今天就分享一个python包，进行单细胞数据umap图绘制。非常的好看实用。
原文链接：https://starlitnightly.github.io/omicverse/Tutorialsplotting/t_visualize_single/
话不多说，直接进入数据分析主题。

1、数据下载

原始数据来源：theislab/scvelo_notebooks (github.com)。
准备数据：DentateGyrus\10X43_1.h5ad。

2、初步代码运行

######数据来源，运行环境在python3.9
import omicverse as ov
import scanpy as sc
import matplotlib.pyplot as plt
ov.ov_plot_set()
adata=ov.read(r'data\DentateGyrus\10X43_1.h5ad')
adata
######初步绘图展示
optim_palette=ov.pl.optim_palette(adata,basis='X_umap',colors='clusters')
import matplotlib.pyplot as plt
fig,ax=plt.subplots(figsize = (4,4))
ov.pl.embedding(adata,
                basis='X_umap',
               color='clusters',
               frameon='small',
               show=False,
               palette=optim_palette,
               ax=ax,)
plt.title('Cell Type of DentateGyrus',fontsize=15)

3、将图中细胞名称按照自定义进行排序展示

new_order=[ 'OL', 'OPC','Cck-Tox','nIPC', 'GABA','Radial Glia-like', 'Astrocytes', 'Endothelial','Cajal Retzius',
       'Granule immature', 'Granule mature', 'Microglia', 'Mossy', 'Neuroblast',
        ]

adata.obs['clusters']=adata.obs['clusters'].cat.reorder_categories(new_order)

fig, ax = plt.subplots(figsize=(4,8))
ov.pl.embedding(adata,
    basis="X_umap",
    color=['clusters'],title='',
    show=False,
    frameon='small',
    size=15,
    ax=ax
)

4、指定细胞展示颜色

color_dict={
    'OL':ov.pl.blue_color[4],
    'OPC':ov.pl.blue_color[5],
    'Cck-Tox':ov.pl.purple_color[0],
    'nIPC':ov.pl.purple_color[1],
    'GABA':ov.pl.green_color[0],
    'Radial Glia-like':ov.pl.green_color[1],
    'Astrocytes':ov.pl.green_color[2],
    'Endothelial':ov.pl.red_color[0],
    'Cajal Retzius':ov.pl.red_color[1],
    'Granule immature':ov.pl.red_color[2],
    'Granule mature':ov.pl.red_color[3],
    'Microglia':ov.pl.red_color[4],
    'Mossy':ov.pl.red_color[5],
    'Neuroblast':ov.pl.red_color[6],
}
fig, ax = plt.subplots(figsize=(4,4))
ov.pl.embedding(adata,
    basis="X_umap",
    color=['clusters'],title='',
    show=False,
    frameon='small',
    size=10,
    palette=color_dict,
    ax=ax
)

5、细胞标签可视化

from matplotlib import patheffects
fig, ax = plt.subplots(figsize=(4,4))
ov.pl.embedding(adata,
    basis="X_umap",
    color=['clusters'],title='',
                   show=False, legend_loc=None, add_outline=False, 
                   frameon='small',legend_fontoutline=2,ax=ax
                 )

ov.pl.embedding_adjust(
    adata,
    basis="X_umap",
    groupby='major_celltype',
    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')] ),
)

6、同类别细胞轮廓展示

from matplotlib import patheffects
fig, ax = plt.subplots(figsize=(4,4))
ov.pl.embedding(adata,
    basis="X_umap",
    color=['clusters'],title='',
                   show=False, legend_loc=None, add_outline=False,
                   frameon='small',legend_fontoutline=2,ax=ax
                 )

ov.pl.embedding_adjust(
    adata,
    basis="X_umap",
    groupby='clusters',
    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=adata,
        basis="X_umap",
        groupby='clusters',clusters=['OL','OPC'],
        contour_threshold=0.02,colors=ov.pl.red_color[2],linestyles='dashed')

ov.pl.contour(ax=ax,adata=adata,
        basis="X_umap",
        groupby='clusters',clusters=['Granule immature','Granule mature'],
        contour_threshold=0.01,colors=ov.pl.blue_color[6],linestyles='dashed')

ov.pl.contour(ax=ax,adata=adata,
        basis="X_umap",
        groupby='clusters',clusters=['Radial Glia-like', 'Astrocytes'],
        contour_threshold=0.002,colors=ov.pl.orange_color[2],linestyles='dashed')

fig.savefig('umap-ct_major.png',dpi=300,bbox_inches='tight')
fig.savefig('umap-ct_major.pdf',dpi=300,bbox_inches='tight')

7、细胞比例图展示

ov.pl.embedding_celltype(adata,
                            figsize=(7,4),basis='X_umap',
                            celltype_key='clusters',
                            title='Cell type',
                            celltype_range=(1,10),
                            embedding_range=(4,10),)

8、细胞分布强度

ov.pl.embedding_density(adata,
                 basis='X_umap',
                 groupby='clusters',
                 target_clusters='Granule mature',
                 frameon='small',
                show=False,cmap='RdBu_r',alpha=0.8)

9、带重叠点的条形图（条形点）图

ov.single.geneset_aucell(adata,
                            geneset_name='Sox',
                            geneset=['Sox17', 'Sox4', 'Sox7', 'Sox18', 'Sox5'])

ov.pl.embedding(adata,
                basis='X_umap',
                color=['Sox4'],
                frameon='small',
                show=False,)

此次绘图参考优秀的公众号以及官方网站进行，如有疑问，请联系管理员：kriswcyYQ。