loom文件

conda info -e

conda create -n scvelo310 python=3.10

conda activate scvelo310

conda install -c conda-forge scvelo

conda install ipykernel

python -m ipykernel install --user --name scvelo310 --display-name scvelo310

conda install -c anaconda notebook

conda install -c conda-forge scanpy python-igraph leidenalg

jupyter notebook --no-browser

2.分析代码：

import scvelo as scv
import scanpy as sc

scv.settings.verbosity = 3  
# show errors(0), warnings(1), info(2), hints(3)
scv.settings.presenter_view = True  
# set max width size for presenter view
scv.set_figure_params('scvelo')  
# for beautified visualization

adata=sc.read("./bcoaneiD201.loom",cache=False)
adata

scv.pl.proportions(adata)

scv.pp.filter_and_normalize(adata, min_shared_counts=30, n_top_genes=2000)

scv.pp.moments(adata, n_pcs=30, n_neighbors=30)

scv.tl.velocity(adata)

scv.tl.velocity_graph(adata)

scv.pp.neighbors(adata, n_neighbors=10, n_pcs=40)

sc.tl.leiden(adata)

sc.tl.umap(adata)

scv.pl.umap(adata, color='leiden')

scv.pl.velocity_embedding_stream(adata, basis='umap', color=['leiden'])

scv.pl.velocity_embedding_stream(adata, basis='umap', color=['initial_size_spliced'])

scv.pl.velocity_embedding(adata, arrow_length=3, arrow_size=2, dpi=120, color=['leiden'])

scv.pl.velocity(adata, ['CCL3'], dpi=120, color=['leiden'])

scv.pl.scatter(adata, 'CCL3', color=['leiden', 'velocity'])

scv.tl.rank_velocity_genes(adata, groupby='leiden', min_corr=.3)

#df = scv.DataFrame(adata.uns['rank_velocity_genes']['names'])
import pandas as pd

df = pd.DataFrame(adata.uns['rank_velocity_genes']['names'])
df.head()

scv.tl.velocity_confidence(adata)

keys = 'velocity_length', 'velocity_confidence'
scv.pl.scatter(adata, c=keys, cmap='coolwarm', perc=[5, 95])

df = adata.obs.groupby('leiden')[keys].mean().T
df.style.background_gradient(cmap='coolwarm', axis=1)

scv.pl.velocity_graph(adata, threshold=.1,color=['leiden'])

scv.tl.velocity_pseudotime(adata)
scv.pl.scatter(adata, color='velocity_pseudotime', cmap='gnuplot')

adata.uns['neighbors']['distances'] = adata.obsp['distances']
adata.uns['neighbors']['connectivities'] = adata.obsp['connectivities']

scv.tl.paga(adata, groups='leiden')

scv.pl.paga(adata, basis='umap', size=50, alpha=.1,
            min_edge_width=2, node_size_scale=1.5)

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