Tutorial:Single-cell RNA-seq: Annotation: Celltype auto annotation with SCSA-2

Question

[Julia Ma]

Part-1 here: Single-cell RNA-seq: Annotation: Celltype auto annotation with SCSA

Part-3 here: Single-cell RNA-seq: Annotation: Celltype auto annotation with SCSA-3

Full article lifted from: https://omicverse.readthedocs.io/en/latest/Tutorials-single/t_cellanno/

---

We can also use panglaodb as target to annotate the celltype

scsa=ov.single.pySCSA(adata=adata,
                          foldchange=1.5,
                          pvalue=0.01,
                          celltype='normal',
                          target='panglaodb',
                          tissue='All',
                          model_path='temp/pySCSA_2023_v2_plus.db'

)

res=scsa.cell_anno(clustertype='leiden',
               cluster='all',rank_rep=True)

ranking genes
    finished (0:00:01)
...Auto annotate cell
Version V2.1 [2023/06/27]
DB load: GO_items:47347,Human_GO:3,Mouse_GO:3,
CellMarkers:82887,CancerSEA:1574,PanglaoDB:24223
Ensembl_HGNC:61541,Ensembl_Mouse:55414
<omicverse.single._SCSA.Annotator object at 0x2c89d0250>
Version V2.1 [2023/06/27]
DB load: GO_items:47347,Human_GO:3,Mouse_GO:3,
CellMarkers:82887,CancerSEA:1574,PanglaoDB:24223
Ensembl_HGNC:61541,Ensembl_Mouse:55414
load markers: 70276
Cluster 0 Gene number: 75
Other Gene number: 701
Cluster 1 Gene number: 144
Other Gene number: 672
Cluster 10 Gene number: 123
Other Gene number: 675
Cluster 2 Gene number: 515
Other Gene number: 667
Cluster 3 Gene number: 129
Other Gene number: 664
Cluster 4 Gene number: 82
Other Gene number: 701
Cluster 5 Gene number: 931
Other Gene number: 612
Cluster 6 Gene number: 243
Other Gene number: 663
Cluster 7 Gene number: 5
Other Gene number: 712
Cluster 8 Gene number: 67
Other Gene number: 712
Cluster 9 Gene number: 587
Other Gene number: 670
#Cluster Type Celltype Score Times
['0', '?', 'T Cells|T Memory Cells', '3.7178236936447595|3.363511285637478', 1.1053400384057666]
['1', '?', 'T Cells|T Memory Cells', '3.531461316580769|3.117745502299072', 1.1326971088488835]
['10', 'Good', 'Platelets', 7.432982147841601, 2.4362620739591723]
['2', '?', 'Monocytes|Alveolar Macrophages', '3.6455690759322272|2.922079559631339', 1.2475940512694892]
['3', '?', 'B Cells Naive|B Cells Memory', '4.328429443730031|3.9545790694449328', 1.0945360726691886]
['4', '?', 'NK Cells|T Cells', '2.96702571977588|2.680719557955109', 1.1068019819421802]
['5', '?', 'Monocytes|Macrophages', '3.774802905873644|2.8442136919017464', 1.327186813220659]
['6', '?', 'NK Cells|Decidual Cells', '4.137392815321161|2.880539867188994', 1.4363254827501069]
['7', '?', 'Decidual Cells|NK Cells', '1.6543443520623498|1.6543443520623498', 1.0]
['8', '?', 'Monocytes|Alveolar Macrophages', '2.7222000722461677|2.1616279260313114', 1.2593286936499064]
['9', '?', 'Dendritic Cells|Langerhans Cells', '4.146171305699119|3.728571260268892', 1.1120000172398774]

We can query only the better annotated results

scsa.cell_anno_print()

Cluster:0   Cell_type:T Cells|T Memory Cells    Z-score:3.718|3.364
Cluster:1   Cell_type:T Cells|T Memory Cells    Z-score:3.531|3.118
Cluster:2   Cell_type:Monocytes|Alveolar Macrophages    Z-score:3.646|2.922
Cluster:3   Cell_type:B Cells Naive|B Cells Memory  Z-score:4.328|3.955
Cluster:4   Cell_type:NK Cells|T Cells  Z-score:2.967|2.681
Cluster:5   Cell_type:Monocytes|Macrophages Z-score:3.775|2.844
Cluster:6   Cell_type:NK Cells|Decidual Cells   Z-score:4.137|2.881
Cluster:7   Cell_type:Decidual Cells|NK Cells   Z-score:1.654|1.654
Cluster:8   Cell_type:Monocytes|Alveolar Macrophages    Z-score:2.722|2.162
Cluster:9   Cell_type:Dendritic Cells|Langerhans Cells  Z-score:4.146|3.729
Nice:Cluster:10 Cell_type:Platelets Z-score:7.433

scsa.cell_auto_anno(adata,key='scsa_celltype_panglaodb')

...cell type added to scsa_celltype_panglaodb on obs of anndata

Here, we introduce the dimensionality reduction visualisation function ov.utils.embedding, which is similar to scanpy.pl.embedding, except that when we set frameon='small', we scale the axes to the bottom-left corner and scale the colourbar to the bottom-right corner.

• adata: the anndata object
• basis: the visualized embedding stored in adata.obsm
• color: the visualized obs/var
• legend_loc: the location of legend, if you set None, it will be visualized in right.
• frameon: it can be set small, False or None
• legend_fontoutline: the outline in the text of legend.
• palette: Different categories of colours, we have a number of different colours preset in omicverse, including ov.utils.palette(), ov.utils.red_color, ov.utils.blue_color, ov.utils.green_color, ov. utils.orange_color. The preset colours can help you achieve a more beautiful visualisation.

ov.utils.embedding(adata,
                   basis='X_mde',
                   color=['leiden','scsa_celltype_cellmarker','scsa_celltype_panglaodb'], 
                   legend_loc='on data', 
                   frameon='small',
                   legend_fontoutline=2,
                   palette=ov.utils.palette()[14:],
                  )

If you want to draw stacked histograms of cell type proportions, you first need to colour the groups you intend to draw using ov.utils.embedding. Then use ov.utils.plot_cellproportion to specify the groups you want to plot, and you can see a plot of cell proportions in the different groups

#Randomly designate the first 1000 cells as group B and the rest as group A
adata.obs['group']='A'
adata.obs.loc[adata.obs.index[:1000],'group']='B'
#Colored
ov.utils.embedding(adata,
                   basis='X_mde',
                   color=['group'], 
                   frameon='small',legend_fontoutline=2,
                   palette=ov.utils.red_color,
                  )

ov.utils.plot_cellproportion(adata=adata,celltype_clusters='scsa_celltype_cellmarker',
                    visual_clusters='group',
                    visual_name='group',figsize=(2,4))



(<Figure size 160x320 with 1 Axes>,
 <AxesSubplot: xlabel='group', ylabel='Cells per Stage'>)

Of course, we also provide another downscaled visualisation of the graph using ov.utils.plot_embedding_celltype

ov.utils.plot_embedding_celltype(adata,figsize=None,basis='X_mde',
                            celltype_key='scsa_celltype_cellmarker',
                            title='            Cell type',
                            celltype_range=(2,6),
                            embedding_range=(4,10),)

(<Figure size 480x320 with 2 Axes>,
 [<AxesSubplot: xlabel='X_mde1', ylabel='X_mde2'>, <AxesSubplot: >])

We calculated the ratio of observed to expected cell numbers (Ro/e) for each cluster in different tissues to quantify the tissue preference of each cluster (Guo et al., 2018; Zhang et al., 2018). The expected cell num- bers for each combination of cell clusters and tissues were obtained from the chi-square test. One cluster was identified as being enriched in a specific tissue if Ro/e>1.

The Ro/e function was wrote by Haihao Zhang.

roe=ov.utils.roe(adata,sample_key='group',cell_type_key='scsa_celltype_cellmarker')

chi2: 1.426243142767526, dof: 5, pvalue: 0.9214211744335161
P-value is greater than 0.05, there is no statistical significance




import seaborn as sns
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(2,4))

transformed_roe = roe.copy()
transformed_roe = transformed_roe.applymap(
    lambda x: '+++' if x >= 2 else ('++' if x >= 1.5 else ('+' if x >= 1 else '+/-')))

sns.heatmap(roe, annot=transformed_roe, cmap='RdBu_r', fmt='', 
            cbar=True, ax=ax,vmin=0.5,vmax=1.5,cbar_kws={'shrink':0.5})
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)

plt.xlabel('Group',fontsize=13)
plt.ylabel('Cell type',fontsize=13)
plt.title('Ro/e',fontsize=13)

Text(0.5, 1.0, 'Ro/e')